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Dapr 1.16.19\r\n\r\nThis update contains the following bug fixes:\r\n- [Azure component authentication halting at the SPIFFE credential instead of falling back](#azure-component-authentication-halting-at-the-spiffe-credential-instead-of-falling-back)\r\n- [SPIFFE SVID component operation propagation](#spiffe-svid-component-operation-propagation)\r\n\r\n## Azure component authentication halting at the SPIFFE credential instead of falling back\r\n\r\n### Problem\r\n\r\nAzure (Microsoft Entra ID) components authenticate by trying a chain of credentials in order until one succeeds.\r\nWhen `azureClientId` and `azureTenantId` were set, the chain included the SPIFFE workload identity credential, and if no SPIFFE JWT SVID source was available the chain stopped at that step:\r\n\r\n```\r\nChainedTokenCredential: failed to acquire a token.\r\nAttempted credentials:\r\n\tClientAssertionCredential: failed to get JWT SVID source from context\r\n```\r\n\r\nCredentials later in the chain, such as managed identity or the Azure CLI, were never attempted, so the component failed to authenticate even though a working credential was available.\r\n\r\n### Impact\r\n\r\nYou were affected on Dapr 1.16.0 or later (where the SPIFFE credential joined the default chain) in either of these configurations:\r\n\r\n- An Azure component with `azureClientId` and `azureTenantId` set but no client secret or certificate, relying on a later credential in the default chain (for example managed identity or the Azure CLI).\r\n- An explicit `azureAuthMethods` list placing `spiffeworkloadidentity` before another method (for example `spiffeworkloadidentity,managedidentity`), expecting fallback when SPIFFE is not configured.\r\n\r\n### Root Cause\r\n\r\n`ChainedTokenCredential` only continues past a credential that reports a `credentialUnavailableError`; any other error is treated as fatal and ends the chain.\r\nThe SPIFFE credential returned a plain error when the context carried no JWT SVID source, so a missing prerequisite was treated as a fatal authentication failure rather than a signal to try the next credential.\r\n\r\n### Solution\r\n\r\nThe SPIFFE credential now reports itself as unavailable when no JWT SVID source is present, before any token request is made, which is exactly what `ChainedTokenCredential` requires to continue to the next credential in the chain.\r\nBoth the default chain and explicitly ordered `azureAuthMethods` lists now fall through as expected, and behavior when a SPIFFE source is configured is unchanged.\r\n\r\n## SPIFFE SVID component operation propagation\r\n\r\n### Problem\r\n\r\nThe SPIFFE SVID source was not propagated to component operation calls. As a result, any component that wanted to use the SPIFFE ID for authentication had to capture the source in its `init` method and manage it explicitly. SDKs such as the Azure SDK, which can transparently use the SPIFFE SVID source when it is present on the context, were unable to do so.\r\n\r\n### Impact\r\n\r\nAzure components could not leverage the SPIFFE ID for authentication without explicitly handling the source, and this also blocked future support for SPIFFE-based authentication in other components.\r\n\r\n### Root Cause\r\n\r\nThe SPIFFE SVID source was attached only to the component's `init` method context, not to the context passed into operation calls.\r\n\r\n### Solution\r\n\r\nThe SPIFFE SVID source is now attached to the context passed into operation calls, allowing components to use the SPIFFE ID for authentication on a per-operation 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Dapr 1.18.3\r\n\r\nThis update contains the following bug fixes:\r\n- [Actor state store components cannot be hot reloaded](#actor-state-store-components-cannot-be-hot-reloaded)\r\n- [All sidecars in a namespace disconnected from Placement when a single sidecar disconnected mid-dissemination](#all-sidecars-in-a-namespace-disconnected-from-placement-when-a-single-sidecar-disconnected-mid-dissemination)\r\n- [Scheduler embedded etcd metrics missing from the metrics endpoint](#scheduler-embedded-etcd-metrics-missing-from-the-metrics-endpoint)\r\n- [Scheduler hanging on shutdown and failing to redeliver jobs after client disconnects](#scheduler-hanging-on-shutdown-and-failing-to-redeliver-jobs-after-client-disconnects)\r\n- [Scheduler crash when scheduling a job with a malformed timezone-prefixed schedule](#scheduler-crash-when-scheduling-a-job-with-a-malformed-timezone-prefixed-schedule)\r\n- [Timezone prefix silently ignored on `@every` job schedules](#timezone-prefix-silently-ignored-on-every-job-schedules)\r\n- [Workflow GetInstance intermittently failing while the workflow is running](#workflow-getinstance-intermittently-failing-while-the-workflow-is-running)\r\n- [Input bindings not activated when the app is slow to answer the subscription discovery probe](#input-bindings-not-activated-when-the-app-is-slow-to-answer-the-subscription-discovery-probe)\r\n- [MCPServer left unusable when its endpoint was briefly unavailable at startup](#mcpserver-left-unusable-when-its-endpoint-was-briefly-unavailable-at-startup)\r\n- [Crash when delivering a pub/sub message with a non-string trace field over HTTP](#crash-when-delivering-a-pubsub-message-with-a-non-string-trace-field-over-http)\r\n- [Workflow `continue_as_new` iterations sharing a single unbounded trace](#workflow-continue_as_new-iterations-sharing-a-single-unbounded-trace)\r\n- [Stalled workflows permanently stuck after the last workflow worker disconnects](#stalled-workflows-permanently-stuck-after-the-last-workflow-worker-disconnects)\r\n- [Fixes orphaned workflow activity-result reminders retrying forever](#fixes-orphaned-workflow-activity-result-reminders-retrying-forever)\r\n- [Pub/sub delivery to a gRPC app failing with \"use of closed network connection\"](#pubsub-delivery-to-a-grpc-app-failing-with-use-of-closed-network-connection)\r\n- [Workflow terminate silently dropped when delivered in the same batch as other events](#workflow-terminate-silently-dropped-when-delivered-in-the-same-batch-as-other-events)\r\n- [Azure component authentication halting at the SPIFFE credential instead of falling back](#azure-component-authentication-halting-at-the-spiffe-credential-instead-of-falling-back)\r\n\r\n## Actor state store components cannot be hot reloaded\r\n\r\n### Problem\r\n\r\nWith hot reloading enabled, daprd refused to apply any change to a state store component that is, or would become, the actor state store.\r\nCreating, updating, or deleting such a component logged `Aborting to hot-reload a state store component that is used as an actor state store` and the change was silently ignored until daprd was restarted.\r\nIn particular, a daprd which started without an actor state store could never gain working actor hosting or Workflow APIs: adding `actorStateStore: \"true\"` to a state store component had no effect, and every workflow operation kept failing with `the state store is not configured to use the actor runtime`.\r\n\r\n### Impact\r\n\r\nYou were affected if you relied on hot reloading to manage components and needed to introduce, replace, or remove the actor state store without restarting daprd; for example enabling workflows on a running sidecar by promoting an existing state store to the actor state store.\r\n\r\n### Root Cause\r\n\r\nThe restriction was added when the actors subsystem had no support for its state store changing at runtime: the store name was captured once at startup, the decision to enable actor hosting was made once at startup, and nothing reacted to the store later appearing, changing, or disappearing.\r\n\r\n### Solution\r\n\r\nThe actor state store can now be hot reloaded, and the actors subsystem reconciles hosting with the store at runtime:\r\n\r\n- **Adding** an actor state store (creating a marked component, or marking an existing state store) enables actor hosting: the app's actor types and the workflow actor types are advertised to placement, and the actor and Workflow APIs start working; including for workflow workers that connected before the store existed.\r\n- **Removing** the actor state store (deleting the component, or unmarking it) shuts down actor hosting in-process: hosted actors are drained and deactivated (the app receives deactivations), actor types are de-advertised from placement and the Scheduler (daprd remains an actor client and can still invoke actors hosted elsewhere), and actor state and Workflow APIs return `the state store is not configured to use the actor runtime` until a store is configured again. daprd itself keeps running, and hosting resumes automatically when a store returns, with no app or workflow-worker reconnection required.\r\n- **Updating** the actor state store component in place (for example a hot reload picking up a rotated secret referenced by the component) swaps the store instance without interrupting actor hosting: the store is resolved per call, so hosted actors and running workflows continue against the same backing store. Draining is reserved for the store being removed, unmarked, or replaced by a differently-named component.\r\n\r\nTwo further consistency fixes ride along with this change:\r\n\r\n- Actor types are now only advertised to placement while an actor state store is configured. Previously an app declaring actor entities with no actor state store at all would still receive actor invocations, despite actor state, reminders, and workflows being non-functional, contradicting the documented requirement and the `actor hosting disabled` log.\r\n- Hot loading a second component marked as the actor state store is skipped with an error log naming the existing actor state store, instead of the duplicate silently corrupting the component store bookkeeping.\r\n\r\n## All sidecars in a namespace disconnected from Placement when a single sidecar disconnected mid-dissemination\r\n\r\n### Problem\r\n\r\nPeriodically, the Placement server closed every sidecar stream in a namespace within the same millisecond, and all sidecars reconnected at once.\r\nOn reconnect each sidecar halts its local actors, so actors mid-call were interrupted on hosts that were perfectly healthy.\r\nThe dissemination version restarted from scratch after each occurrence.\r\n\r\n### Impact\r\n\r\nYou were affected if you ran actor-hosting sidecars, most visibly in namespaces with many sidecars and frequent deployment rollouts.\r\nEach sidecar disconnecting while a dissemination round was in flight brought the namespace one step closer to a mass disconnect, so large, frequently rolled clusters could see every sidecar in a namespace dropped every few minutes.\r\nActors mid-invocation on unaffected hosts were interrupted each time.\r\n\r\n### Root Cause\r\n\r\nA single failing Placement stream could report its closure twice: once from its receive loop unwinding, and once from a failed dissemination send on the same connection.\r\nThe per-namespace connection counter was decremented for both reports, leaving it one below the real number of live connections after each such event.\r\nOnce the accumulated drift reached zero, the namespace's dissemination loop was shut down while live streams were still attached, closing all of them and resetting the dissemination version.\r\n\r\n### Solution\r\n\r\nA stream closure is now reported exactly once: a failed dissemination send cancels the stream's context, preserving the send error as the close cause, and the receive loop unwinding is the single place a closure is reported from.\r\nThe namespace connection counter now always matches the number of live streams, so a healthy namespace is never torn down by one sidecar's disconnect.\r\n\r\n## Scheduler embedded etcd metrics missing from the metrics endpoint\r\n\r\n### Problem\r\n\r\nAfter upgrading to 1.18, all embedded etcd metrics (`etcd_*`, such as `etcd_server_has_leader`, `etcd_mvcc_db_total_size_in_bytes`, and `etcd_disk_backend_commit_duration_seconds_bucket`) disappeared from the Scheduler's metrics endpoint (default port `9090`).\r\nDapr's own `dapr_scheduler_*` metrics were unaffected.\r\n\r\n### Impact\r\n\r\nYou were affected if you scraped the Scheduler metrics endpoint for embedded etcd metrics, for example to populate the embedded etcd panels of the `dapr-scheduler` Grafana dashboard or to alert on etcd database size and disk commit latency.\r\nThose series stopped being emitted after upgrading from 1.17 to 1.18.\r\n\r\n### Root Cause\r\n\r\nIn 1.18 the metrics exporter shared by all Dapr binaries switched from serving the process-global default Prometheus registry to a fresh private registry, to avoid duplicate collector registration panics when the exporter is restarted within the same process.\r\nEmbedded etcd registers all of its collectors on the global default registry during package initialization, so its metrics were never written into the private registry the endpoint served, and were exposed nowhere.\r\n\r\n### Solution\r\n\r\nThe metrics endpoint now gathers from both the private registry and the global default registry.\r\nNothing is ever registered on the default registry by the exporter itself, so in-process restarts remain safe and the original panic cannot reoccur.\r\nAll `etcd_*` metrics are emitted on the Scheduler metrics endpoint again, and other metrics registered on the default registry by linked libraries (such as `grpc_server_*` and `grpc_client_*`) are restored to their 1.17 behavior across Dapr binaries.\r\n\r\n## Scheduler hanging on shutdown and failing to redeliver jobs after client disconnects\r\n\r\n### Problem\r\n\r\nTwo related failure modes in the Scheduler:\r\n\r\n1. A Scheduler instance could hang forever during shutdown, remaining as a zombie process: its gRPC transport stayed alive and its health endpoint kept returning 200, but no handler was serving.\r\n   Connected daprd sidecars hung on in-flight calls to the zombie instead of failing over.\r\n2. After a daprd sidecar disconnected abruptly, the Scheduler could stop delivering jobs and actor reminders for that sidecar's entire namespace, in some cases for minutes, even though other healthy sidecars remained connected.\r\n\r\n### Impact\r\n\r\nA Scheduler restart or crash under load could escalate into a cluster-wide outage window: job and actor reminder delivery stalled until pods were manually restarted or clients reconnected and re-registered.\r\nYou were affected if you run the Scheduler in HA with actively connected sidecars, most visibly under high job or workflow throughput.\r\n\r\n### Root Cause\r\n\r\nShutdown used an unbounded gRPC `GracefulStop()`, which waits for every open stream.\r\nA `WatchJobs` stream whose client had connected but never sent, or had stopped reading, left its handler blocked in its initial `Recv` (or a flow-control blocked `Send`), so the drain never completed and the process never exited.\r\nReadiness was only failed after the drain completed, so health probes never detected the zombie.\r\n\r\nSeparately, a dead `WatchJobs` stream emitted duplicate close events (one from its receive loop plus one per failed send), while the per-namespace connection counter was decremented once per event.\r\nDuplicates could drive the count to zero while live streams remained, deleting the namespace and dropping every stream and deliverable job prefix in it.\r\nFailed sends also held their job's completion callback until the stream was fully reaped, so the scheduling engine could not promptly redeliver those jobs to healthy streams.\r\nA data race in the connection event-loop object recycling could additionally corrupt loop state under connection churn.\r\n\r\n### Solution\r\n\r\nScheduler shutdown now fails readiness before draining and bounds the graceful drain to 5 seconds before forcing the gRPC server to stop, guaranteeing process exit.\r\nDead streams are now closed exactly once: the close cancels the stream at detection time, promptly deregisters its deliverable prefixes, and resolves in-flight jobs as undeliverable so the engine immediately redelivers them to healthy streams.\r\nNamespace deletion is now confirmed by the connection tracking loop that owns the authoritative stream set, so duplicate or stray close events can no longer tear down a namespace that still has live streams.\r\nThe event-loop object recycling race was removed.\r\n\r\n## Scheduler crash when scheduling a job with a malformed timezone-prefixed schedule\r\n\r\n### Problem\r\n\r\nScheduling a job whose `schedule` carried a timezone prefix (`TZ=` or `CRON_TZ=`) with no schedule after it, such as `TZ=UTC`, crashed the Scheduler process instead of returning an error.\r\n\r\n### Impact\r\n\r\nAny client permitted to schedule a job could bring down a shared Scheduler with a single malformed schedule string.\r\ndaprd does not validate the schedule; it forwards it verbatim to the Scheduler, which parses it, and the Scheduler's gRPC server has no panic-recovery interceptor, so the parse panic terminated the process.\r\nIn a highly available deployment this manifested as a Scheduler pod crash-looping whenever the offending job was (re)loaded.\r\n\r\n### Root Cause\r\n\r\nThe cron parser located the boundary between the timezone prefix and the schedule with `strings.Index(spec, \" \")` and sliced the string on the result without checking for `-1`.\r\nA prefix with no following schedule produced an index of `-1`, and the resulting out-of-range slice panicked.\r\n\r\n### Solution\r\n\r\nThe cron parser (from `github.com/dapr/kit`) now finds the boundary using any whitespace and returns a descriptive error when no schedule follows the timezone prefix.\r\nThe malformed schedule is now rejected when the job is scheduled, and the Scheduler no longer crashes.\r\n\r\n## Timezone prefix silently ignored on `@every` job schedules\r\n\r\n### Problem\r\n\r\nA `schedule` that combined a timezone prefix with an `@every` interval, such as `CRON_TZ=Europe/Rome @every 1h`, was accepted but silently dropped the timezone.\r\n\r\n### Impact\r\n\r\nThe prefix looked like it pinned the job to a timezone, but an `@every` schedule fires at a fixed interval and has no wall-clock time for a timezone to apply to.\r\nA user who set `CRON_TZ=Europe/Rome @every 24h` intending \"every day at the same local time\" would see the fire time drift by an hour across daylight saving transitions — the exact problem the timezone prefix appears to prevent.\r\n\r\n### Root Cause\r\n\r\nAn `@every` schedule parses to a fixed constant-delay schedule that carries no location, so the parsed timezone was discarded rather than applied.\r\n\r\n### Solution\r\n\r\nThe cron parser (from `github.com/dapr/kit`) now rejects a timezone prefix on an `@every` schedule when the job is scheduled, so the misconfiguration surfaces immediately instead of silently producing wrong fire times.\r\nTo pin a recurring job to a wall-clock time in a timezone, use a cron expression instead, for example `CRON_TZ=Europe/Rome 0 0 9 * * *`.\r\n\r\n## Workflow GetInstance intermittently failing while the workflow is running\r\n\r\n### Problem\r\n\r\nA read-only workflow status query (`GetInstance` / `GetWorkflowMetadata`, or the equivalent SDK calls such as `GetWorkflowStateAsync`) could intermittently fail with a gRPC `Unknown` error while the queried workflow was running normally:\r\n\r\n```\r\nStatus(StatusCode=\"Unknown\", Detail=\"workflow '<id>': inbox key 'inbox-000000' declared in metadata (inboxLength=1) but missing from state store (transient store read failure or partial save?)\")\r\n```\r\n\r\nThe failure was transient and self-healing: the next poll for the same instance succeeded, and the workflow itself completed successfully.\r\n\r\n### Impact\r\n\r\nYou were affected if you polled workflow status while workflows were making progress, most visibly under high activity concurrency (for example a fan-out of many parallel activities polled by several concurrent clients), although a single activity transition could also trigger it.\r\nCallers received a terminal-looking error for a healthy workflow, so clients without their own retry logic surfaced spurious failures.\r\nThe persisted workflow state was never actually inconsistent.\r\n\r\n### Root Cause\r\n\r\nLoading workflow state reads the `metadata` row and the `inbox-*`/`history-*` entry rows in two separate state store calls, and the status query path performs these reads without holding the workflow actor's lock.\r\nA workflow actor save is a single atomic transaction that deletes consumed inbox entries and writes updated metadata, so a save committing between the reader's two calls produced a torn read: the old metadata still declared inbox entries that the second read no longer found.\r\nThis torn read was reported as a hard error to the caller.\r\n\r\n### Solution\r\n\r\nThe workflow state load now detects this mismatch and retries the whole load with freshly read metadata, up to 5 attempts spaced 15 ms apart.\r\nThe metadata ETag is compared between attempts: a changed ETag proves a concurrent save landed between the reads (retry), while an unchanged ETag proves the entries are genuinely missing from the store, in which case the original error is still returned.\r\nStatus queries racing an active workflow now return consistent results instead of transient `Unknown` errors.\r\n\r\n## Input bindings not activated when the app is slow to answer the subscription discovery probe\r\n\r\n### Problem\r\n\r\nBefore it starts reading from an input binding, daprd asks the application whether it subscribes to that binding: an HTTP `OPTIONS` request to the binding's route, or a gRPC `ListInputBindings` call.\r\nThis request was given a hardcoded 3 second budget with no way to change it.\r\nAn application that had not finished warming up within those 3 seconds never answered in time, so daprd treated the binding as unsubscribed and never activated it.\r\n\r\n### Impact\r\n\r\nYou were affected if your application is slow to serve its first request after startup — JVM or JIT warmup, large dependency-injection graphs, or resource-constrained nodes — and you declared an input binding without an explicit `direction: input` metadata entry.\r\n\r\nThe binding component itself initialized correctly and appeared in the sidecar's metadata endpoint, so the component looked healthy while no events were ever delivered.\r\nOn the HTTP channel a failed probe also aborted the remaining bindings, leaving every input binding on that sidecar inactive; the only trace was a `failed to read from bindings` warning in the sidecar log.\r\nOn the gRPC channel the probe failure was silent.\r\n\r\n### Root Cause\r\n\r\nThe subscription discovery deadline was hardcoded to 3 seconds in the binding processor and built from a background context.\r\nIt could neither be tuned for applications with slow startup nor cancelled when the runtime shut down while a probe was still in flight.\r\n\r\n### Solution\r\n\r\nThe timeout is now configurable through the new daprd `--app-binding-options-timeout` flag, which applies to both the HTTP `OPTIONS` probe and the gRPC `ListInputBindings` probe.\r\nThe default remains 3 seconds, so existing deployments are unchanged, and non-positive values fall back to that default.\r\nThe probe is now derived from the runtime's context, so it is cancelled promptly on shutdown instead of running to its full deadline.\r\n\r\nSetting `direction: input` on the binding component continues to skip the probe entirely.\r\n\r\n## MCPServer left unusable when its endpoint was briefly unavailable at startup\r\n\r\n### Problem\r\n\r\nLoading an MCPServer resource connects to the MCP server and lists its tools, which is what installs that server's `dapr.internal.mcp.<name>.ListTools` and `.CallTool.<tool>` workflows and registers the workflow actor host they run on.\r\nIf that connection failed, the failure was final: registration was never re-attempted for the life of the sidecar process.\r\n\r\n### Impact\r\n\r\nYou were affected if an MCPServer's endpoint was not serving at the moment daprd loaded it, for example an origin still starting up, a proxy or tunnel in front of it returning 502 while its backend came up, or DNS that had not yet propagated.\r\n\r\nThe resource still appeared in the metadata API, so it looked loaded, but none of its workflows existed.\r\nA client that read the metadata and scheduled one of them found no host for the workflow actor type, and workflow creation retries a missing host indefinitely, so the call hung until the caller's context expired instead of failing.\r\nWith an unbounded context it did not return at all.\r\nRecovery required editing the resource to trigger a hot reload, or restarting the sidecar.\r\n\r\n### Root Cause\r\n\r\nRegistration was invoked exactly once per resource load with no retry, so a momentary connection failure was indistinguishable from a permanently unreachable endpoint.\r\nBoth left the server listed in metadata and unusable.\r\n\r\n### Solution\r\n\r\nMCPServer registration now runs under Dapr's built-in initialization retry policy, the same one used elsewhere for resource initialization: exponential backoff starting at 500ms, up to 3 retries within a 10 second budget.\r\nAn endpoint that becomes reachable inside that window is registered normally and its tools work as expected.\r\nA genuinely unreachable endpoint behaves as before once the retries are exhausted, and `ignoreErrors: true` continues to keep daprd running.\r\n\r\n## Crash when delivering a pub/sub message with a non-string trace field over HTTP\r\n\r\n### Problem\r\n\r\nDelivering a pub/sub message to a subscriber over HTTP crashed the daprd process when the message's CloudEvent carried a non-string `traceparent` or `traceid` field, for example a JSON number, boolean, or object instead of a string.\r\n\r\n### Impact\r\n\r\nAny client permitted to publish to a subscribed topic could crash the sidecar with a single message.\r\nA publisher controls the CloudEvent trace fields: publishing with content type `application/cloudevents+json` and a body such as `{\"specversion\":\"1.0\", ..., \"traceid\":12345}` preserves the non-string value all the way through to delivery.\r\nThe HTTP delivery path runs in a background goroutine with no panic recovery, so the failure terminated the whole process rather than dropping the single message.\r\nThe gRPC delivery path was not affected.\r\nIn a highly available deployment this manifested as sidecars crash-looping whenever the offending message was redelivered.\r\n\r\n### Root Cause\r\n\r\nThe HTTP pub/sub delivery path (`Deliver` and `DeliverBulk` in `pkg/runtime/subscription/postman/http`) read the trace field from the CloudEvent and performed an unchecked type assertion to `string`.\r\nThe CloudEvent is deserialized from publisher-controlled bytes into a `map[string]any`, so a non-string trace field became a `float64`, `bool`, or map, and the assertion panicked.\r\n\r\n### Solution\r\n\r\nThe HTTP delivery path now uses a checked type assertion, matching the gRPC delivery path: a non-string trace field is ignored (tracing is skipped for that message) instead of crashing the process.\r\nMessages carrying a malformed trace field are now delivered to the subscriber normally.\r\n\r\n## Workflow `continue_as_new` iterations sharing a single unbounded trace\r\n\r\n### Problem\r\n\r\nWhen a workflow restarted itself with `continue_as_new`, the new iteration inherited the previous iteration's trace context instead of starting a trace of its own.\r\nEvery iteration of an eternal workflow therefore joined the trace of the very first iteration, sharing one trace ID indefinitely.\r\n\r\n### Impact\r\n\r\nYou were affected if you ran eternal or polling workflows built on `continue_as_new` with distributed tracing enabled (OTLP or Zipkin exporter to any backend, such as Jaeger, Azure Monitor, or Geneva).\r\nA workflow iterating over hours or days produced a single trace accumulating thousands of spans.\r\nWorkflows running without tracing enabled were unaffected.\r\n\r\n### Root Cause\r\n\r\nOn a `continue_as_new` completion, the durable task engine built the new iteration's execution-started event by copying the previous iteration's parent trace context verbatim.\r\nEach iteration's orchestration span was therefore parented off the original trace, and the trace grew without bound.\r\n\r\n### Solution\r\n\r\nWhen the prior iteration was traced, each `continue_as_new` transition now generates a fresh W3C root trace context (new random trace and span IDs) for the new iteration, so every iteration produces its own bounded, independently queryable trace.\r\nActivity spans and spans created inside the application continue to join their own iteration's trace as before.\r\nA workflow that was not being traced stays untraced across the transition.\r\n\r\n## Stalled workflows permanently stuck after the last workflow worker disconnects\r\n\r\n### Problem\r\n\r\nA workflow that entered the `STALLED` state (version not available, patch mismatch, or payload size exceeded) could become permanently stuck if the last connected workflow worker disconnected while the workflow was stalled.\r\nReconnecting workers, including workers registering the exact workflow version the stall was waiting for, did not resume it: status queries kept reporting `STALLED` and the only recovery was restarting daprd.\r\nWhen it occurred, the sidecar logged:\r\n\r\n```\r\nerror while disconnecting work item stream: failed to deactivate workflow '<id>': actor is stalled\r\n```\r\n\r\n### Impact\r\n\r\nStalling exists so that a workflow survives its workers going away and resumes once capable workers return, most commonly a rolling upgrade where the old application version disconnects and the new version reconnects.\r\nYou were affected if you use workflow versioning, patching, or a configured `--max-body-size`, and all workflow workers of an application disconnected while a workflow was stalled; for example during application restarts, rolling upgrades, scale-to-zero.\r\n\r\n### Root Cause\r\n\r\nA stalled workflow actor parks its execution in-process, holding the execution reminder in flight until its context is cancelled, and marks its lock as stalled.\r\nWhen the last workflow worker disconnects, daprd unregisters the workflow actor types and deactivates all workflow actors, but deactivation begins by acquiring the actor's lock, and the lock rejects acquisition while the actor is stalled.\r\nWhether the workflow could later recover came down to a race: if the scheduler's reminder stream teardown cancelled the parked execution before deactivation reached the actor, deactivation succeeded and the unacknowledged reminder was redelivered when workers reconnected.\r\nIf deactivation won the race, it failed with `actor is stalled`, leaving the actor activated and holding the reminder in flight, so reconnecting workers had nothing to redeliver and the workflow never re-executed.\r\n\r\n### Solution\r\n\r\nDeactivating a stalled workflow actor now wakes the parked execution instead of failing: the held execution returns immediately, leaving the execution reminder unacknowledged, and deactivation completes.\r\nWhen workflow workers reconnect, the reminder is redelivered and the workflow re-executes, resuming and completing once the connected workers satisfy the stall condition (for example the required workflow version is registered, or daprd was restarted with a larger `--max-body-size`).\r\nRecovery from a stall no longer depends on timing, and a daprd restart is no longer required.\r\n\r\n## Fixes orphaned workflow activity-result reminders retrying forever\r\n\r\n### Problem\r\n\r\nWhen a workflow activity completes but its parent workflow actor is unreachable (for example during placement rebalancing or a host restart), the activity actor durably queues the result as an `activity-result` reminder on the workflow actor so the outcome is delivered once the workflow is reachable again.\r\nIf the workflow instance is purged before that reminder fires, the reminder becomes an orphan: it targets an instance that no longer exists.\r\n\r\n### Impact\r\n\r\nThe issue impacts users on Dapr 1.18.0-1.18.2 running workflows with activities where instances are purged (explicitly, or via a state retention policy) while activity results are still in flight, particularly across placement churn such as rolling restarts or scale events.\r\n\r\nEach orphaned reminder adds a permanent one-invocation-per-second load on the daprd hosting the workflow actor type.\r\n\r\n### Root Cause\r\n\r\nThe workflow actor's `activity-result` reminder handler forwarded the instance-not-found error to the reminder system without classifying it as terminal.\r\nThe scheduler treats any error as a failed invocation and applies the reminder's failure policy, which for this reminder type is a constant one-second retry with no retry limit.\r\n\r\n### Solution\r\n\r\nThe `activity-result` reminder handler now treats instance-not-found as a successful delivery outcome:\r\nAt-least-once delivery for live instances is unchanged as the fix only affects reminders whose target instance has been purged.\r\n\r\n## Pub/sub delivery to a gRPC app failing with \"use of closed network connection\"\r\n\r\n### Problem\r\n\r\nDelivering a pub/sub message to an application connected over gRPC intermittently failed before the application ever saw the message, and the sidecar logged:\r\n\r\n```\r\nerror returned from app while processing pub/sub event <id>: retriable error occurred: rpc error: code = Unavailable desc = connection error: desc = \"error reading server preface: read tcp 127.0.0.1:56264->127.0.0.1:14208: use of closed network connection\"\r\n```\r\n\r\nThe application was healthy and listening throughout, and the same message was delivered successfully on a later attempt or after a sidecar restart.\r\n\r\n### Impact\r\n\r\nYou were affected if you ran an application with `--app-protocol grpc` (or `grpcs`) subscribing to topics, and had upgraded to 1.18.\r\nThe failures were most visible on subscriptions receiving sporadic traffic, and on hosts where the application is slow to accept a new connection, for example a busy thread pool or a garbage collection pause.\r\n\r\nEvery affected delivery was reported to the broker as a retriable failure, so the message was either redelivered by the broker (RabbitMQ, Kafka, and other components with redelivery) or dropped for components without it.\r\nThe same connection handling is used for service invocation into a gRPC application, input binding delivery, and job triggers, so those paths could fail the same way.\r\nApplications using `--app-protocol http` were not affected.\r\n\r\n### Root Cause\r\n\r\ndaprd dialed the application with a `MinConnectTimeout` of one second.\r\nIn gRPC that value is the budget for an entire connection attempt, covering the TCP connect and the HTTP/2 handshake, not just the TCP connect: when it expires, gRPC hard-closes the socket, and a request already riding on that connection fails with `error reading server preface: use of closed network connection`.\r\n\r\nUp to 1.17 this was almost never reachable, because daprd held a single application connection for the lifetime of the process and only ever dialed at startup.\r\nIn 1.18 the application connection moved into the same pool used for sidecar-to-sidecar connections, so connections are established on demand while messages are being delivered, and each new connection had one second to complete its handshake before the delivery riding on it failed.\r\n\r\nThe pool made this far more frequent than it needed to be.\r\nThe pool is configured to keep one warm connection to the application, but once that connection had been idle for longer than the pool's three minute idle window it was neither handed out again (it was treated as expired) nor closed (it was the connection being kept warm).\r\nIt was stuck in the pool, unusable and open, so every request arriving after an idle period dialed a brand new connection rather than reusing the warm one.\r\n\r\n### Solution\r\n\r\nA connection attempt to the application is now given gRPC's default budget of 20 seconds instead of one second, matching what daprd already used for sidecar-to-sidecar connections.\r\nThis does not change how long a request waits for an unreachable application: a refused connection still fails immediately, and how long a request waits remains governed by the caller's context and resiliency policy.\r\n\r\nConnections the pool holds to satisfy its warm connection minimum are also no longer expired out.\r\nThe application connection established at startup now stays in use for the life of the sidecar, so a subscription that receives one message an hour no longer re-dials the application for every message.\r\n\r\n## Workflow terminate silently dropped when delivered in the same batch as other events\r\n\r\n### Problem\r\n\r\nTerminating a workflow intermittently had no effect: the workflow's status stayed `RUNNING` and it kept executing activities and timers as if the terminate had never been issued.\r\nBlocking terminate calls (such as `TerminateWorkflowAsync` in the .NET SDK) never returned, because they wait for the instance to reach a terminal status that never came.\r\nWith debug logging enabled, a dropped terminate showed the `ExecutionTerminated` event being delivered and consumed with no effect:\r\n\r\n```\r\nreceived work item with 2 new event(s): [ExecutionTerminated, TaskCompleted#1]\r\nworkflow execution returned with status 'ORCHESTRATION_STATUS_RUNNING'\r\n```\r\n\r\nA workflow terminated while it was suspended was affected the same way, deterministically: the status stayed `SUSPENDED` and every terminate sent to it was lost.\r\n\r\n### Impact\r\n\r\nYou were affected if you terminated workflows that were actively making progress, regardless of SDK language.\r\nThe window depends on inbox pressure: the terminate is dropped when it arrives in the same work item batch as another event and is not the last event in that batch, so long-running workflows that loop over short activities and timers (endless pollers, monitors, `continue_as_new` loops) were the most exposed, while a workflow idling on a single slow activity or timer almost always received its terminate alone and was unaffected.\r\n\r\nA dropped terminate is consumed with its batch and never redelivered, so the instance kept running; a retried terminate raced the same window again.\r\nA recursive terminate that was dropped also never cascaded, leaving child workflows running as orphans.\r\nFor suspended workflows there was no window: every terminate was lost until the workflow was resumed.\r\n\r\n### Root Cause\r\n\r\nThe workflow engine hands an instance's pending events to the workflow executor as one batch, and relied entirely on the SDK executor to turn an `ExecutionTerminated` event into a termination outcome.\r\nSDK executors registered the termination when they processed the event but kept feeding the remaining events of the batch into the workflow code, which resumed past its own termination and produced a competing outcome: scheduling more work, completing normally, or restarting via `continue_as_new`.\r\nA `continue_as_new` outcome always discarded the termination; other outcomes raced it nondeterministically.\r\nA terminate delivered while the workflow was suspended produced no outcome at all, because suspension suppressed every action the executor would have returned.\r\nSince the engine trusted the executor's result and the event was consumed with the batch, the terminate was lost permanently.\r\n\r\n### Solution\r\n\r\nWhen a delivered batch contains an `ExecutionTerminated` event and the executor does not complete the workflow, daprd discards the doomed execution's pending work and completes the instance as `TERMINATED`.\r\nA `continue_as_new` returned alongside a terminate no longer starts a new iteration, and terminating a suspended workflow now terminates it without requiring a resume.\r\n\r\n## Azure component authentication halting at the SPIFFE credential instead of falling back\r\n\r\n### Problem\r\n\r\nAzure (Microsoft Entra ID) components authenticate by trying a chain of credentials in order until one succeeds.\r\nWhen `azureClientId` and `azureTenantId` were set, the chain included the SPIFFE workload identity credential, and if no SPIFFE JWT SVID source was available the chain stopped at that step:\r\n\r\n```\r\nChainedTokenCredential: failed to acquire a token.\r\nAttempted credentials:\r\n\tClientAssertionCredential: failed to get JWT SVID source from context\r\n```\r\n\r\nCredentials later in the chain, such as managed identity or the Azure CLI, were never attempted, so the component failed to authenticate even though a working credential was available.\r\n\r\n### Impact\r\n\r\nYou were affected on Dapr 1.16.0 or later (where the SPIFFE credential joined the default chain) in either of these configurations:\r\n\r\n- An Azure component with `azureClientId` and `azureTenantId` set but no client secret or certificate, relying on a later credential in the default chain (for example managed identity or the Azure CLI).\r\n- An explicit `azureAuthMethods` list placing `spiffeworkloadidentity` before another method (for example `spiffeworkloadidentity,managedidentity`), expecting fallback when SPIFFE is not configured.\r\n\r\n### Root Cause\r\n\r\n`ChainedTokenCredential` only continues past a credential that reports a `credentialUnavailableError`; any other error is treated as fatal and ends the chain.\r\nThe SPIFFE credential returned a plain error when the context carried no JWT SVID source, so a missing prerequisite was treated as a fatal authentication failure rather than a signal to try the next credential.\r\n\r\n### Solution\r\n\r\nThe SPIFFE credential now reports itself as unavailable when no JWT SVID source is present, before any token request is made, which is exactly what `ChainedTokenCredential` requires to continue to the next credential in the chain.\r\nBoth the default chain and explicitly ordered `azureAuthMethods` lists now fall through as expected, and behavior when a SPIFFE source is configured is 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Dapr 1.17.13\r\n\r\nThis update contains the following bug fixes:\r\n- [Stalled workflows permanently stuck after the last workflow worker disconnects](#stalled-workflows-permanently-stuck-after-the-last-workflow-worker-disconnects)\r\n- [Azure component authentication halting at the SPIFFE credential instead of falling back](#azure-component-authentication-halting-at-the-spiffe-credential-instead-of-falling-back)\r\n- [SPIFFE SVID component operation propagation](#spiffe-svid-component-operation-propagation)\r\n\r\n## Stalled workflows permanently stuck after the last workflow worker disconnects\r\n\r\n### Problem\r\n\r\nA workflow that entered the `STALLED` state (version not available, patch mismatch, or payload size exceeded) could become permanently stuck if the last connected workflow worker disconnected while the workflow was stalled.\r\nReconnecting workers, including workers registering the exact workflow version the stall was waiting for, did not resume it: status queries kept reporting `STALLED` and the only recovery was restarting daprd.\r\nWhen it occurred, the sidecar logged:\r\n\r\n```\r\nerror while disconnecting work item stream: failed to deactivate workflow '<id>': actor is stalled\r\n```\r\n\r\n### Impact\r\n\r\nStalling exists precisely so that a workflow survives its workers going away and resumes once capable workers return, most commonly a rolling upgrade where the old application version disconnects and the new version reconnects.\r\nYou were affected if you use workflow versioning, patching, or a configured `--max-body-size`, and all workflow workers of an application disconnected while a workflow was stalled; for example during application restarts, rolling upgrades, scale-to-zero, or behind a load balancer with `WorkflowsClusteredDeployment` enabled.\r\n\r\n### Root Cause\r\n\r\nA stalled workflow actor parks its execution in-process, holding the execution reminder in flight until its context is cancelled, and marks its lock as stalled.\r\nWhen the last workflow worker disconnects, daprd unregisters the workflow actor types and deactivates all workflow actors, but deactivation begins by acquiring the actor's lock, and the lock rejects acquisition while the actor is stalled.\r\nWhether the workflow could later recover came down to a race: if the scheduler's reminder stream teardown cancelled the parked execution before deactivation reached the actor, deactivation succeeded and the unacknowledged reminder was redelivered when workers reconnected; if deactivation won the race, it failed with `actor is stalled`, leaving the actor activated and holding the reminder in flight, so reconnecting workers had nothing to redeliver and the workflow never re-executed.\r\n\r\n### Solution\r\n\r\nDeactivating a stalled workflow actor now wakes the parked execution instead of failing: the held execution returns immediately, leaving the execution reminder unacknowledged, and deactivation completes.\r\nWhen workflow workers reconnect, the reminder is redelivered and the workflow re-executes, resuming and completing once the connected workers satisfy the stall condition (for example the required workflow version is registered, or daprd was restarted with a larger `--max-body-size`).\r\nRecovery from a stall no longer depends on timing, and a daprd restart is no longer required.\r\n\r\n## Azure component authentication halting at the SPIFFE credential instead of falling back\r\n\r\n### Problem\r\n\r\nAzure (Microsoft Entra ID) components authenticate by trying a chain of credentials in order until one succeeds.\r\nWhen `azureClientId` and `azureTenantId` were set, the chain included the SPIFFE workload identity credential, and if no SPIFFE JWT SVID source was available the chain stopped at that step:\r\n\r\n```\r\nChainedTokenCredential: failed to acquire a token.\r\nAttempted credentials:\r\n\tClientAssertionCredential: failed to get JWT SVID source from context\r\n```\r\n\r\nCredentials later in the chain, such as managed identity or the Azure CLI, were never attempted, so the component failed to authenticate even though a working credential was available.\r\n\r\n### Impact\r\n\r\nYou were affected on Dapr 1.16.0 or later (where the SPIFFE credential joined the default chain) in either of these configurations:\r\n\r\n- An Azure component with `azureClientId` and `azureTenantId` set but no client secret or certificate, relying on a later credential in the default chain (for example managed identity or the Azure CLI).\r\n- An explicit `azureAuthMethods` list placing `spiffeworkloadidentity` before another method (for example `spiffeworkloadidentity,managedidentity`), expecting fallback when SPIFFE is not configured.\r\n\r\n### Root Cause\r\n\r\n`ChainedTokenCredential` only continues past a credential that reports a `credentialUnavailableError`; any other error is treated as fatal and ends the chain.\r\nThe SPIFFE credential returned a plain error when the context carried no JWT SVID source, so a missing prerequisite was treated as a fatal authentication failure rather than a signal to try the next credential.\r\n\r\n### Solution\r\n\r\nThe SPIFFE credential now reports itself as unavailable when no JWT SVID source is present, before any token request is made, which is exactly what `ChainedTokenCredential` requires to continue to the next credential in the chain.\r\nBoth the default chain and explicitly ordered `azureAuthMethods` lists now fall through as expected, and behavior when a SPIFFE source is configured is unchanged.\r\n\r\n## SPIFFE SVID component operation propagation\r\n\r\n### Problem\r\n\r\nThe SPIFFE SVID source was not propagated to component operation calls. As a result, any component that wanted to use the SPIFFE ID for authentication had to capture the source in its `init` method and manage it explicitly. SDKs such as the Azure SDK, which can transparently use the SPIFFE SVID source when it is present on the context, were unable to do so.\r\n\r\n### Impact\r\n\r\nAzure components could not leverage the SPIFFE ID for authentication without explicitly handling the source, and this also blocked future support for SPIFFE-based authentication in other components.\r\n\r\n### Root Cause\r\n\r\nThe SPIFFE SVID source was attached only to the component's `init` method context, not to the context passed into operation calls.\r\n\r\n### Solution\r\n\r\nThe SPIFFE SVID source is now attached to the context passed into operation calls, allowing components to use the SPIFFE ID for authentication on a per-operation 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Dapr 1.17.12\r\n\r\nThis update contains the following changes:\r\n- [Update Go to 1.26.5](#update-go-to-1265)\r\n- [Input bindings not activated when the app is slow to answer the subscription discovery probe](#input-bindings-not-activated-when-the-app-is-slow-to-answer-the-subscription-discovery-probe)\r\n\r\n## Update Go to 1.26.5\r\n\r\n### Problem\r\n\r\nDapr was built with Go 1.26.4, which `govulncheck` reported as affected by known vulnerabilities in the Go standard library and toolchain.\r\n\r\n### Impact\r\n\r\nUsers running Dapr 1.17.11 or earlier are affected. The exposure depends on which standard library packages the reported vulnerabilities cover; see the [Go 1.26.5 release notes](https://go.dev/doc/devel/release#go1.26) for the specific issues fixed.\r\n\r\n### Root Cause\r\n\r\nThe issues originated in the Go standard library and toolchain rather than in Dapr code.\r\n\r\n### Solution\r\n\r\nThe Go version used to build Dapr has been updated from 1.26.4 to 1.26.5 across the runtime, build tooling, and container images.\r\n\r\n## Input bindings not activated when the app is slow to answer the subscription discovery probe\r\n\r\n### Problem\r\n\r\nBefore it starts reading from an input binding, daprd asks the application whether it subscribes to that binding: an HTTP `OPTIONS` request to the binding's route, or a gRPC `ListInputBindings` call.\r\nThis request was given a hardcoded 3 second budget with no way to change it.\r\nAn application that had not finished warming up within those 3 seconds never answered in time, so daprd treated the binding as unsubscribed and never activated it.\r\n\r\n### Impact\r\n\r\nYou were affected if your application is slow to serve its first request after startup — JVM or JIT warmup, large dependency-injection graphs, or resource-constrained nodes — and you declared an input binding without an explicit `direction: input` metadata entry.\r\n\r\nThe binding component itself initialized correctly and appeared in the sidecar's metadata endpoint, so the component looked healthy while no events were ever delivered.\r\nOn the HTTP channel a failed probe also aborted the remaining bindings, leaving every input binding on that sidecar inactive; the only trace was a `failed to read from bindings` warning in the sidecar log.\r\nOn the gRPC channel the probe failure was silent.\r\n\r\n### Root Cause\r\n\r\nThe subscription discovery deadline was hardcoded to 3 seconds in the binding processor and built from a background context.\r\nIt could neither be tuned for applications with slow startup nor cancelled when the runtime shut down while a probe was still in flight.\r\n\r\n### Solution\r\n\r\nThe timeout is now configurable through the new daprd `--app-binding-options-timeout` flag, which applies to both the HTTP `OPTIONS` probe and the gRPC `ListInputBindings` probe.\r\nThe default remains 3 seconds, so existing deployments are unchanged, and non-positive values fall back to that default.\r\nThe probe is now derived from the runtime's context, so it is cancelled promptly on shutdown instead of running to its full deadline.\r\n\r\nSetting `direction: input` on the binding component continues to skip the probe 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Dapr 1.16.18\n\nThis update contains the following bug fix:\n- [Input bindings not activated when the app is slow to answer the subscription discovery probe](#input-bindings-not-activated-when-the-app-is-slow-to-answer-the-subscription-discovery-probe)\n\n## Input bindings not activated when the app is slow to answer the subscription discovery probe\n\n### Problem\n\nBefore it starts reading from an input binding, daprd asks the application whether it subscribes to that binding: an HTTP `OPTIONS` request to the binding's route, or a gRPC `ListInputBindings` call.\nThis request was given a hardcoded 3 second budget with no way to change it.\nAn application that had not finished warming up within those 3 seconds never answered in time, so daprd treated the binding as unsubscribed and never activated it.\n\n### Impact\n\nYou were affected if your application is slow to serve its first request after startup — JVM or JIT warmup, large dependency-injection graphs, or resource-constrained nodes — and you declared an input binding without an explicit `direction: input` metadata entry.\n\nThe binding component itself initialized correctly and appeared in the sidecar's metadata endpoint, so the component looked healthy while no events were ever delivered.\nOn the HTTP channel a failed probe also aborted the remaining bindings, leaving every input binding on that sidecar inactive; the only trace was a `failed to read from bindings` warning in the sidecar log.\nOn the gRPC channel the probe failure was silent.\n\n### Root Cause\n\nThe subscription discovery deadline was hardcoded to 3 seconds in the binding processor and built from a background context.\nIt could neither be tuned for applications with slow startup nor cancelled when the runtime shut down while a probe was still in flight.\n\n### Solution\n\nThe timeout is now configurable through the new daprd `--app-binding-options-timeout` flag, which applies to both the HTTP `OPTIONS` probe and the gRPC `ListInputBindings` probe.\nThe default remains 3 seconds, so existing deployments are unchanged, and non-positive values fall back to that default.\nThe probe is now derived from the runtime's context, so it is cancelled promptly on shutdown instead of running to its full deadline.\n\nSetting `direction: input` on the binding component continues to skip the probe 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Dapr 1.18.2\r\n\r\nThis update contains the following bug fixes:\r\n- [SPIFFE SVID component operation propagation](#spiffe-svid-component-operation-propagation)\r\n- [MCPServers with secret-referenced credentials reload every 60 seconds](#mcpservers-with-secret-referenced-credentials-reload-every-60-seconds)\r\n- [Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`](#actor-reminders-and-jobs-fail-to-register-when-their-name-or-actor-id-contains-characters-such-as--or-)\r\n- [IAM Roles Anywhere X.509 authentication breaks with certificate chains, SVID refresh, and missing SVIDs](#iam-roles-anywhere-x509-authentication-breaks-with-certificate-chains-svid-refresh-and-missing-svids)\r\n- [Redis components updated to go-redis v9.21.0](#redis-components-updated-to-go-redis-v9210)\r\n- [Kafka components lack connection timeouts, a broker health check, and tunable producer configuration](#kafka-components-lack-connection-timeouts-a-broker-health-check-and-tunable-producer-configuration)\r\n- [Sidecars restart when an unrelated Configuration is created or updated](#sidecars-restart-when-an-unrelated-configuration-is-created-or-updated)\r\n- [Sidecar reserves its internal gRPC port before initializing components](#sidecar-reserves-its-internal-grpc-port-before-initializing-components)\r\n- [A slow actor timer callback delays timers for every other actor](#a-slow-actor-timer-callback-delays-timers-for-every-other-actor)\r\n- [Reusing a workflow instance ID while child workflows from the previous execution are still running](#reusing-a-workflow-instance-id-while-child-workflows-from-the-previous-execution-are-still-running)\r\n- [gRPC streaming actor applications could not host actors without opening an app port](#grpc-streaming-actor-applications-could-not-host-actors-without-opening-an-app-port)\r\n- [go-chi updated to v5.2.4 for CVE-2025-69725](#go-chi-updated-to-v524-for-cve-2025-69725)\r\n- [mongo-driver updated to v1.17.7 for CVE-2026-2303](#mongo-driver-updated-to-v1177-for-cve-2026-2303)\r\n- [workflow execution metrics status for terminated workflows reported as failed](#workflow-execution-metrics-status-for-terminated-workflows-reported-as-failed)\r\n- [Sub-millisecond latencies are omitted from latency metrics](#sub-millisecond-latencies-are-omitted-from-latency-metrics)\r\n- [Parent workflow becomes stuck when creating a child workflow with an instance ID that is already in use](#parent-workflow-becomes-stuck-when-creating-a-child-workflow-with-an-instance-id-that-is-already-in-use)\r\n- [Querying workflow instance history crashes the sidecar when no actor state store is configured](#querying-workflow-instance-history-crashes-the-sidecar-when-no-actor-state-store-is-configured)\r\n- [Terminating a running workflow could leave it permanently stuck in RUNNING](#terminating-a-running-workflow-could-leave-it-permanently-stuck-in-running)\r\n\r\n## SPIFFE SVID component operation propagation\r\n\r\n### Problem\r\n\r\nThe SPIFFE SVID source was not propagated to component operation calls. As a result, any component that wanted to use the SPIFFE ID for authentication had to capture the source in its `init` method and manage it explicitly. SDKs such as the Azure SDK, which can transparently use the SPIFFE SVID source when it is present on the context, were unable to do so.\r\n\r\n### Impact\r\n\r\nAzure components could not leverage the SPIFFE ID for authentication without explicitly handling the source, and this also blocked future support for SPIFFE-based authentication in other components.\r\n\r\n### Root Cause\r\n\r\nThe SPIFFE SVID source was attached only to the component's `init` method context, not to the context passed into operation calls.\r\n\r\n### Solution\r\n\r\nThe SPIFFE SVID source is now attached to the context passed into operation calls, allowing components to use the SPIFFE ID for authentication on a per-operation basis.\r\n\r\n## MCPServers with secret-referenced credentials reload every 60 seconds\r\n\r\n### Problem\r\n\r\nAn MCPServer whose transport credentials come from a secret (a `secretKeyRef` or `envRef` in `spec.endpoint.streamableHTTP.headers`, `spec.endpoint.sse.headers`, or `spec.endpoint.stdio.env`) was closed and reloaded roughly every 60 seconds, even when nothing about the resource had changed.\r\n\r\n### Impact\r\n\r\nYou were affected if you ran one or more MCPServers whose headers or stdio environment referenced a secret.\r\nPlain MCPServers with no secret references were not affected.\r\n\r\nNote: auth.oauth2.secretKeyRef is not affected.\r\nThe OAuth2 client secret is fetched at connection time and never written into the spec, so it never took part in the comparison and did not churn.\r\n\r\n### Root Cause\r\n\r\nAlongside the event-driven watch, the hot-reload reconciler runs a periodic backup reconcile (about every 60 seconds) that lists resources from the control plane and compares them against the copy the sidecar currently has loaded.\r\nThe loaded copy has its secret references resolved to their values, but the incoming copy was compared without resolving its secret references first.\r\nFor any secret-backed MCPServer the resolved value never matched the unresolved reference, so the comparison always reported a difference and the server was reloaded on every cycle.\r\n\r\n### Solution\r\n\r\nThe MCPServer reconciler now resolves the incoming resource's secret references before comparing it against the loaded copy, matching the behavior already used for components.\r\nAn unchanged secret-backed MCPServer now compares equal and is left running, while a genuine change, including a rotated secret value, still triggers a reload.\r\n\r\n## Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`\r\n\r\n### Problem\r\n\r\nRegistering an actor reminder through the Scheduler service failed when the reminder name, or the actor ID it belongs to, contained certain characters such as the pipe `|` or at sign `@`.\r\nThe same characters are accepted when invoking actors and when saving actor state, so an actor that worked everywhere else could not have a reminder created for it.\r\n\r\nThe error returned in this case was also misleading:\r\n\r\n```\r\na lowercase RFC 1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')\r\n```\r\n\r\nIt claimed only lowercase names were allowed even though uppercase names are in fact accepted, and it did not describe which characters were actually rejected.\r\n\r\n### Impact\r\n\r\nYou were affected if you used Scheduler-backed actor reminders (the default since 1.15) and your reminder names, actor IDs, or scheduled job names contained characters outside the strict DNS-1123 set, for example `|`, `@`, or uppercase letters.\r\n\r\n### Root Cause\r\n\r\nThe Scheduler composes each reminder or job into a single name of the form `actorreminder||<namespace>||<type>||<id>||<name>` (or `app||<namespace>||<appID>||<name>` for jobs), using `||` as an internal delimiter.\r\nEach `||`-delimited segment was then validated against Kubernetes' DNS-1123 subdomain rules, which only permit lowercase alphanumeric characters, `-`, and `.`.\r\nThis was far stricter than the character set Dapr already accepts at its API edge, producing the inconsistency between actor invocation and reminder registration.\r\nBecause the validator lowercased each segment before checking it, uppercase names passed in practice while the surfaced error still referred to lowercase-only RFC 1123 subdomains.\r\n\r\n### Solution\r\n\r\nThe Scheduler now validates names using the same policy Dapr applies at its API edge, so anything accepted for actor invocation can also be used for a reminder or job.\r\nReminder names, job names, and actor identifiers may now contain any character except `/`, `\\`, `#`, `?`, control characters (including the NUL byte), and the exact path sequences `.` and `..`.\r\nUppercase letters and characters such as `|` and `@` are allowed, and `||` continues to be accepted within actor IDs and names.\r\nListing reminders for actors whose IDs contain `||` now also reports the correct actor metadata.\r\nValidation errors now describe the characters that are actually disallowed.\r\n\r\n## IAM Roles Anywhere X.509 authentication breaks with certificate chains, SVID refresh, and missing SVIDs\r\n\r\n### Problem\r\n\r\nThe IAM Roles Anywhere X.509 credential provider used by AWS components did not work with realistic SPIFFE / Dapr Sentry PKIs.\r\nIt presented only the leaf certificate, so a workload whose leaf is signed by an intermediate CA could not be validated against a trust anchor registered at the chain root.\r\nAfter the short-lived SPIFFE SVID expired, every credential refresh failed with `missing required fields` and the component lost AWS access.\r\nWhen no SPIFFE SVID was present, or the workload private key was not ECDSA, the provider panicked and crashed the entire sidecar.\r\n\r\n### Impact\r\n\r\nYou were affected if you used AWS components configured with IAM Roles Anywhere X.509 authentication (`trustAnchorArn`, `trustProfileArn`, and `assumeRoleArn`), particularly with a certificate chain that includes one or more intermediate CAs.\r\nAffected sidecars lost AWS access once the SVID rotated, and a missing or non-ECDSA SVID could crash the sidecar outright.\r\n\r\n### Root Cause\r\n\r\nOnly the leaf certificate was sent to IAM Roles Anywhere, omitting the intermediate chain needed to build a path to the registered trust anchor.\r\nThe refresh path rebuilt the credential provider from region and ARN fields that the constructor never populated, so any refresh after SVID expiry failed validation.\r\nThe provider also used an unchecked type assertion on the private key and did not guard against a missing SVID, turning both conditions into panics.\r\n\r\n### Solution\r\n\r\nThe signer now presents the leaf plus the intermediate chain, which is sent in the `X-Amz-X509-Chain` header so IAM Roles Anywhere can validate against a trust anchor at the chain root.\r\nCredential refresh now swaps the signer on the existing provider, which already retains the region and profile/anchor/role ARNs from initialization, instead of rebuilding it from unset fields.\r\nThe refreshed certificate is stored only after the signer swap succeeds, keeping the certificate and signer in lockstep so a failed refresh keeps retrying instead of silently adopting a stale signer.\r\nA missing SPIFFE SVID and a non-ECDSA workload key now return errors instead of panicking, so a misconfiguration no longer crashes the sidecar.\r\n\r\n## Redis components updated to go-redis v9.21.0\r\n\r\n### Problem\r\n\r\nThe shared Redis client used by Dapr's Redis-backed components was pinned to `github.com/redis/go-redis/v9` v9.6.3.\r\nMoving forward to the current v9.21.0, which carries the bug fixes and improvements accumulated across many upstream releases, was blocked by a build break: v9.21.0 adds two fields (`MillisElapsedFromDelivery` and `DeliveredCount`) to the go-redis `XMessage` type, which broke the whole-struct conversion Dapr used to map `XClaim` results into its own `RedisXMessage` type.\r\n\r\n### Impact\r\n\r\nYou are affected if you use any Redis-backed component, such as the Redis state store, pub/sub, configuration store, or distributed lock.\r\nThese now run on go-redis v9.21.0 instead of v9.6.3.\r\n\r\n### Root Cause\r\n\r\n`XClaimResult` converted each go-redis `XMessage` to the internal `RedisXMessage` with a whole-struct conversion (`RedisXMessage(xMessage)`), which is only valid while the two structs have identical fields.\r\nWhen v9.21.0 added fields to `XMessage`, that conversion no longer compiled.\r\n\r\n### Solution\r\n\r\nThe Redis client dependency is bumped to go-redis v9.21.0, and `XClaimResult` now assigns the `ID` and `Values` fields explicitly, matching the field-by-field pattern already used by `XReadGroupResult`.\r\nThis keeps the conversion resilient to future field additions in the upstream `XMessage` type.\r\n\r\n## Kafka components lack connection timeouts, a broker health check, and tunable producer configuration\r\n\r\n### Problem\r\n\r\nThe Kafka components (pub/sub and bindings, including the confluent and wurstmeister variants) are built on a shared Kafka component that uses the Sarama client with its default network configuration. The Sarama dial, read, write, and metadata timeouts were not exposed as component metadata, there was no way to probe broker reachability, and the producer's acknowledgement and retry settings were hard-coded. Against unreachable or slow brokers, operations — including component initialization — could block far longer than expected, with no health signal to surface that a broker had become unreachable.\r\n\r\n### Impact\r\n\r\nYou were affected if you used the Kafka pub/sub or Kafka binding components. Connectivity problems could cause operations to hang and initialization to block on unreachable brokers, no built-in check existed to detect broker unavailability, and the producer's durability and retry behavior was fixed and could not be tuned.\r\n\r\n### Root Cause\r\n\r\nThe shared Kafka component created its Sarama client with Sarama's default configuration and did not surface the `Net` dial, read, write, and metadata timeouts as component metadata, so callers could not bound how long operations waited on the network. No broker connectivity probe existed, and the producer's `RequiredAcks` and retry count were hard-coded when constructing the sync producer.\r\n\r\n### Solution\r\n\r\nThe shared Kafka component now exposes tunable network timeouts and producer settings through component metadata, and adds a broker health check:\r\n\r\n- New network timeout metadata: `dialTimeout`, `readTimeout`, and `writeTimeout` (each default 30s), and `metadataTimeout` (default 0, meaning Sarama's own default applies). Invalid values fall back to the defaults.\r\n- New producer metadata: `producerRequiredAcks` (`all` (default), `local`, or `none`) and `producerRetryMax` (default 5), preserving the previous behavior when unset.\r\n- A broker connectivity probe that verifies the configured brokers are reachable. It disables metadata retries so it fails fast against an unhealthy cluster, honors the caller's context and timeout, and does not modify component state.\r\n- Component initialization no longer blocks indefinitely on unreachable brokers.\r\n\r\nAll defaults preserve the previous behavior, so no configuration change is required.\r\n\r\n## Sidecars restart when an unrelated Configuration is created or updated\r\n\r\n### Problem\r\n\r\nIn Kubernetes mode, a Dapr sidecar performed a full runtime restart (a SIGHUP hot reload) whenever any Configuration resource in its namespace was created or updated, including Configuration resources the sidecar does not use. Configuration hot reloading restarts the sidecar by design, but it should only do so for the Configuration that the sidecar is actually running with.\r\n\r\n### Impact\r\n\r\nYou were affected if you ran more than one app with different `dapr.io/config` annotations in the same namespace and created or updated Configuration resources there.\r\nUnrelated apps would restart their sidecars on every such change.\r\nApps that are the only Dapr app in their namespace, or namespaces with a single Configuration, were effectively unaffected.\r\n\r\n### Root Cause\r\n\r\nThe operator streams Configuration updates to sidecars over a per-app stream that filters resources by the requesting app's namespace and scopes.\r\nConfiguration resources do not carry scopes, so the operator's informer filter treated every Configuration in the namespace as belonging to every app and streamed all of them to every connected sidecar.\r\nThe sidecar's SIGHUP reconciler then restarted the runtime for Configurations it had never loaded.\r\n\r\n### Solution\r\n\r\nThe operator now determines, server side, which Configuration is assigned to a connecting sidecar from its pod's `dapr.io/config` annotation, keyed off the app identity authenticated over mTLS, and only streams updates for that Configuration.\r\nThe sidecar is not trusted to self-report which Configuration is its own, so a sidecar can no longer be made to receive updates for a Configuration that does not belong to it. A sidecar is now restarted only when its own assigned Configuration changes.\r\nThe pod lookup uses a dedicated metadata-only cache so the operator does not fetch or retain full pod objects.\r\n\r\n## Sidecar reserves its internal gRPC port before initializing components\r\n\r\n### Problem\r\n\r\nA Dapr sidecar (`daprd`) could randomly fail to start with `bind: address already in use` on its internal gRPC port (default `50002`), even though no other process was using it.\r\n\r\n### Impact\r\n\r\nYou were affected if your sidecars loaded components that open outbound connections during initialization (Redis-backed state stores, pub/sub, configuration stores, or locks). It was most likely on Linux and got more frequent with more components and more frequent pod restarts.\r\n\r\n### Root Cause\r\n\r\nThe sidecar initialized its components before binding its internal gRPC port. Component initialization opens outbound connections, and the OS can assign the internal gRPC port as the connection's ephemeral source port (the default `50002` falls inside the Linux ephemeral range `32768`–`60999`), so the sidecar's later attempt to listen on that port failed.\r\n\r\n### Solution\r\n\r\nThe sidecar now binds its internal gRPC port at the very start of runtime initialization, before any component is initialized, and hands that already-bound listener to the internal gRPC server rather than binding the port a second time.\r\nBecause the sidecar holds the port from the outset, the operating system will not hand it out as an ephemeral source port, so component connections can no longer take it and the sidecar starts reliably.\r\nIf initialization fails before the internal gRPC server starts, the reserved port is released.\r\nNo configuration change is required. If you previously set the `dapr.io/internal-grpc-port` annotation to a value outside the dynamic range (for example `61002`) as a workaround, you can keep or remove it.\r\n\r\n## A slow actor timer callback delays timers for every other actor\r\n\r\n### Problem\r\n\r\nAll actor timer callbacks on a sidecar executed one at a time.\r\nWhile one callback was running, no other timer could fire, so a single slow callback delayed the timers of every other actor hosted on that sidecar, and due timers piled up behind it.\r\n\r\n### Impact\r\n\r\nYou were affected if you used actor timers and any timer callback took a noticeable amount of time to return.\r\nTimers for unrelated actors on the same sidecar fired late by the accumulated duration of the callbacks queued ahead of them.\r\n\r\n### Root Cause\r\n\r\nActor timers live in the sidecar's memory and are scheduled by a single time-ordered queue.\r\nThe queue's processing loop invoked each due timer's callback synchronously on its own goroutine, so callbacks executed serially across the entire sidecar regardless of which actor they belonged to.\r\n\r\n### Solution\r\n\r\nDue timers are now routed to a per-actor execution loop.\r\nCallbacks for the same actor still run one at a time in scheduled order, and a repeating timer is still re-armed only after its current callback returns, but callbacks for different actors now run concurrently, so a slow callback delays only its own actor's timers.\r\nPer-actor loops are created when an actor's first timer fires and are reclaimed once the actor has no registered timers and no outstanding fires.\r\nDeleting or replacing a timer now also cancels a fire that is already waiting behind a running callback for the same actor.\r\n\r\n## Reusing a workflow instance ID while child workflows from the previous execution are still running\r\n\r\n### Problem\r\n\r\nCreating a workflow with the instance ID of a completed, failed, or terminated workflow succeeded even while child workflows started by that previous execution were still running.\r\nA still-running child belongs to the old execution but reports its completion to the parent instance ID, so its events could be delivered into the new execution, and a new execution that pins the same child IDs could collide with the old execution's live children.\r\n\r\n### Impact\r\n\r\nYou were affected if you reuse deterministic workflow instance IDs for workflows that create child workflows, and recreated a parent whose children had not finished.\r\nThis arises when a parent completes without awaiting its children, or is terminated without recursion, leaving the children running.\r\nWorkflows created with fresh or auto-generated instance IDs were not affected, and are not affected by the fix.\r\n\r\n### Root Cause\r\n\r\nThe create path only checked the runtime status of the instance being recreated. Child workflows are independent instances that can outlive a terminal parent, and the ones recorded in the previous execution's history were never consulted.\r\n\r\n### Solution\r\n\r\nRecreating a terminal workflow now verifies that every child workflow of the previous execution, checked recursively and across app boundaries, is also in a terminal state or has been purged.\r\nIf a descendant is still running or cannot be verified, the create request is rejected with a conflict error naming the blocking child workflow; purging the workflow continues to free its instance ID unconditionally.\r\nCreates with a fresh instance ID take the same path as before and perform no additional work.\r\n\r\n## gRPC streaming actor applications could not host actors without opening an app port\r\n\r\n### Problem\r\n\r\nDapr 1.18 introduced hosting actors over the app-initiated gRPC callback stream (`SubscribeActorEventsAlpha1`).\r\nA headline property of this feature is that the application dials the sidecar itself and therefore does not need to listen on any port.\r\nIn practice, opening the stream against a sidecar started without `--app-port` failed immediately with a `FailedPrecondition` error stating that actor callback streaming requires a gRPC app channel.\r\nApplications were forced to open a local port and pass it to the sidecar via `--app-port` (or the `dapr.io/app-port` annotation) purely to satisfy the sidecar's channel setup, even though actor traffic never used that port.\r\n\r\n### Impact\r\n\r\nYou were affected if you hosted actors over the streaming actor API and ran the sidecar without an app port configured.\r\nThe callback stream was rejected before registration, so the application's actor types were never registered and no actor traffic could be served.\r\n\r\n### Root Cause\r\n\r\nThe sidecar only creates its app channel when an app port is configured.\r\nBoth the stream endpoint's guard and the actor transport selection obtained the callback stream manager by inspecting the gRPC app channel, so with no app port there was no channel to inspect and the stream was rejected as if the application were HTTP-based.\r\n\r\n### Solution\r\n\r\nThe stream endpoint now serves the callback stream from the runtime-owned stream manager when no app channel exists, and actor registration passes that manager explicitly to the actor transport instead of deriving it from the app channel.\r\nA sidecar started without an app port now accepts the callback stream, registers the application's actor types, and routes invocations, reminders, timers, and deactivations over the stream, so the application does not need to listen on any port.\r\n\r\n## go-chi updated to v5.2.4 for CVE-2025-69725\r\n\r\n### Problem\r\n\r\nThe Dapr HTTP server depends on `github.com/go-chi/chi/v5`, which was pinned to v5.2.2. That version is affected by CVE-2025-69725.\r\n\r\n### Impact\r\n\r\nYou are affected if you run daprd. Dependency scanners report CVE-2025-69725 against the sidecar binary while it is built against go-chi v5.2.2.\r\n\r\n### Root Cause\r\n\r\nThe `github.com/go-chi/chi/v5` dependency predated the upstream fix released in v5.2.4.\r\n\r\n### Solution\r\n\r\n`github.com/go-chi/chi/v5` is updated to v5.2.4, which resolves CVE-2025-69725. This is a dependency-only change with no behavioral impact.\r\n\r\n## mongo-driver updated to v1.17.7 for CVE-2026-2303\r\n\r\n### Problem\r\n\r\nThe MongoDB Go driver `go.mongodb.org/mongo-driver` was pinned to v1.14.0, which is affected by CVE-2026-2303. It is used directly by the actor reminder subsystem and by the MongoDB components in components-contrib.\r\n\r\n### Impact\r\n\r\nYou are affected if you run daprd, and in particular if you use the MongoDB state store. Dependency scanners report CVE-2026-2303 against the sidecar binary while it is built against mongo-driver v1.14.0.\r\n\r\n### Root Cause\r\n\r\nThe `go.mongodb.org/mongo-driver` dependency predated the upstream fix released in v1.17.7, both in the runtime and in the components-contrib dependency.\r\n\r\n### Solution\r\n\r\n`go.mongodb.org/mongo-driver` is updated to v1.17.7, and the `github.com/dapr/components-contrib` dependency is bumped to v1.18.3 to carry the same driver update on the component side. Both resolve CVE-2026-2303. The upgrade stays within the driver's v1 line, so it is a dependency-only change with no behavioral impact.\r\n\r\n## Sub-millisecond latencies are omitted from latency metrics\r\n\r\n### Problem\r\n\r\nLatency measurements below one millisecond were omitted from latency histograms.\r\n\r\n### Impact\r\n\r\nOperations completing in less than one millisecond were not represented in the affected latency histograms, causing low-latency observations to be undercounted.\r\n\r\n### Root Cause\r\n\r\n`ElapsedSince` divided `time.Duration` values before converting the result to `float64`, truncating sub-millisecond durations to zero. Existing positive-latency guards then skipped those observations.\r\n\r\n### Solution\r\n\r\n`ElapsedSince` now converts durations to floating-point milliseconds before division, preserving fractional milliseconds and allowing sub-millisecond latency observations to be recorded.\r\n\r\n## workflow execution metrics status for terminated workflows reported as failed\r\n\r\n### Problem\r\n\r\nThe `dapr_runtime_workflow_execution_count` metric does not distinguish `failed` workflow executions from deliberately `terminated` ones and reports both with the `failed` status label\r\n\r\n### Impact\r\n\r\nOperators are expected to alert on workflow failures using this metric, so status=\"failed\" should mean the workflow failed, excluding those terminated by users.\r\n\r\n### Root Cause\r\n\r\nThe workflow runtime has a simple, two ways logic for assigning the status to the metric: if it is not `RUNTIME_STATUS_COMPLETED` is assumed failed.\r\n\r\n### Solution\r\n\r\nModify this logic to consider `failed` and `terminated` statuses.\r\n\r\n## Parent workflow becomes stuck when creating a child workflow with an instance ID that is already in use\r\n\r\n### Problem\r\n\r\nA workflow that created a child workflow with an explicit instance ID already belonging to another active workflow never advanced.\r\nThe runtime retried creating the child indefinitely, the parent stayed in `RUNNING` forever, and no error was surfaced to the workflow code.\r\nThe sidecar logged a retry warning on every attempt:\r\n\r\n```\r\nWorkflow actor 'parent': execution failed with a recoverable error and will be retried later:\r\n'failed to invoke method 'CreateWorkflowInstance' on actor 'child-id':\r\nrpc error: code = AlreadyExists desc = an active workflow with ID 'child-id' already exists'\r\n```\r\n\r\n### Impact\r\n\r\nYou were affected if your workflows create child workflows with deterministic or user-provided instance IDs that can collide with a live workflow instance.\r\nThe parent workflow hung indefinitely and had to be terminated manually.\r\nChild workflows with auto-generated instance IDs were not affected.\r\n\r\n### Root Cause\r\n\r\nWhen dispatching a child workflow creation, the parent's workflow actor treated every failure as transient and retried it through its wake-up reminder.\r\nThe `AlreadyExists` rejection from the target instance is not transient while the occupying workflow remains active, so the retry loop never succeeded and the failure was never reported back to the awaited child workflow task.\r\n\r\n### Solution\r\n\r\nThe child workflow task now fails immediately with an error naming the conflict (`an active workflow with ID '<id>' already exists`) instead of being retried by the runtime.\r\nThe parent advances: workflow code can handle the error and continue, propagate it to fail the parent, or attach a retry policy to the child workflow call, in which case the creation is re-attempted and succeeds once the instance ID becomes free.\r\nThe same handling applies when the target ID belongs to a terminal workflow whose child workflow tree is not yet terminal, to cross-app child workflows, and to child workflows re-driven by a workflow rerun.\r\nA detached workflow spawn onto an occupied instance ID is dropped with a warning, matching its fire-and-forget semantics.\r\nThe workflow occupying the instance ID is never affected by the rejected creation.\r\n\r\n## Sub-millisecond latencies are omitted from latency metrics\r\n\r\n### Problem\r\n\r\nLatency measurements below one millisecond were omitted from latency histograms.\r\n\r\n### Impact\r\n\r\nOperations completing in less than one millisecond were not represented in the affected latency histograms, causing low-latency observations to be undercounted.\r\n\r\n### Root Cause\r\n\r\n`ElapsedSince` divided `time.Duration` values before converting the result to `float64`, truncating sub-millisecond durations to zero. Existing positive-latency guards then skipped those observations.\r\n\r\n### Solution\r\n\r\n`ElapsedSince` now converts durations to floating-point milliseconds before division, preserving fractional milliseconds and allowing sub-millisecond latency observations to be recorded.\r\n\r\n## Querying workflow instance history crashes the sidecar when no actor state store is configured\r\n\r\n### Problem\r\n\r\nQuerying workflow instance history (`dapr workflow history`, or the `GetInstanceHistory` call on the workflow gRPC API) against a sidecar whose state store is not an actor state store (no component with `actorStateStore: \"true\"`) crashed `daprd` with a nil pointer dereference. The sidecar exited with status 2, taking the application down with it.\r\n\r\n### Impact\r\n\r\nAny 1.18 sidecar without an actor state store that is reachable over the workflow gRPC API could be taken down by a single read-only history query — including queries for instance IDs that do not exist. Other workflow APIs on the same sidecar (such as listing instances) returned a clean error and were not affected.\r\n\r\n### Root Cause\r\n\r\nThe workflow read and purge paths in the actor backend (`GetInstanceHistory`, `loadInternalState`, `purgeWorkflowForce`) obtained the actor state from `State()`, which returns a nil state with no error when no actor state store is configured, and passed it straight to `LoadWorkflowState`, which dereferenced it. The write path already guarded against this, but the read paths did not.\r\n\r\n### Solution\r\n\r\nThe read and purge paths now return the same actionable error the write path already surfaces (`the state store is not configured to use the actor runtime. Have you set the - name: actorStateStore value: \"true\" in your state store component file?`) instead of dereferencing a nil state. The history query fails cleanly and the sidecar stays up.\r\n\r\n## Terminating a running workflow could leave it permanently stuck in RUNNING\r\n\r\n### Problem\r\n\r\nTerminating a running workflow that had fanned out to child workflows could leave the parent workflow permanently stuck in the RUNNING status.\r\nThe termination event remained undrained in the workflow's inbox, no `ExecutionCompleted` event was written to history, and repeated `TerminateWorkflow` calls could hang.\r\nRestarting the application did not recover the instance; only purging it did.\r\n\r\n### Impact\r\n\r\nYou were affected if you terminated running workflows that had child workflows, most likely under load or during transient Scheduler or host unavailability.\r\nAffected instances could never be terminated and reported RUNNING forever, even though the runtime logged them as terminated.\r\nTermination of sibling child workflows could also be delayed indefinitely while the parent retried.\r\n\r\n### Root Cause\r\n\r\nOn a recursive terminate, the workflow engine delivered the termination event to every child workflow before the parent's own terminal state was persisted.\r\nIf delivery to any child failed, for example an unreachable host or a transient Scheduler error while creating the child's wake-up reminder, the whole operation was rolled back, discarding the parent's computed terminal state, and every retry repeated the same failing sequence.\r\nChild delivery also ran while holding the parent workflow actor's lock, which could deadlock against children concurrently reporting their completion back to the parent, causing the terminate call to hang.\r\n\r\n### Solution\r\n\r\nThe parent workflow now persists its terminal state and drains its inbox first, then delivers the termination to its children as a separate, idempotent step that is retried until every child is reached.\r\nSame-app children are terminated through a durable reminder carrying the termination event, which takes no actor locks and keeps retrying while a child's host is unavailable.\r\nCross-app children continue to be terminated through the existing event delivery method.\r\nA failure to reach a child can no longer roll back the parent's terminal state or block its 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Dapr 1.17.11\r\n\r\nThis update contains the following bug fix:\r\n- [Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`](#actor-reminders-and-jobs-fail-to-register-when-their-name-or-actor-id-contains-characters-such-as--or-)\r\n\r\n## Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`\r\n\r\n### Problem\r\n\r\nRegistering an actor reminder through the Scheduler service failed when the reminder name, or the actor ID it belongs to, contained certain characters such as the pipe `|` or at sign `@`.\r\nThe same characters are accepted when invoking actors and when saving actor state, so an actor that worked everywhere else could not have a reminder created for it.\r\n\r\nThe error returned in this case was also misleading:\r\n\r\n```\r\na lowercase RFC 1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')\r\n```\r\n\r\nIt claimed only lowercase names were allowed even though uppercase names are in fact accepted, and it did not describe which characters were actually rejected.\r\n\r\n### Impact\r\n\r\nYou were affected if you used Scheduler-backed actor reminders (the default since 1.15) and your reminder names, actor IDs, or scheduled job names contained characters outside the strict DNS-1123 set, for example `|`, `@`, or uppercase letters.\r\n\r\n### Root Cause\r\n\r\nThe Scheduler composes each reminder or job into a single name of the form `actorreminder||<namespace>||<type>||<id>||<name>` (or `app||<namespace>||<appID>||<name>` for jobs), using `||` as an internal delimiter.\r\nEach `||`-delimited segment was then validated against Kubernetes' DNS-1123 subdomain rules, which only permit lowercase alphanumeric characters, `-`, and `.`.\r\nThis was far stricter than the character set Dapr already accepts at its API edge, producing the inconsistency between actor invocation and reminder registration.\r\nBecause the validator lowercased each segment before checking it, uppercase names passed in practice while the surfaced error still referred to lowercase-only RFC 1123 subdomains.\r\n\r\n### Solution\r\n\r\nThe Scheduler now validates names using the same policy Dapr applies at its API edge, so anything accepted for actor invocation can also be used for a reminder or job.\r\nReminder names, job names, and actor identifiers may now contain any character except `/`, `\\`, `#`, `?`, control characters (including the NUL byte), and the exact path sequences `.` and `..`.\r\nUppercase letters and characters such as `|` and `@` are allowed, and `||` continues to be accepted within actor IDs and names.\r\nListing reminders for actors whose IDs contain `||` now also reports the correct actor metadata.\r\nValidation errors now describe the characters that are actually 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Dapr 1.16.17\r\n\r\nThis update contains the following bug fix:\r\n- [Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`](#actor-reminders-and-jobs-fail-to-register-when-their-name-or-actor-id-contains-characters-such-as--or-)\r\n\r\n## Actor reminders and jobs fail to register when their name or actor ID contains characters such as `|` or `@`\r\n\r\n### Problem\r\n\r\nRegistering an actor reminder through the Scheduler service failed when the reminder name, or the actor ID it belongs to, contained certain characters such as the pipe `|` or at sign `@`.\r\nThe same characters are accepted when invoking actors and when saving actor state, so an actor that worked everywhere else could not have a reminder created for it.\r\n\r\nThe error returned in this case was also misleading:\r\n\r\n```\r\na lowercase RFC 1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')\r\n```\r\n\r\nIt claimed only lowercase names were allowed even though uppercase names are in fact accepted, and it did not describe which characters were actually rejected.\r\n\r\n### Impact\r\n\r\nYou were affected if you used Scheduler-backed actor reminders (the default since 1.15) and your reminder names, actor IDs, or scheduled job names contained characters outside the strict DNS-1123 set, for example `|`, `@`, or uppercase letters.\r\n\r\n### Root Cause\r\n\r\nThe Scheduler composes each reminder or job into a single name of the form `actorreminder||<namespace>||<type>||<id>||<name>` (or `app||<namespace>||<appID>||<name>` for jobs), using `||` as an internal delimiter.\r\nEach `||`-delimited segment was then validated against Kubernetes' DNS-1123 subdomain rules, which only permit lowercase alphanumeric characters, `-`, and `.`.\r\nThis was far stricter than the character set Dapr already accepts at its API edge, producing the inconsistency between actor invocation and reminder registration.\r\nBecause the validator lowercased each segment before checking it, uppercase names passed in practice while the surfaced error still referred to lowercase-only RFC 1123 subdomains.\r\n\r\n### Solution\r\n\r\nThe Scheduler now validates names using the same policy Dapr applies at its API edge, so anything accepted for actor invocation can also be used for a reminder or job.\r\nReminder names, job names, and actor identifiers may now contain any character except `/`, `\\`, `#`, `?`, control characters (including the NUL byte), and the exact path sequences `.` and `..`.\r\nUppercase letters and characters such as `|` and `@` are allowed, and `||` continues to be accepted within actor IDs and names.\r\nListing reminders for actors whose IDs contain `||` now also reports the correct actor metadata.\r\nValidation errors now describe the characters that are actually 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Dapr 1.18.1\r\n\r\nThis update contains the following bug fixes:\r\n- [Workflow event-timer reminders leak when the same event name is awaited repeatedly](#workflow-event-timer-reminders-leak-when-the-same-event-name-is-awaited-repeatedly)\r\n- [Workflow sidecars become permanently unavailable after a configuration reload](#workflow-sidecars-become-permanently-unavailable-after-a-configuration-reload)\r\n- [Sidecars restart unnecessarily on operator configuration resyncs](#sidecars-restart-unnecessarily-on-operator-configuration-resyncs)\r\n- [Workflows hang in RUNNING when a child workflow completion crosses a ContinueAsNew boundary](#workflows-hang-in-running-when-a-child-workflow-completion-crosses-a-continueasnew-boundary)\r\n- [Workflow concurrency limits are silently ignored when Dapr is installed via the Helm chart](#workflow-concurrency-limits-are-silently-ignored-when-dapr-is-installed-via-the-helm-chart)\r\n\r\n## Workflow event-timer reminders leak when the same event name is awaited repeatedly\r\n\r\n### Problem\r\n\r\nWhen a workflow waits on an external event and the event arrives before the wait's timeout timer fires, the timer's reminder is supposed to be deleted, since it no longer serves a purpose.\r\nTo find which reminder to delete, the runtime paired each newly arrived event against the oldest unfired event timer of the same name found anywhere in the workflow's history.\r\nA timer cancelled in a previous run is indistinguishable in history from a pending one — its `TimerCreated` is persisted and its `TimerFired` never arrives — so for workflows that wait on the same event name repeatedly (for example agent-style loops of `waitForExternalEvent` plus an activity per turn), those dead timers sat at the head of the per-name FIFO forever.\r\nEvery new event re-deleted the first turn's long-gone timer reminder while the reminder of the timer actually cancelled that turn was never deleted.\r\n\r\n### Impact\r\n\r\nAny workflow that awaits the same external event name more than once is affected.\r\n\r\nVisible symptoms include:\r\n- One leaked timer reminder in the scheduler per satisfied wait after the first. For indefinite waits (no timeout) these are far-future one-shots that never fire and only get cleaned up by the reminder sweep at workflow completion; long-running instances accumulate them for their entire lifetime.\r\n- For finite timeouts, the leaked reminder eventually fires, causing a spurious workflow wake-up and a stray `TimerFired` event that the SDK then discards as unexpected.\r\n- Debug logs showing the same `deleting cancelled event timer reminder 'timer-N'` line (always the oldest dead timer) on every turn.\r\n\r\n### Root Cause\r\n\r\n`deleteCancelledEventTimers` built the set of unfired event timers from the full history but only consumed entries for events arriving in the current run.\r\nEvents already in history — which had performed their cancellations in previous runs — never advanced the FIFO, so previously cancelled timers permanently shadowed the timer each new event had actually cancelled.\r\n\r\n### Solution\r\n\r\nThe event-to-timer pairing is now replayed deterministically over the full history in chronological order: each `EventRaised` consumes the oldest unfired event timer of the same name created before it.\r\nPairings driven by events already in history reproduce the cancellations performed by previous runs, and only timers consumed by the current run's new events trigger reminder deletions.\r\nThe created-before constraint additionally guarantees that a timer guarding a still-armed wait is never deleted ahead of its own event, which the previous pairing could do when an event and the next wait's timer were persisted in the same run.\r\nTimers with non-event origins (`CreateTimer`, `ActivityRetry`, `ChildWorkflowRetry`) remain excluded from the pairing regardless of name collisions.\r\n\r\n## Workflow sidecars become permanently unavailable after a configuration reload\r\n\r\n### Problem\r\n\r\nA Dapr sidecar that hosts workflows could become permanently unavailable after a configuration hot-reload (SIGHUP).\r\nOnce the sidecar entered this state, every workflow operation failed and the only way to recover was to restart the pod.\r\n\r\n### Impact\r\n\r\nAfter any configuration reload, an affected sidecar kept running but never came back to a usable state.\r\n\r\nVisible symptoms include:\r\n- The sidecar health endpoint reports `NotReady` indefinitely.\r\n- The Dapr gRPC API (port `50001`) stops accepting connections, so workflow calls fail with errors such as `14 UNAVAILABLE: No connection established. Last error: connect ECONNREFUSED 127.0.0.1:50001`.\r\n- Sidecar logs show the workflow engine stopping during the reload but never logging it starting again (the `API gRPC server is running on port 50001` and `Registering workflow engine for gRPC endpoint` lines never reappear).\r\n- The condition persists until the pod is manually restarted.\r\n\r\n### Root Cause\r\n\r\nA configuration reload restarts the Dapr runtime, and the new runtime cannot start until the previous one has fully shut down.\r\nShutdown waits for in-flight gRPC calls to drain gracefully, but a connected workflow worker holds a long-lived `GetWorkItems` streaming connection.\r\nThe workflow engine never signalled that stream to close, so the graceful shutdown blocked on it and the runtime never finished restarting, leaving the API server and workflow engine down.\r\nWith workflow SDK clients that automatically reconnect, the stream was continually re-established, so the sidecar stayed stuck for as long as a worker was connected.\r\n\r\n### Solution\r\n\r\nThe workflow engine now signals all connected `GetWorkItems` streams to close as part of shutdown, before draining the worker, so graceful shutdown completes promptly instead of blocking on those streams.\r\nAfter a configuration reload the runtime restarts cleanly: the sidecar returns to `Ready`, the gRPC API rebinds on port `50001`, and connected workflow clients reconnect automatically with no manual intervention.\r\nIn-flight work items are not lost; undelivered items are re-dispatched from the durable actor backend after the restart.\r\n\r\n## Sidecars restart unnecessarily on operator configuration resyncs\r\n\r\n### Problem\r\n\r\nIn Kubernetes mode, a Dapr sidecar would perform a full runtime restart (SIGHUP) in response to operator events that carried no actual change.\r\n\r\n### Impact\r\n\r\nLong-running sidecars restarted their runtime periodically with no configuration change and no human action.\r\n\r\n### Root Cause\r\n\r\nThe operator's informer streams resource events for the whole namespace to every connected sidecar.\r\nThe Kubernetes informer periodically resyncs, re-delivering every cached object as an `UPDATED` event with an unchanged `resourceVersion`, and also re-delivers on reconnect.\r\nThe operator forwarded these no-op replays verbatim, and the sidecar's SIGHUP reconciler restarts the runtime.\r\n\r\n### Solution\r\n\r\nThe operator now drops informer events whose `resourceVersion` is unchanged before streaming them to sidecars.\r\nA genuine change always advances the `resourceVersion`, so real updates are still delivered and hot reload continues to work; only the no-op resync and reconnect replays are suppressed.\r\nSidecars no longer restart on routine operator resyncs.\r\n\r\n## Workflows hang in RUNNING when a child workflow completion crosses a ContinueAsNew boundary\r\n\r\n### Problem\r\n\r\nA workflow that uses ContinueAsNew and child workflows (or activities) can permanently hang in the RUNNING state.\r\nA child workflow or activity visibly completes on the application side, but the parent never observes the completion and waits forever. The daprd log shows the workflow execution returning `ORCHESTRATION_STATUS_RUNNING` followed later by:\r\n\r\n```\r\nWorkflow actor '<id>': dropping duplicate completion event already present in history/inbox\r\nWorkflow actor '<id>': ignoring run request for reminder 'new-event-...' because the workflow inbox is empty\r\n```\r\n\r\n### Impact\r\n\r\nAffected workflows freeze indefinitely and never complete, fail, or time out.\r\nThe failure is timing dependent and intermittent: it occurs when a child workflow or activity started before a ContinueAsNew completes after it.\r\nAgentic workflows are particularly exposed, as they commonly loop via ContinueAsNew while running child workflows and activities with variable latency.\r\n\r\n### Root Cause\r\n\r\nContinueAsNew resets the workflow's task ID sequence.\r\nWhen a child workflow or activity abandoned by a previous generation completes after the ContinueAsNew, its completion event carries a task ID from the old sequence.\r\nThe new generation has no matching scheduled operation, so the workflow execution consumes the event without effect and it is persisted into history as an orphan.\r\n\r\nWhen the new generation later schedules its own operation, that operation legitimately reuses the same task ID.\r\nIts real completion event is then matched against the orphaned event in history by the completion deduplication added in v1.18.0-rc.3 (intended to drop redelivered completions), and is discarded as a duplicate.\r\nThe sender is acknowledged, so the event is never redelivered, and the re-asserted wake-up reminder fires against an empty inbox and is deleted.\r\nThe workflow is left RUNNING with nothing to drive it.\r\n\r\n### Solution\r\n\r\nThe workflow actor no longer persists resolution events (task or child workflow completions and failures) that match no operation scheduled in history.\r\nStale cross-generation completions are discarded with a warning at the point they are consumed instead of being written into history, so the completion deduplication can no longer mistake a later, legitimate completion for a duplicate. Completions for operations of the current generation are unaffected.\r\n\r\n## Workflow concurrency limits are silently ignored when Dapr is installed via the Helm chart\r\n\r\n### Problem\r\n\r\nThe workflow concurrency limits introduced in 1.18.0 (`globalMaxConcurrentWorkflowInvocations`, `globalMaxConcurrentActivityInvocations`, `workflowConcurrencyLimits`, and `activityConcurrencyLimits` under `spec.workflow` of a `Configuration` resource) were added to the API types but the matching `Configuration` CRD bundled in the Helm chart was never regenerated.\r\nThe chart CRD's OpenAPI schema therefore did not know about these fields.\r\n\r\n### Impact\r\n\r\nYou were affected if both of the following were true:\r\n- You installed the Dapr control plane via the Helm chart (which applies the CRDs from `charts/dapr/crds`) rather than via the CLI.\r\n- You set any of the global or per-name workflow/activity concurrency limits on a `Configuration` resource.\r\n\r\n### Root Cause\r\n\r\nThe CRDs shipped in the Helm chart are generated from the Go API types under `pkg/apis` with `controller-gen`, but the generated output had not been copied into `charts/dapr/crds/configuration.yaml` after the concurrency-limit fields were added. The bundled CRD drifted out of sync with the API types.\r\n\r\n### Solution\r\n\r\nThe chart `Configuration` CRD was regenerated so its schema includes the concurrency-limit fields (and the `NamedConcurrencyLimit` schema they 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Dapr 1.16.16\r\n\r\nThis update contains bug fixes:\r\n\r\n- [Sentry fails to sign certificates when the issuer key type does not match the CSR signature algorithm](#sentry-fails-to-sign-certificates-when-the-issuer-key-type-does-not-match-the-csr-signature-algorithm)\r\n- [Helm downgrade from 1.17 or 1.18 fails on the scheduler StatefulSet storage size](#helm-downgrade-from-117-or-118-fails-on-the-scheduler-statefulset-storage-size)\r\n\r\n## Sentry fails to sign certificates when the issuer key type does not match the CSR signature algorithm\r\n\r\n### Problem\r\n\r\nSentry crashes on startup with:\r\n\r\n```\r\nfatal: error running Sentry: failed to retrieve the initial identity: x509: requested SignatureAlgorithm does not match private key type\r\n```\r\n\r\nThis occurs when the trust bundle issuer key type does not match the key type used in certificate signing requests.\r\nDapr 1.18 generates Ed25519 issuer keys, while 1.16 generates ECDSA keys and ECDSA-signed CSRs.\r\n\r\n### Impact\r\n\r\nAny 1.16 control plane whose `dapr-trust-bundle` secret was generated by Dapr 1.18 is affected, most commonly after a downgrade from 1.18 to 1.16 on the same cluster.\r\n\r\n### Root Cause\r\n\r\nSentry copied the `SignatureAlgorithm` from the incoming CSR onto the workload certificate template. When `x509.CreateCertificate` was called, Go's x509 library rejected the mismatch between the template's signature algorithm (derived from the CSR key type) and the issuer's private key type.\r\n\r\n### Solution\r\n\r\nRemoved the `SignatureAlgorithm` from certificate templates and the `SignRequest` struct, backporting the fix released in Dapr 1.17.2. Go's `x509.CreateCertificate` now infers the correct signature algorithm from the issuer's signing key, allowing sentry to sign certificates regardless of the CSR's key type. A regression test covers signing an ECDSA-keyed identity with an Ed25519 issuer key.\r\n\r\n## Helm downgrade from 1.17 or 1.18 fails on the scheduler StatefulSet storage size\r\n\r\n### Problem\r\n\r\nDowngrading a Dapr control plane from 1.17 or 1.18 to 1.16 with Helm fails with:\r\n\r\n```\r\nStatefulSet.apps \"dapr-scheduler-server\" is invalid: spec: Forbidden: updates to statefulset spec for fields other than 'replicas', 'ordinals', 'template', 'updateStrategy', 'persistentVolumeClaimRetentionPolicy' and 'minReadySeconds' are forbidden\r\n```\r\n\r\n### Impact\r\n\r\nClusters installed on Dapr 1.17 or 1.18, where the scheduler chart defaults the persistent volume claim size to 16Gi, cannot be downgraded to 1.16 without manually passing `--set dapr_scheduler.cluster.storageSize=<live size>` or deleting the StatefulSet with `--cascade=orphan` first.\r\n\r\n### Root Cause\r\n\r\nThe 1.16 chart rendered `spec.volumeClaimTemplates` storage directly from `.Values.cluster.storageSize` (default 1Gi). `StatefulSet.spec.volumeClaimTemplates` is immutable in Kubernetes, so rendering any size other than the one already deployed causes the API server to reject the patch.\r\n\r\n### Solution\r\n\r\nBackported the `dapr_scheduler.storageSize` template helper from the 1.17 chart.\r\nThe chart now looks up the live `dapr-scheduler-server` StatefulSet and pins the rendered storage request to the value already in use, falling back to `.Values.cluster.storageSize` for fresh installs.\r\n\r\n### Downgrade guidance\r\n\r\nWhen downgrading to 1.16.16 with Helm, prefer `--reset-values` and explicitly re-pass the values the install was created with (for example `global.ha.enabled`).\r\nAvoid `--reuse-values`: it carries forward the previous chart's computed defaults, some of which are invalid for 1.16 binaries.\r\nIn particular, the 1.17 and 1.18 charts default the placement `disseminateTimeout` to 8s, which the 1.16 placement binary rejects at startup (valid range 1s to 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Dapr 1.16.15\r\n\r\nThis update contains a bug fix:\r\n- [Sentry fails to start with \"unsupported key type\" when the issuer key is Ed25519 or RSA](#sentry-fails-to-start-with-unsupported-key-type-when-the-issuer-key-is-ed25519-or-rsa)\r\n\r\n## Sentry fails to start with \"unsupported key type\" when the issuer key is Ed25519 or RSA\r\n\r\n### Problem\r\n\r\nOperators who downgraded a control plane from 1.18 back to 1.16 saw `dapr-sentry` crash on startup with:\r\n\r\n```\r\nfatal: error creating CA: failed to get CA bundle: failed to verify CA bundle: unsupported key type ed25519.PrivateKey\r\n```\r\n\r\nThe same failure mode also rejected RSA-keyed issuer bundles.\r\nThe crash is hit before sentry serves any traffic, so every sidecar that depends on sentry for its identity certificate stops being able to obtain or rotate one.\r\n\r\n### Impact\r\n\r\nAny 1.16 control plane whose `dapr-trust-bundle` secret was generated by, or migrated through, a newer Dapr release that issues Ed25519 (or RSA) issuer keys is affected. In practice this includes:\r\n\r\n- Downgrade from Dapr 1.18 to 1.16 against the same cluster.\r\n- Existing 1.16 deployments where the issuer key was rotated or replaced with an Ed25519 / RSA key by the operator.\r\n\r\nSentry crash-loops, no new mTLS identities are issued, and existing certificates are not rotated. Sidecars whose certs have not yet expired keep working; sidecars that come up fresh, restart, or hit cert expiry start failing to obtain identities.\r\n\r\n### Root Cause\r\n\r\n`dapr/kit`'s `crypto/pem.EncodePrivateKey` (used by sentry to re-encode the issuer key it just decoded from the trust bundle) only matched `*ecdsa.PrivateKey` and `*ed25519.PrivateKey` in its type switch. `ed25519.PrivateKey` is itself a `[]byte` alias rather than a struct, so the `*ed25519.PrivateKey` case never matched a real Ed25519 key. RSA private keys were never listed at all.\r\n\r\nWhen sentry called `EncodePrivateKey` on an Ed25519 or RSA issuer key it fell through to the default branch and returned `unsupported key type %T`, which the CA initialiser surfaced as a fatal error.\r\n\r\n### Solution\r\n\r\n`dapr/kit`'s `EncodePrivateKey` now matches `ed25519.PrivateKey` (value form) and `*rsa.PrivateKey` alongside `*ecdsa.PrivateKey`. All three round-trip through PKCS#8 unchanged.\r\nDapr 1.16.15 picks up this fix by bumping `github.com/dapr/kit` to `v0.16.3`, which also includes table-driven roundtrip tests for ECDSA P-256, RSA-2048, and Ed25519 to guard the 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Dapr 1.18\r\n\r\nWe're excited to announce the release of Dapr 1.18! This is primarily a Workflows release, focused on security, durability, and scale. Workflow history can now optionally be cryptographically signed and verified on every state load, so tampering is caught the moment state is read. The same protection extends across application boundaries: child workflow and activity completions are attested between apps. A new `WorkflowAccessPolicy` resource controls which application IDs may invoke which workflows and activities. Workflows can also propagate slices of their history to the children they schedule, letting a child act on upstream context instead of reading it back from a state store. And the Scheduler gained global and per-name concurrency limits that cap how many workflows or activities run at once across the cluster.\r\n\r\nOutside of Workflows, the Jobs API graduates to stable, hot-reloading of components and other resources is now GA and on by default, and the sidecar injector understands Kubernetes native sidecar containers. This release also introduces a new `MCPServer` resource that exposes Model Context Protocol tool calls as durable Dapr Workflows. Several changes make the control plane steadier under load: an opt-in coalesce window collapses redundant placement dissemination rounds during large scale-ups, placement and scheduler support hard anti-affinity, host resolution now works over IPv6 and dual-stack, and pub/sub drains in-flight messages on graceful shutdown instead of dropping them.\r\n\r\nBefore you upgrade, note one change in Sentry. It now generates workload identity keys using Ed25519 instead of ECDSA. This is invisible on a fresh install or a normal upgrade, but it means you cannot safely roll back below 1.17.7. See the new [Downgrading](#downgrading-to-earlier-versions) section for the safe rollback path.\r\n\r\nThanks to all the new and existing contributors who helped make this release happen.\r\n\r\nIf you're new to Dapr, visit the [getting started](https://docs.dapr.io/getting-started/) page and familiarize yourself with Dapr.\r\n\r\nThe docs have been updated with all the new features and changes of this release. To get started with new capabilities introduced in this release, go to the [Concepts](https://docs.dapr.io/concepts/) and the [Developing applications](https://docs.dapr.io/developing-applications/).\r\n\r\n> **Note: This release contains a few [breaking changes](#breaking-changes).**\r\n\r\nGo to the [upgrading Dapr](#upgrading-to-dapr-118) section for steps to upgrade to version 1.18.\r\n\r\n\r\n## Acknowledgements\r\n\r\nThanks to everyone who made this release possible!\r\n\r\n@1Ninad, @acroca, @alekhrycaiko, @antontroshin, @artur-ciocanu, @aviralgarg05, @bibryam, @CasperGN, @cerebrixos, @cicoyle, @Copilot, @cyphercodes, @Eckii24, @erj826, @erwinkramer, @famarting, @gaganhr94, @imneov, @ja-sg, @javier-aliaga, @Jeevansm25, @jjcollinge, @jmfloreszazo, @JoshVanL, @lrascao, @marcduiker, @matheusandre1, @MichaelHindley, @middt, @mikeee, @mohitpalsingh, @MukundaKatta, @MyMirelHub, @nelson-parente, @nikitasarawgi, @officialasishkumar, @olitomlinson, @pablochacin, @Pittu-Sharma, @pravinpushkar, @Rishabh-git10, @salaboy, @seherv, @sicoyle, @siri-varma, @spolom, @stywzn, @vaibhavatlan, @WhitWaldo, @yaron2, @ZeynelKoca\r\n\r\n\r\n## Highlights\r\n\r\nThese are the v1.18 release highlights:\r\n\r\n### WorkflowAccessPolicy\r\n\r\n`WorkflowAccessPolicy` is a new Kubernetes CRD that controls which Dapr app IDs are allowed to operate on which workflows running on a target app. Before v1.18, any caller in the same trust domain could schedule, terminate, or query any other app's workflows. That was acceptable for single-team clusters but a real problem in shared or multi-tenant ones, especially now that workflows can branch into remote apps where unrestricted cross-app calls are a security risk.\r\n\r\nThe policy is a **pure allow-list**:\r\n\r\n- With **no** policy loaded for a target app, every call is allowed — open by default, so existing deployments are unaffected.\r\n- With **one or more** policies loaded, the target defaults to deny, and only calls matching a rule are permitted.\r\n- **Self-calls** (where the caller's app ID equals the target's) are always allowed and need no rule.\r\n\r\nCaller identity comes from the SPIFFE identity in the mTLS certificate for remote calls, and from the local app ID for same-sidecar calls. Untrusted identity headers on user-facing requests are stripped before evaluation, so one app cannot spoof another's identity.\r\n\r\nRules are expressed per operation. Workflows support all eight operations — `schedule`, `terminate`, `raise`, `pause`, `resume`, `purge`, `get`, and `rerun` — while activities support `schedule` only. Names can be exact or glob patterns (`*`, `?`, `[abc]`).\r\n\r\nExample:\r\n\r\n```yaml\r\napiVersion: dapr.io/v1alpha1\r\nkind: WorkflowAccessPolicy\r\nmetadata:\r\n  name: orders-policy\r\n  namespace: production\r\nscopes:\r\n  # the target apps that load and enforce this policy\r\n  - orders-target\r\nspec:\r\n  rules:\r\n    # Frontend and ops-console can schedule and terminate OrderWF.\r\n    - callers:\r\n        - appID: frontend\r\n        - appID: ops-console\r\n      workflows:\r\n        - name: OrderWF\r\n          operations: [schedule, terminate]\r\n\r\n    # Frontend can also schedule the ChargePayment activity directly.\r\n    - callers:\r\n        - appID: frontend\r\n      activities:\r\n        - name: ChargePayment\r\n        - name: \"RefundEvent*\"\r\n```\r\n\r\nWhen a call is denied, the caller receives an opaque error (`access denied by workflow access policy`) that doesn't reveal which rule matched or what the policy would otherwise allow.\r\n\r\nFor configuration and examples, see [How-To: Apply workflow access policies](https://v1-18.docs.dapr.io/operations/security/workflow-access-policy/) and the [`WorkflowAccessPolicy` spec](https://v1-18.docs.dapr.io/reference/resource-specs/workflow-access-policy-schema/). For background, see [Workflow access control](https://v1-18.docs.dapr.io/concepts/security-concept/#workflow-access-control) in the security concepts.\r\n\r\n### Workflow History Tamper Detection\r\n\r\nWorkflow history events can now be optionally cryptographically signed and verified. Each step's signature is produced under the sidecar's mTLS X.509 SPIFFE identity and chained to the previous one, creating an auditable signature chain that is checked on every state load.\r\n\r\nEach workflow execution step produces a batch of history events that are signed and chained to the previous signature:\r\n\r\n```\r\n  ┌─────────────────────────────────────────────────────────────┐\r\n  │                    Workflow History                         │\r\n  │  Event 0 ─ Event 1 ─ Event 2 ─ Event 3 ─ Event 4 ─ Event 5  │\r\n  └─────┬──────────┬─────────┬──────────┬─────────┬──────────┬──┘\r\n        ▼          ▼         ▼          ▼         ▼          ▼\r\n  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐\r\n  │    Signature 0   │  │    Signature 1   │  │    Signature 2   │\r\n  │  Events [0,2)    │  │  Events [2,4)    │  │  Events [4,6)    │\r\n  │  prevDigest: nil ├─►│ prevDigest: d0   ├─►│  prevDigest: d1  │\r\n  └──────────────────┘  └──────────────────┘  └──────────────────┘\r\n```\r\n\r\nOn every state load, the runtime verifies:\r\n\r\n- chain linkage between signatures (`prevDigest`),\r\n- event contiguity (no gaps in the `[start, end)` ranges),\r\n- digest recomputation matches the signed digest,\r\n- the signature itself,\r\n- certificate validity at event time, and\r\n- the signing app's SPIFFE identity matches the workflow's owning app ID.\r\n\r\nIf verification fails, the actor appends a terminal `ExecutionCompleted` event with the well-known error type `DAPR_WORKFLOW_HISTORY_TAMPERED`, **terminates the in-flight workflow**, and leaves the original (untrusted) state intact for forensic inspection.\r\n\r\nTwo additional layers extend the same integrity guarantee across app boundaries:\r\n\r\n- **Child workflow and activity completion attestation** — before a child workflow or activity reports back to its parent, the executor signs a `ChildCompletionAttestation` / `ActivityCompletionAttestation` that commits to the parent instance ID, parent task scheduled ID, a canonical input/output digest (SHA-256 over a wire-format-independent, NFC-normalized encoding so the digest is stable across language SDKs), the signer's certificate chain digest, and the terminal status. The parent verifies the attestation on inbox ingestion and stores the foreign signer's certificate in the `ext-sigcert` table, keyed by certificate-chain digest.\r\n- **Cryptographic verification of propagated history** — every chunk of [propagated history](#workflow-history-context-propagation) now carries its own chunk-local signature and DER cert chain, produced at dispatch time by the chunk's owning app. Receivers verify each chunk against the Sentry trust anchor and check that the leaf SPIFFE ID's app component matches `chunk.appId`.\r\n\r\nSigning is a **one-way decision**. Once a workflow is created with signing enabled, its history must stay signed — disabling signing for that workflow (or enabling it on one that started unsigned) is a hard verification error, and there is no catch-up signing of pre-existing events. Before turning signing on cluster-wide, let any in-flight unsigned workflows complete or purge them. The feature is gated by the `WorkflowHistorySigning` configuration feature flag, requires mTLS to be active (daprd refuses to start with signing enabled but mTLS off), and is disabled by default in v1.18.\r\n\r\nSee [Workflow history signing](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-history-signing/) for how to enable signing, the verification model, and the cross-app attestation guarantees.\r\n\r\n### Workflow History Context Propagation\r\n\r\nWorkflows can now propagate slices of their history down to child workflows and activities, where the consumer can query it by name.\r\n\r\nTwo opt-in propagation modes are supported:\r\n\r\n- **Lineage** — the full ancestor chain of workflow history is propagated to children.\r\n- **OwnHistory** — only the current workflow's own history is propagated to its immediate children.\r\n\r\nPropagation is per call: the caller decides whether — and what — to propagate when scheduling a child workflow or activity, and the consumer reads it back through a typed query API (for example, fetching a previously-completed activity's result by workflow name and activity name). This avoids threading state through inputs or reaching into a state store, the latter of which would break workflow determinism.\r\n\r\nSee [Workflow history propagation](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-history-propagation/) for the `Lineage` and `OwnHistory` modes and the typed query API.\r\n\r\n\r\n### Workflow Concurrency Limits\r\n\r\nWorkflow and activity concurrency can now be capped at the scheduler, across all sidecar replicas, alongside the existing per-sidecar limits. You can set a global maximum across all workflows or activities, plus per-name limits for specific workflow or activity names.\r\n\r\nWhen a limit is hit, the scheduler holds triggers in a pending queue and dispatches them as slots free up via the existing ACK protocol. Global limits are sharded deterministically across scheduler instances: each replica enforces an even share of the limit, with the remainder handed to the lowest-indexed replicas so the cluster-wide total matches what you configured. Per-name limits run as independent gates, and a trigger must pass every applicable gate before it dispatches. There is one edge case: if a global limit is set lower than the number of scheduler replicas, each replica still allows one concurrent invocation to avoid starvation, so the effective cap can exceed the configured value.\r\n\r\n```yaml\r\nspec:\r\n  workflow:\r\n    globalMaxConcurrentWorkflowInvocations: 50\r\n    globalMaxConcurrentActivityInvocations: 200\r\n    activityConcurrencyLimits:\r\n      - name: SendEmail\r\n        maxConcurrent: 5\r\n    workflowConcurrencyLimits:\r\n      - name: OrderProcess\r\n        maxConcurrent: 20\r\n```\r\n\r\nSee [Workflow concurrency limits](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-concurrency/) for how to configure the global and per-name limits.\r\n\r\n\r\n### Graceful Stall on Oversized Workflow Payloads\r\n\r\nPreviously, a workflow whose history payload (`PastEvents + NewEvents + PropagatedHistory`) exceeded the Dapr API gRPC server's `MaxSendMsgSize` (= `--max-body-size`, default 4 MiB) caused `stream.Send` on the `GetWorkItems` server stream to return `ResourceExhausted`. The whole stream was torn down, cancelling every other workflow pending on it. The SDK would reconnect, and the same offending workflow would fail again, in a loop.\r\n\r\nIn v1.18, the orchestrator **precomputes the proto size** of the dispatch a workflow (or activity) would produce and compares it against 95% of `--max-body-size`. The 5% headroom covers the `WorkflowStarted` event the engine injects plus gRPC framing. Anything over the threshold is **gracefully stalled** in a recoverable state, leaving the rest of the stream untouched. The runtime emits the payload size as a ratio of the limit so operators can watch the distribution, and a stalled instance resumes once you raise `--max-body-size` and restart daprd. No instance is lost.\r\n\r\n\r\nSee [Workflow payload size limits](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-payload-size/) for the stall behavior, the payload-size ratio metric, and how to recover a stalled instance.\r\n\r\n### Workflow External Event Timers\r\n\r\nA `WaitForExternalEvent` wait with no timeout used to be invisible to the runtime: from the outside, there was no way to tell what an instance was blocked on. In v1.18 such an indefinite wait materialises a synthetic timer (with a fire-at far enough in the future that it never actually fires), tagged with an `origin` that records the name of the event being awaited. With the existing `RaiseEvent` API, this makes approval gates and other human-in-the-loop workflows operable. The same timer-origin field also distinguishes timers created as activity and child-workflow retry delays, so every timer in a workflow's history now records why it was created.\r\n\r\nSee [External events](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-features-concepts/#external-events) and the [external system interaction pattern](https://v1-18.docs.dapr.io/developing-applications/building-blocks/workflow/workflow-patterns/#external-system-interaction) for how to author approval gates and other human-in-the-loop workflows using `WaitForExternalEvent` and `RaiseEvent`.\r\n\r\n\r\n### Jobs API is now Stable\r\n\r\nThe [Jobs API](https://v1-18.docs.dapr.io/developing-applications/building-blocks/jobs/) graduates to stable in v1.18. Performance regression tests were added as part of the stabilization work to guard against future regressions.\r\n\r\nYou should move application code to the stable `ScheduleJob` / `GetJob` / `DeleteJob` endpoints and the `OnJobEvent` callback. The alpha RPCs (`ScheduleJobAlpha1` and friends) are now deprecated but remain fully functional, and the transition needs no coordination: a 1.18 sidecar still serves the alpha RPCs for older clients, and updated SDK clients fall back to the alpha RPCs automatically when they detect an older sidecar. You can upgrade the runtime and migrate application code separately.\r\n\r\n\r\n### HotReload is now GA\r\n\r\nConfiguration and component [hot-reloading](https://v1-18.docs.dapr.io/operations/configuration/hotreload/) graduates to GA and is **on by default** in v1.18. The following resources are hot-reloadable without restarting the sidecar:\r\n\r\n- Components\r\n- Subscriptions\r\n- MCPServers\r\n- Configurations\r\n- HTTPEndpoints\r\n- Resiliencies\r\n- WorkflowAccessPolicies\r\n\r\nThe Subscription reconciler now skips the close+reopen when an incoming `Subscription` event is functionally identical to the one already loaded. This fixes a startup race where the boot-time loader and the operator stream both delivered the same subscription, which previously caused transient subscriber overlap and occasional duplicated or lost messages.\r\n\r\nOperators who want the pre-1.18 behavior can opt out by disabling the `HotReload` feature in the Dapr `Configuration` — add it to `spec.features` with `enabled: false`.\r\n\r\n### MCPServer Resource — Built-in Workflow Orchestration for MCP\r\n\r\nA new `MCPServer` Kubernetes CRD, plus a built-in workflow orchestration engine, exposes Model Context Protocol (MCP) servers through the standard Dapr Workflow API.\r\n\r\nWhen a sidecar loads one or more `MCPServer` resources, daprd registers built-in `dapr.internal.mcp.<server>.ListTools` and `dapr.internal.mcp.<server>.CallTool.<toolName>` workflow orchestrations. These execute MCP protocol calls as durable activities, so application code doesn't need to import an MCP SDK, manage connections, or handle credentials. The runtime handles all of that, with the same durability guarantees as any Dapr workflow.\r\n\r\n- **Transports:** `streamable_http`, `sse`, `stdio`.\r\n- **Auth:** static header injection, OAuth2 client credentials (secret fetched at call time from Dapr secret stores with retryable error handling), and SPIFFE JWT SVID injection.\r\n- **Spec-compliant**: both ListTools and CallTool follow the MCP specification. ListTools returns each tool's name, description, and inputSchema exactly as the server advertises them, without flattening, renaming, or rewriting the schema. CallTool preserves the full CallToolResult shape, including the content union (text, image with base64 + mimeType, audio with base64 + mimeType, resource_link with URI and metadata, and embedded resource with inlined text or blob), the isError flag, and any server-provided metadata. JSON-RPC envelopes pass through unmodified. Unknown or future content types fall back to a structured JSON envelope rather than being dropped, so the path stays compatible with new MCP revisions.\r\n- **Lifecycle hooks:** `beforeCallTool`, `afterCallTool`, `beforeListTools`, `afterListTools`, each able to mutate arguments / results.\r\n- **Only loaded when used:** the MCP executor is wired in only when at least one `MCPServer` resource is loaded, so sidecars with no MCP resources incur no MCP-related allocations.\r\n\r\nThe preview feature gate was removed before GA; loading the CRD is now the only opt-in ([#9838](https://github.com/dapr/dapr/pull/9838)).\r\n\r\n\r\n\r\n### Placement: Coalesce Window + Stable Scheduler Reload\r\n\r\nTwo improvements to placement dissemination behavior at scale:\r\n\r\n- **Stable scheduler reload (always on).** The actor-type list returned by `actorTable.Types()` is built by ranging a map, so its order was non-deterministic. `scheduler.ReloadActorTypes` compares that list against the last-seen one to decide whether to reconnect, and the reordering alone made it look changed — triggering spurious reconnects on rounds where nothing actually had. It now sorts the list and skips the reconnect when the sorted set is unchanged.\r\n- **Post-round coalesce window (opt-in).** A new `--disseminate-coalesce-window` flag (default `0` = disabled). When set, after a dissemination round completes with queued host registers/disconnects, the disseminator arms a one-shot timer instead of starting the next round immediately. Additional host events arriving inside the window accumulate and fold into a single follow-up round. The first event in an otherwise idle state still fires a round immediately, so cold-starts and isolated churn stay responsive; only sustained bursts pay the (configurable) latency to save rounds.\r\n\r\nFor 100+-replica scale-ups or rolling restarts, the coalesce window collapses what would have been O(N) follow-up rounds into O(1), removing N-1 rounds of drain+halt cost from every existing daprd in the namespace. Recommended values are 100–250 ms for clusters with frequent rapid churn. Default 0 preserves the existing behavior.\r\n\r\n\r\n### Placement & Scheduler: Hard Anti-Affinity\r\n\r\nA configurable HA pod anti-affinity policy for the **placement and scheduler** StatefulSets in the Helm chart:\r\n\r\n- `preferredDuringSchedulingIgnoredDuringExecution` (default): soft anti-affinity; the Kubernetes scheduler tries to spread replicas but can still place pods when capacity is limited.\r\n- `requiredDuringSchedulingIgnoredDuringExecution`: hard anti-affinity; enforces replica separation and may leave pods pending if constraints cannot be satisfied.\r\n\r\nConfigured via `global.ha.podAntiAffinityPolicy`, and combinable with `global.ha.topologyKey` to choose the spread domain (e.g. `topology.kubernetes.io/zone`, `kubernetes.io/hostname`). The other control-plane components (operator, sentry, injector) continue to use the soft policy.\r\n\r\nSee [Spreading Placement and Scheduler replicas](https://v1-18.docs.dapr.io/operations/hosting/kubernetes/kubernetes-production/#spreading-placement-and-scheduler-replicas) in the production guidelines for how to configure `global.ha.podAntiAffinityPolicy` and `global.ha.topologyKey`.\r\n\r\n### Actor APIs: App-Initiated gRPC Streams\r\n\r\nActor hosts (the application side) can now open a single bidirectional gRPC stream to daprd via `SubscribeActorEventsAlpha1` and receive all four callback types — invoke, reminder, timer, and deactivate — over that one connection. Apps no longer need to expose an HTTP or gRPC server port for daprd to call into; the connection flows app → sidecar, mirroring how pubsub subscriptions, configuration watch, and scheduler job streams already work.\r\n\r\nIt also makes actor hosts deployable behind tighter firewall and NetworkPolicy rules, since there's no inbound port to allow.\r\n\r\n\r\n### Sidecar Probe Defaults Tuned for Production\r\n\r\nLiveness probes are only appropriate as a last-resort restart mechanism for software you don't own. For processes Dapr owns, short liveness windows turn transient blips (GC pauses, startup work, brief stalls) into restart loops. Readiness is the correct lever for routing traffic away from an unhealthy replica.\r\n\r\nv1.18 pushes liveness well past any plausible real-world stall (~230 s before kubelet restart) while making readiness more responsive:\r\n\r\n```\r\nLiveness (control plane + injected sidecar):\r\n  initialDelaySeconds: 180\r\n  periodSeconds:        10\r\n  failureThreshold:      5     (~230s before restart)\r\n\r\nReadiness (control plane, few replicas — eject fast on failure):\r\n  initialDelaySeconds: 1\r\n  periodSeconds:       1\r\n  failureThreshold:    3       (~3s to eject)\r\n\r\nReadiness (daprd sidecar, more forgiving for user workloads):\r\n  initialDelaySeconds: 1\r\n  periodSeconds:       1\r\n  timeoutSeconds:      1\r\n  failureThreshold:    5       (~5s to eject; absorbs brief GC / CPU stalls)\r\n```\r\n\r\nSee [Sidecar health](https://v1-18.docs.dapr.io/operations/resiliency/health-checks/sidecar-health/) for the probe defaults, and the [arguments and annotations reference](https://v1-18.docs.dapr.io/reference/arguments-annotations-overview/) for the `dapr.io/sidecar-liveness-probe-*` and `dapr.io/sidecar-readiness-probe-*` annotations used to override them.\r\n\r\n### Sentry: Ed25519 Workload Identity Keys\r\n\r\nSentry and workloads now generate X.509 certificate keys using Ed25519 instead of ECDSA P-256 for all workload identity. The migration is invisible on a fresh install or a normal upgrade.\r\n\r\n- **Root CA and issuer CA** keys are now Ed25519; the X.509 signature algorithm changes from `ECDSAWithSHA256` to `PureEd25519`.\r\n- **Sentry's CA accepts CSRs of any supported algorithm** (Ed25519, RSA, ECDSA), so mixed-version clusters during a rolling upgrade are fully supported — a 1.17.x daprd presenting an ECDSA CSR is still signed by a 1.18 sentry.\r\n- **JWT signing remains RSA-2048** for cloud provider OIDC compatibility.\r\n- **Injector and operator** continue using RSA keys for their *webhook serving* certificates ([#9873](https://github.com/dapr/dapr/pull/9873)), because some managed Kubernetes distributions reject Ed25519 on admission webhooks.\r\n\r\n> ⚠ **Downgrade implications.** Sentry versions before **1.17.7** cannot parse the Ed25519-keyed trust bundle that 1.18 writes to the `dapr-trust-bundle` secret and will crash on startup with `unsupported key type ed25519.PrivateKey`. See the new [Downgrading to Earlier Versions](#downgrading-to-earlier-versions) section for the safe rollback path. Dapr 1.17.7 includes the [dapr/kit](https://github.com/dapr/kit) PEM-decoder fix ([dapr/dapr#9904](https://github.com/dapr/dapr/pull/9904)) that makes 1.17.x forward-compatible with 1.18-issued bundles.\r\n\r\nSee [Workload identity key algorithm](https://v1-18.docs.dapr.io/concepts/security-concept/#workload-identity-key-algorithm) in the security concepts for the full rationale, compatibility matrix, FIPS guidance, and the 1.17.7 downgrade floor. The operational view is in [mTLS](https://v1-18.docs.dapr.io/operations/security/mtls/#workload-identity-key-algorithm).\r\n\r\n\r\n### Component Improvements\r\n\r\nNotable component highlights this cycle:\r\n\r\n- **Configuration stores**: New ConfigMap configuration store with both get and subscribe options (#4275) — for Dapr Agents this is valuable to hot-reload operator-led configurations; PostgreSQL fix to read pgNotifyChannel from component spec instead of requiring client code to pass it (#4241); Azure App Configuration surfaces contentType and label in metadata (#4338).\r\n- **Pulsar pubsub**: listenerName metadata field for advertisedListeners support, Avro schema registration on the CloudEvents envelope, JSON-payload validation against schema, Avro payload decoding on the subscribe path, and processMode async backpressure (#4244, #4266, #4302, #4305, #4309, #4386, and immediate-bulk-subscribe #4364).\r\n- **Kafka pubsub**: PausableSubscriber interface and graceful shutdown hardening (#4366), per-message partition support (#4298), bulk-flush ticker reset (#4378).\r\n- **RabbitMQ pubsub**: unique consumer tags to prevent subscription disruption on restart (#4326).\r\n- **State stores**: SQL Server native batched BulkGet for v2 (#4365), Oracle BulkGet chunking (#4341), MongoDB KeysLiker (#4278), CockroachDB image bump (#4291), RavenDB state store registered in the runtime (dapr/dapr#9654).\r\n- **Bindings**: Azure Blob Storage presign operation + bulk operations (#4299, #4295); AWS S3 bulk file transfer (#4292).\r\n- **Conversation API**: Anthropic on Microsoft Foundry (#4374), LLM model surfaced on response (#4331), error returned when required tool calls weren't made (#4277), Ollama endpoint metadata field (#4314).\r\n- **TLS hardening**: redis common no longer unconditionally skips TLS cert verification (#4261) — see changes below.\r\n\r\nSee the [full per-repo PR list below](#list-of-issues-addressed-in-this-release) for the rest.\r\n\r\n\r\n## SDK Improvements\r\n\r\n### Go SDK\r\n\r\nThe marquee feature is **workflow history context propagation** ([#823](https://github.com/dapr/go-sdk/pull/823)): a workflow can hand a slice of its execution history to the child workflows and activities it schedules, instead of threading that state through inputs or a state store. Propagation is opt-in per call. A producer passes `workflow.WithHistoryPropagation(workflow.PropagateLineage())` (full ancestor chain) or `…PropagateOwnHistory()` (own events only) to `ctx.CallChildWorkflow(...)` / `ctx.CallActivity(...)`, and the consumer reads back through a typed API: `ctx.GetPropagatedHistory()` returns a `*workflow.PropagatedHistory`, then `GetLastWorkflowByName(...).GetLastActivityByName(...)` retrieves a completed result by name. A runnable [`workflow-history-propagation` example](https://github.com/dapr/go-sdk/tree/main/examples/workflow-history-propagation) ships with the SDK.\r\n\r\nThe **Jobs API graduates to stable** ([#824](https://github.com/dapr/go-sdk/pull/824)). `ScheduleJob`, `GetJob`, and `DeleteJob` call the stable RPCs and fall back to alpha against older sidecars, so one SDK build works on both 1.17 and 1.18 with no code change. The `*Alpha1` client methods are deprecated and now delegate to the stable ones; the service callback is now `OnJobEvent`, with `OnJobEventAlpha1` kept for older sidecars.\r\n\r\nBeyond those two, this release fixes gRPC endpoint parsing when a connection string is supplied without a DNS scheme, and aligns dependencies and security baselines: durabletask-go is bumped to `v0.12.1`, the runtime protos to `v1.18.0-rc.4`, and gRPC is updated to address CVE-2026-33186.\r\n\r\nVersion mapping: Go SDK **v1.15** ships with Dapr 1.18.\r\n\r\n\r\n### .NET SDK\r\nSince the v1.17.0 release of the Dapr runtime, the .NET SDK has continued to modularize around individual building blocks, introducing two new clients in their own NuGet packages: `Dapr.SecretsManagement` and `Dapr.StateManagement` for secret and state management operations, respectively. Beyond simply yielding smaller, more focused dependencies, splitting these clients out of `DaprClient` lets each package adopt more modern .NET practices and ship tooling (such as source generators, analyzers and code-fixes) tailored to its building block rather than being constrained by a single monolithic client.\r\n\r\nThis release also consolidates the `Dapr.Workflow` package. The separate `Dapr.Workflow.Versioning` and `Dapr.Workflow.Analyzers` packages are no longer required - both are now bundled into `Dapr.Workflow` itself. No refactoring is necessary, but you should remove the now-stale package references pointing to the since-deprecated packages.\r\n\r\nBuilding on the automatic workflow registration introduced in v1.17, registration is now handled for both workflows and activities, meaning neither needs to be registered with dependency injection at startup as the SDK takes care of it for you. There is no need to immediately clear out your existing DI registration block when upgrading to 1.18, as the SDK will gracefully continue to accept manual and duplicate registrations, but as of this release such registration is entirely unnecessary.\r\n\r\nFinally, this release ships several new analyzers that validate type consistency between workflow and activity inputs and outputs, surfacing mismatches at build time rather than at runtime.\r\n\r\n\r\n### Python SDK\r\n\r\nThis release of the Python SDK folds the durabletask-python project into the main SDK, so workflow primitives now ship in a single package with version alignment to the Dapr runtime. The MCP surface is now exposed through a dedicated workflow client, letting workflows orchestrate MCP tool calls without wiring up a separate MCP SDK.\r\n\r\nWorkflow history context propagation is available on both child workflows and activities, with opt-in scopes for the caller's own history or the full lineage; consumers retrieve the propagated history through a typed query API. Workflows and activities also accept Pydantic models as inputs, removing the previous need to hand-roll conversions.\r\n\r\nThis release also addresses several rough edges: the workflow gRPC connection now honors the standard API timeout environment variable, the default 60-second drain timeout has been removed, the maximum inbound gRPC message size is configurable via an environment variable, and workers wait indefinitely for the runtime to be ready instead of failing fast.\r\n\r\nBulk pub/sub is now supported, Python 3.14 is supported (with the last Python 3.9 vestiges removed), and the Jobs API graduates to stable alongside the runtime. The package layout has been simplified — unnecessary dev packages removed and version metadata centralized into a single file.\r\n\r\n\r\n### Java SDK\r\n\r\nThe most disruptive changes are at the build level. The **minimum Java version is now 17** ([#1721](https://github.com/dapr/java-sdk/pull/1721)); Java 11 is no longer supported. The **Spring Boot baseline moves from 3.4 to 3.5** (the root `springboot.version` goes from `3.4.13` to `3.5.12`), with **Spring Boot 4 supported in parallel** so projects on either line are covered. The SDK also now publishes a **`dapr-sdk-bom`** artifact ([#1722](https://github.com/dapr/java-sdk/pull/1722)) you can import to keep every Dapr dependency on a single, consistent version.\r\n\r\nOn the workflow side, the headline is **history context propagation** ([#1739](https://github.com/dapr/java-sdk/pull/1739)), matching the other SDKs. A producer opts in per call with `WorkflowTaskOptions.withHistoryPropagation(scope)` (or the `propagateLineage()` / `propagateOwnHistory()` shortcuts) on child workflow and activity calls, where `scope` is a `HistoryPropagationScope` of `LINEAGE` or `OWN_HISTORY`. Consumers read back through `WorkflowContext.getPropagatedHistory()` and `WorkflowActivityContext.getPropagatedHistory()`, which return an `Optional<PropagatedHistory>` with typed lookups (by workflow name, app ID, or instance ID, then per-workflow drill-down to the last activity or child workflow). Workflows also gain a **timer origin** field ([#1733](https://github.com/dapr/java-sdk/pull/1733)) so external-event waits are visible from outside, and `DurableTaskGrpcWorker` now **shuts down cleanly** by breaking out of the gRPC worker loop instead of hanging ([#1727](https://github.com/dapr/java-sdk/pull/1727)).\r\n\r\nFor service invocation, the new **`DaprClient.invokeHttpClient(appId)`** factory ([#1742](https://github.com/dapr/java-sdk/pull/1742)) returns an SDK-native HTTP client preconfigured to call a target app, mirroring .NET's `CreateInvokeHttpClient`. It is the recommended successor to the now-deprecated `invokeMethod` APIs. Streaming subscriptions gain **dead-letter topic support** ([#1746](https://github.com/dapr/java-sdk/pull/1746)): `subscribeToTopic` now takes a `deadLetterTopic` to which failed messages are forwarded. There is also **baggage support** ([#1659](https://github.com/dapr/java-sdk/pull/1659)) and **trace propagation in local observations** ([#1724](https://github.com/dapr/java-sdk/pull/1724)).\r\n\r\nOn the testing side, the SDK **migrated from Testcontainers 1.21.4 to 2.0.5 and from JUnit 4 to JUnit 5** ([#1736](https://github.com/dapr/java-sdk/pull/1736)).\r\n\r\n\r\n### JS SDK\r\nThis release is mostly about setting the groundwork to catch the SDK up to features recently introduced in other packages. Going forward, package versioning will indefinitely remain at major version 3, the minor version will track the Dapr runtime release and the patch value will reflect any mid-cycle SDK releases that don't have new runtime dependencies. Thus, as the 1.18 Dapr runtime is being release, this package releases as v3.18.0.\r\n\r\nIn the prior 3.17.0 release, CommonJS modules were inadvertently discontinued in favor of ESM as a side effect of overhauling the gRPC generators. This module-packaging change has been reverted and we have no plans to deprecate our reliance on CommonJS. Should that ever change, it will be advertised via these release notes well in advance.\r\n\r\nThe [dapr-client](https://www.npmjs.com/package/dapr-client) package on NPM has been marked as deprecated for a while, but was still receiving updates, but version 3.6.1 will be the last release on that package. All updates going forward will be applied to [@dapr/dapr](https://www.npmjs.com/package/@dapr/dapr) going forward.\r\n\r\nWorkflow client methods have been renamed for nomenclature consistency with the other SDKs (e.g. \"orchestrators\" -> \"workflows\"). This is not a hard break for now. The old names remain as '@deprecated' aliases and will continue to work, with removal scheduled for the release of Dapr 1.20, so you can migrate at your own pace.\r\n\r\n### Rust SDK\r\n\r\nWith version v0.19.0 of the Rust SDK significant changes have been made towards general stability and alignment with the other SDKs including the initialisation of the client using EnvVars and also by arguments.\r\n\r\nAs of this release, Workflows support has been added bringing almost complete support for all the Dapr APIs/Building Blocks. Siginificant changes are expected to follow after more feedback to ensure that the developer experience is improved.\r\n\r\n\r\n\r\n#### Breaking Changes / Deprecations\r\n\r\nThis SDK is still in alpha and with each release, there are usually breaking changes. In this release the MSRV (Minimum Supported Rust Version) has been bumped to 1.88 using Edition 2024.\r\n\r\nPlease migrate to the new client implementation as documented in the [examples](https://github.com/dapr/rust-sdk/tree/main/examples/src/client-config).\r\n\r\n\r\n\r\n## List of issues addressed in this release\r\n\r\n### Dapr Runtime\r\n- **ADDED** Implement actor APIs for gRPC [927](https://github.com/dapr/dapr/issues/927)\r\n- **RESOLVED** add replace to go.mod [8868](https://github.com/dapr/dapr/pull/8868)\r\n- **ADDED** Support for Spread Constraints and strict affinity rules in Dapr Control Plane charts to ensure Quorum resilience. [9223](https://github.com/dapr/dapr/issues/9223)\r\n- **RESOLVED** Promote Dapr Jobs API to Stable [9257](https://github.com/dapr/dapr/issues/9257)\r\n- **RESOLVED** Version Skew: 1.16-1.17 [9315](https://github.com/dapr/dapr/pull/9315)\r\n- **RESOLVED** Doc: link to security tab [9356](https://github.com/dapr/dapr/pull/9356)\r\n- **FIXED** feat: improve output message of ErrActorNoAddress by adding req.ActorKey() [9360](https://github.com/dapr/dapr/pull/9360)\r\n- **ADDED** Sentry: Support Per Audience Token SVID [9381](https://github.com/dapr/dapr/pull/9381)\r\n- **RESOLVED** Bump actions/checkout from 3 to 6 [9418](https://github.com/dapr/dapr/pull/9418)\r\n- **ADDED** Support for Kubernetes native sidecar containers [9422](https://github.com/dapr/dapr/issues/9422)\r\n- **RESOLVED** Constrain loop event types with scoped sealed interfaces [9423](https://github.com/dapr/dapr/pull/9423)\r\n- **ADDED** Sidecar Injector supports Native Sidecars [9450](https://github.com/dapr/dapr/pull/9450)\r\n- **RESOLVED** Bump github.com/lestrrat-go/jwx/v2 from 2.0.21 to 2.1.6 [9454](https://github.com/dapr/dapr/pull/9454)\r\n- **RESOLVED** Feature Gate: WorkflowsRemoteActivityReminder [9456](https://github.com/dapr/dapr/pull/9456)\r\n- **RESOLVED** components-contrib: fix redis no key [9476](https://github.com/dapr/dapr/pull/9476)\r\n- **RESOLVED** Fix pubsub e2e flake [9478](https://github.com/dapr/dapr/pull/9478)\r\n- **RESOLVED** diagridio/go-etcd-cron: v0.12.3 [9489](https://github.com/dapr/dapr/pull/9489)\r\n- **RESOLVED** Point backport action to a specific commit [9491](https://github.com/dapr/dapr/pull/9491)\r\n- **RESOLVED** use latest from contrib [9496](https://github.com/dapr/dapr/pull/9496)\r\n- **ADDED** [1.18] Add hard anti-affinity policy option for placement and scheduler [9498](https://github.com/dapr/dapr/pull/9498)\r\n- **RESOLVED** fix(ollama): rm old workaround for ollama [9499](https://github.com/dapr/dapr/pull/9499)\r\n- **RESOLVED** Cherrypick in 1.17 perf charts [9501](https://github.com/dapr/dapr/pull/9501)\r\n- **RESOLVED** [FORWARD_PORT] :secretRef for configuration otel headers (#9399) [9523](https://github.com/dapr/dapr/pull/9523)\r\n- **RESOLVED** Add .github/copilot-instructions.md for Copilot coding agent onboarding [9534](https://github.com/dapr/dapr/pull/9534)\r\n- **RESOLVED** Integration: Breakup resiliency app tests [9544](https://github.com/dapr/dapr/pull/9544)\r\n- **RESOLVED** chore: Use dapr-bot to create backport PRs [9547](https://github.com/dapr/dapr/pull/9547)\r\n- **RESOLVED** make pubsub_azure_servicebus_queues stable [9550](https://github.com/dapr/dapr/pull/9550)\r\n- **RESOLVED** Integration: basic termination test [9557](https://github.com/dapr/dapr/pull/9557)\r\n- **RESOLVED** Update kit & contrib v17 [9568](https://github.com/dapr/dapr/pull/9568)\r\n- **REFACTOR** Operator: refactor update streams to loops [9570](https://github.com/dapr/dapr/pull/9570)\r\n- **RESOLVED** fix(ci): fix gh api command in update-longhauls workflow [9571](https://github.com/dapr/dapr/pull/9571)\r\n- **RESOLVED** Bump Go to 1.26.0 and fix golangci-lint modernize/prealloc failures [9573](https://github.com/dapr/dapr/pull/9573)\r\n- **REFACTOR** hotreload: simplify disk loader triggers and move reconciler to event loop [9574](https://github.com/dapr/dapr/pull/9574)\r\n- **UPDATED** Component hot reloading is now generally available [9577](https://github.com/dapr/dapr/issues/9577)\r\n- **ADDED** daprds: Adds SIGHUP handler reloader [9579](https://github.com/dapr/dapr/pull/9579)\r\n- **RESOLVED** Integration: build stablecomponents [9580](https://github.com/dapr/dapr/pull/9580)\r\n- **ADDED** hotreload: Configuration, HTTPEndpoint, and Resiliency [9582](https://github.com/dapr/dapr/pull/9582)\r\n- **RESOLVED** Stalled workflow retention deletion [9588](https://github.com/dapr/dapr/pull/9588)\r\n- **FIXED** Unnecessary placement dissemination for daprd hosts with no actor types [9591](https://github.com/dapr/dapr/pull/9591)\r\n- **ADDED** Allow wildcards for service account names [9596](https://github.com/dapr/dapr/issues/9596)\r\n- **ADDED** Sentry: Use Ed25519 for X.509 certificate key generation [9598](https://github.com/dapr/dapr/pull/9598)\r\n- **RESOLVED** Fix flaky TestStartInternalCallbackSpan sampling rate test [9599](https://github.com/dapr/dapr/pull/9599)\r\n- **RESOLVED** release notes added Reset bulk subscribe timer [9603](https://github.com/dapr/dapr/pull/9603)\r\n- **CHORE** Bump Go to 1.26.1 [9608](https://github.com/dapr/dapr/pull/9608)\r\n- **RESOLVED** Fix Broken Perf Test Suite [9609](https://github.com/dapr/dapr/pull/9609)\r\n- **FIXED** fix: Replace deep proto.Clone with shallow copy in invoke method request/response [9610](https://github.com/dapr/dapr/pull/9610)\r\n- **RESOLVED** test: fix flaky retryGRPCProxy test by avoiding codes.Canceled in multi-range scenario [9616](https://github.com/dapr/dapr/pull/9616)\r\n- **FIXED** fix: Race condition causes scheduler jobs to fire multiple times during host reconnection [9617](https://github.com/dapr/dapr/pull/9617)\r\n- **FIXED** fix: Prevent pub/sub messages from being NACKed during graceful shutdown [9619](https://github.com/dapr/dapr/pull/9619)\r\n- **RESOLVED** test: fix flaky actor reminder e2e tests by polling instead of fixed sleep [9620](https://github.com/dapr/dapr/pull/9620)\r\n- **ADDED** feat(helm): Add overrideBroadcastHostPort option to scheduler chart [9629](https://github.com/dapr/dapr/pull/9629)\r\n- **FIXED** ci: fix excessive GitHub workflow token permissions [9631](https://github.com/dapr/dapr/pull/9631)\r\n- **FIXED** Fix actor placement version check [9639](https://github.com/dapr/dapr/pull/9639)\r\n- **RESOLVED** Fix version skew tests [9640](https://github.com/dapr/dapr/pull/9640)\r\n- **RESOLVED** Release notes for #9606 [9646](https://github.com/dapr/dapr/pull/9646)\r\n- **RESOLVED** test: fix flaky placement version assertions after batched dissemination [9648](https://github.com/dapr/dapr/pull/9648)\r\n- **ADDED** feat(comp): register ravendb state component [9654](https://github.com/dapr/dapr/pull/9654)\r\n- **RESOLVED** Add RavenDB state store component to v1.17.2 release notes [9657](https://github.com/dapr/dapr/pull/9657)\r\n- **RESOLVED** Integration: Fix workflow test race [9660](https://github.com/dapr/dapr/pull/9660)\r\n- **RESOLVED** CoPilot: reduce top level review comment [9663](https://github.com/dapr/dapr/pull/9663)\r\n- **ADDED** Added support for timer origins [9665](https://github.com/dapr/dapr/pull/9665)\r\n- **RESOLVED** integration: increase timeouts in flaky integration tests [9671](https://github.com/dapr/dapr/pull/9671)\r\n- **RESOLVED** Use shared workflows and actions [9683](https://github.com/dapr/dapr/pull/9683)\r\n- **ADDED** feat(mcp): add init mcp server resource [9685](https://github.com/dapr/dapr/pull/9685)\r\n- **FIXED** Scheduler fix fatal crash on server restart due to duplicate controller registration [9688](https://github.com/dapr/dapr/pull/9688)\r\n- **FIXED** Fix placement connection and dissemination churn [9689](https://github.com/dapr/dapr/pull/9689)\r\n- **FIXED** Scheduler: fix silent cron exit on quorum change under load [9696](https://github.com/dapr/dapr/pull/9696)\r\n- **RESOLVED** Remove redundant version-skew patch for release-1.17 [9700](https://github.com/dapr/dapr/pull/9700)\r\n- **ADDED** Support glob service accounts [9704](https://github.com/dapr/dapr/pull/9704)\r\n- **RESOLVED** Add --amend and --purge flags to docker manifest commands [9705](https://github.com/dapr/dapr/pull/9705)\r\n- **UPDATED** Jobs API stable [9706](https://github.com/dapr/dapr/pull/9706)\r\n- **TEST** Jobs API stable — perf tests [9711](https://github.com/dapr/dapr/pull/9711)\r\n- **RESOLVED** fix cleanup with peerShared for actors tests [9714](https://github.com/dapr/dapr/pull/9714)\r\n- **TEST** Add e2e test for scheduler quorum recovery after pod kill [9717](https://github.com/dapr/dapr/pull/9717)\r\n- **RESOLVED** [Workflows] Context propagation [9721](https://github.com/dapr/dapr/issues/9721)\r\n- **ADDED** [Workflows] Human in the Loop support [9722](https://github.com/dapr/dapr/issues/9722)\r\n- **RESOLVED** [Workflows] History signing [9723](https://github.com/dapr/dapr/issues/9723)\r\n- **ADDED** MCP Server support for Dapr APIs [9724](https://github.com/dapr/dapr/issues/9724)\r\n- **ADDED** [Workflows] Access policy [9725](https://github.com/dapr/dapr/issues/9725)\r\n- **ADDED** Migrate DurableTask repos into corresponding SDK repos [9726](https://github.com/dapr/dapr/issues/9726)\r\n- **FIXED** Bulk subscribe + sync mode causes high unacked count [9727](https://github.com/dapr/dapr/issues/9727)\r\n- **FIXED** dapr workflow list not working with MongoDB actor state store in 1.17 [9728](https://github.com/dapr/dapr/issues/9728)\r\n- **ADDED** workflow state management improvements (purge, archive) [9730](https://github.com/dapr/dapr/issues/9730)\r\n- **ADDED** feat(mcp): add built-in mcp wf orchestration engine for mcpserver crd [9731](https://github.com/dapr/dapr/pull/9731)\r\n- **RESOLVED** Cherry pick 9718 [9737](https://github.com/dapr/dapr/pull/9737)\r\n- **UPDATED** updates based on durabletask proto changes [9739](https://github.com/dapr/dapr/pull/9739)\r\n- **ADDED** feat(mcp): add crd clientset for cli mcp cmd [9742](https://github.com/dapr/dapr/pull/9742)\r\n- **FIXED** fix(placement): reuse existing hashMap in VirtualNodesCache.setHashes [9753](https://github.com/dapr/dapr/pull/9753)\r\n- **RESOLVED** Fix version-skew for cross-app workflow tests [9756](https://github.com/dapr/dapr/pull/9756)\r\n- **RESOLVED** fix flaky crossactivity workflow integration test [9757](https://github.com/dapr/dapr/pull/9757)\r\n- **FIXED** Tolerate missing CRDs in operator informers [9765](https://github.com/dapr/dapr/pull/9765)\r\n- **RESOLVED** CI: retry transient docker push and crane.Copy failures [9766](https://github.com/dapr/dapr/pull/9766)\r\n- **CHORE** Go: Update to v1.26.2 [9767](https://github.com/dapr/dapr/pull/9767)\r\n- **FIXED** Workflow: Fix carry over events for ContinueAsNew [9769](https://github.com/dapr/dapr/pull/9769)\r\n- **FIXED** Fix daprd sidecar placement dissemination timeout handling [9770](https://github.com/dapr/dapr/pull/9770)\r\n- **TEST** Add tests for retry timers origin field [9771](https://github.com/dapr/dapr/pull/9771)\r\n- **ADDED** Workflow: Adds global and per-name concurrency limits [9775](https://github.com/dapr/dapr/pull/9775)\r\n- **ADDED** Add workflow history signing for tamper detection [9778](https://github.com/dapr/dapr/pull/9778)\r\n- **ADDED** Scheduler: more metrics [9787](https://github.com/dapr/dapr/pull/9787)\r\n- **ADDED** Runtime: adds jitter to scheduler & placement re-conns [9788](https://github.com/dapr/dapr/pull/9788)\r\n- **UPDATED** Workflow: default WorkflowsRemoteActivityReminder true [9789](https://github.com/dapr/dapr/pull/9789)\r\n- **ADDED** Workflows: Adds WorkflowAccessPolicy [9790](https://github.com/dapr/dapr/pull/9790)\r\n- **FIXED** fix(pubsub): flush bulk subscribe batches immediately via drain-and-flush [9792](https://github.com/dapr/dapr/pull/9792)\r\n- **DOCS** document changes in `allowedServiceAccounts` [9793](https://github.com/dapr/dapr/pull/9793)\r\n- **RESOLVED** Version-Skew: fix [9794](https://github.com/dapr/dapr/pull/9794)\r\n- **ADDED** [P1] Add workflow global and per-name concurrency limits [9796](https://github.com/dapr/dapr/issues/9796)\r\n- **RESOLVED** Fix 'sucessful' typo in scheduler.proto comment [9798](https://github.com/dapr/dapr/pull/9798)\r\n- **RESOLVED** Tests: flakes [9799](https://github.com/dapr/dapr/pull/9799)\r\n- **RESOLVED** feat: add config map configuration store component [9801](https://github.com/dapr/dapr/pull/9801)\r\n- **SECURITY** fix service invocation path traversal ACL bypass [9805](https://github.com/dapr/dapr/pull/9805)\r\n- **RESOLVED** Add missing release notes [9806](https://github.com/dapr/dapr/pull/9806)\r\n- **RESOLVED** Fix flaky memory check in `dapr-standalone-validation` [9808](https://github.com/dapr/dapr/pull/9808)\r\n- **RESOLVED** Integration: fix more flakes [9809](https://github.com/dapr/dapr/pull/9809)\r\n- **ADDED** wf history ctx propagation [9810](https://github.com/dapr/dapr/pull/9810)\r\n- **UPDATED** hotreload: enable HotReload by default (GA) [9811](https://github.com/dapr/dapr/pull/9811)\r\n- **ADDED** Actors: app-initiated gRPC stream for actor callbacks [9812](https://github.com/dapr/dapr/pull/9812)\r\n- **SECURITY** fix: bump golang.org/x/image to v0.39.0 for GO-2026-4962 [9815](https://github.com/dapr/dapr/pull/9815)\r\n- **UPDATED** Widen liveness and tighten readiness probe defaults [9818](https://github.com/dapr/dapr/pull/9818)\r\n- **RESOLVED** Integration: fix flakes for stream dep jobs [9819](https://github.com/dapr/dapr/pull/9819)\r\n- **RESOLVED** Fix typo in multiple license headers [9821](https://github.com/dapr/dapr/pull/9821)\r\n- **RESOLVED** Integration: Workflow: scheduler disk exhaustion tests [9823](https://github.com/dapr/dapr/pull/9823)\r\n- **FIXED** Workflow: terminate tampered in-flight workflows [9825](https://github.com/dapr/dapr/pull/9825)\r\n- **FIXED** Bulk subscribe batches messages up to `maxMessagesCount` or `maxAwaitDurationMs`, whichever comes first, and restarts the await-duration timer on every flush so leftover messages join the next pending batch with a fresh window. Components that declare `pubsub.FeatureBulkSubscribeImmediate` (Pulsar) take a flush-on-arrival path so each message is acked as soon as it arrives. [9826](https://github.com/dapr/dapr/pull/9826)\r\n- **ADDED** Workflow: child workflow & activity attestation [9831](https://github.com/dapr/dapr/pull/9831)\r\n- **FIXED** Workflow: Fix recursive cross-app purge and terminate [9832](https://github.com/dapr/dapr/pull/9832)\r\n- **ADDED** placement: post-round coalesce window + stable scheduler reload [9837](https://github.com/dapr/dapr/pull/9837)\r\n- **REMOVED** Remove MCPServerResource and WorkflowAccessPolicy feature gates [9838](https://github.com/dapr/dapr/pull/9838)\r\n- **RESOLVED** fix: remove availability zone requirement from AKS test clusters [9840](https://github.com/dapr/dapr/pull/9840)\r\n- **FIXED** workflow: gracefully stall on oversized payload [9847](https://github.com/dapr/dapr/pull/9847)\r\n- **CHORE** go-etcd-cron: update to origin HEAD [9849](https://github.com/dapr/dapr/pull/9849)\r\n- **UPDATED** WorkflowAccessPolicy: Extend to all workflow operations [9850](https://github.com/dapr/dapr/pull/9850)\r\n- **RESOLVED** Integration: speed up tests [9854](https://github.com/dapr/dapr/pull/9854)\r\n- **UPDATED** WorkflowAccessPolicy: pure allow-list with self-call exemption [9870](https://github.com/dapr/dapr/pull/9870)\r\n- **UPDATED** Injector/Operator: use RSA keys for webhook serving certs [9873](https://github.com/dapr/dapr/pull/9873)\r\n- **FIXED** Workflow: dedup duplicate completion events at the actor inbox [9875](https://github.com/dapr/dapr/pull/9875)\r\n- **FIXED** fix(placement): always arm coalesce timer after round to fix dissemination race [9876](https://github.com/dapr/dapr/pull/9876)\r\n- **RESOLVED** Integration: fix flaky tests via metadata-API polling and tuning [9877](https://github.com/dapr/dapr/pull/9877)\r\n- **FIXED** fix: Proto files must have different java outer classname [9878](https://github.com/dapr/dapr/pull/9878)\r\n- **CHORE** Update go-jose & OpenTelemetry [9882](https://github.com/dapr/dapr/pull/9882)\r\n- **ADDED** Workflow: cryptographically verify history propagation [9890](https://github.com/dapr/dapr/pull/9890)\r\n- **ADDED** Add start time to wf metadata [9898](https://github.com/dapr/dapr/pull/9898)\r\n- **RESOLVED** docs: add 1.17.7 release note for RabbitMQ consumer tag fix [9926](https://github.com/dapr/dapr/pull/9926)\r\n- **RESOLVED** Integration: stdio helper binary and move helpers. [9932](https://github.com/dapr/dapr/pull/9932)\r\n- **ADDED** feat: add initContainers and extraContainers support to control plane Helm charts [9939](https://github.com/dapr/dapr/pull/9939)\r\n- **FIXED** MCPServer: correct workflow actor refcount [9940](https://github.com/dapr/dapr/pull/9940)\r\n- **ADDED** Add support for sub-second precision in reminders [9946](https://github.com/dapr/dapr/pull/9946)\r\n- **RESOLVED** version-skew: Fix against 1.17 [9953](https://github.com/dapr/dapr/pull/9953)\r\n- **REFACTOR** refactor(mcp): replace hand-defined MCP proto types with upstream go-sdk types [9959](https://github.com/dapr/dapr/pull/9959)\r\n- **RESOLVED** scheduler: reconnect on WatchHosts first-Recv error instead of tearing down daprd [9968](https://github.com/dapr/dapr/pull/9968)\r\n- **RESOLVED** fix(ci): migrate e2e to westus  [9971](https://github.com/dapr/dapr/pull/9971)\r\n- **FIXED** Actors: gate Register/UnRegisterHosted on runtime init [9976](https://github.com/dapr/dapr/pull/9976)\r\n- **RESOLVED** CI: run macOS tests natively on arm64 [9979](https://github.com/dapr/dapr/pull/9979)\r\n- **RESOLVED** getLast func names for workflow history propagation [9981](https://github.com/dapr/dapr/pull/9981)\r\n- **RESOLVED** chore(ci): update vm sizes [9983](https://github.com/dapr/dapr/pull/9983)\r\n- **RESOLVED** chore: Update durabletask-go and components-contrib [9992](https://github.com/dapr/dapr/pull/9992)\r\n- **RESOLVED** Fix test flakes [9993](https://github.com/dapr/dapr/pull/9993)\r\n- **RESOLVED** fix(ci): use Hyper-V Gen 1-compatible Windows VM size for AKS test pool [9994](https://github.com/dapr/dapr/pull/9994)\r\n- **RESOLVED** fix: update Go SSH crypto dependency [9999](https://github.com/dapr/dapr/pull/9999)\r\n- **RESOLVED** fix dep review: net [10003](https://github.com/dapr/dapr/pull/10003)\r\n- **RESOLVED** docs: list new workflow payload size ratio metrics [10005](https://github.com/dapr/dapr/pull/10005)\r\n- **RESOLVED** workflow: save inbox event before creating wake-up reminder [10008](https://github.com/dapr/dapr/pull/10008)\r\n- **RESOLVED** tests: stabilize flaky CI tests [10013](https://github.com/dapr/dapr/pull/10013)\r\n- **RESOLVED** github.com/dapr/kit: update 0.18.1 [10014](https://github.com/dapr/dapr/pull/10014)\r\n- **RESOLVED** fix critical vulns: bump durabletask-go to v0.12.1 and bump pgx [10015](https://github.com/dapr/dapr/pull/10015)\r\n- **RESOLVED** Workflow: retentioner ignore not completed errors [10019](https://github.com/dapr/dapr/pull/10019)\r\n- **RESOLVED** Dependency update: govulncheck [10020](https://github.com/dapr/dapr/pull/10020)\r\n- **RESOLVED** fix(mcp): gate /healthz/outbound on mcpserver wf registration [10023](https://github.com/dapr/dapr/pull/10023)\r\n- **RESOLVED** Use contrib v1.18.0-rc.1 and bump kit [10024](https://github.com/dapr/dapr/pull/10024)\r\n- **RESOLVED** ci(e2e): rename Azure Key Vault resource in workflow [10030](https://github.com/dapr/dapr/pull/10030)\r\n- **RESOLVED** ci: pin azure/login to v3 [10034](https://github.com/dapr/dapr/pull/10034)\r\n- **RESOLVED** feat: register nameformat nameresolution component [10038](https://github.com/dapr/dapr/pull/10038)\r\n\r\n### Dapr CLI\r\n- **FIXED** Using an image registry no longer attempts to reach out over the network. [1575](https://github.com/dapr/cli/issues/1575)\r\n- **CHORE** use dapr v1.17.3 [1598](https://github.com/dapr/cli/pull/1598)\r\n- **CHORE** Adds the shared workflows [1603](https://github.com/dapr/cli/pull/1603)\r\n- **FIXED** fix flaky standalone e2e by dropping \"Exited App successfully\" requirement [1605](https://github.com/dapr/cli/pull/1605)\r\n- **CHORE** chore: Fetch latest version from the image registry and not from github [1615](https://github.com/dapr/cli/pull/1615)\r\n- **FIXED** fix(ci): trigger CLI build after creating release tag [1626](https://github.com/dapr/cli/pull/1626)\r\n- **FIXED** fix: rename with proto changes [1629](https://github.com/dapr/cli/pull/1629)\r\n\r\n### Components\r\n- **FIXED** Bug in Dapr bulk pubsub with rabbitmq [3663](https://github.com/dapr/components-contrib/issues/3663)\r\n- **FIXED** Trouble Generating SAS Token for Blob Storage [3817](https://github.com/dapr/components-contrib/issues/3817)\r\n- **UPDATED** Oracle state store bulk querying improvements [4041](https://github.com/dapr/components-contrib/issues/4041)\r\n- **RESOLVED** feat(pubsub/azure/servicebus/queues): add certification tests [4150](https://github.com/dapr/components-contrib/pull/4150)\r\n- **RESOLVED** fix(nameresolution.aws.cloudmap): quote numeric example in metadata.yaml [4233](https://github.com/dapr/components-contrib/pull/4233)\r\n- **RESOLVED** Component Metadata Bundle GHA Tweaking [4234](https://github.com/dapr/components-contrib/pull/4234)\r\n- **CHORE** chore: Use dapr-bot to create backport PRs [4237](https://github.com/dapr/components-contrib/pull/4237)\r\n- **FIXED** ci: Workflows running twice on merge to master [4240](https://github.com/dapr/components-contrib/pull/4240)\r\n- **FIXED** fix(configuration): ensure postgresql configuration reads pgNotifyChannel from component spec instead of requiring client code passing it [4241](https://github.com/dapr/components-contrib/pull/4241)\r\n- **RESOLVED** fix: Fix dynamodb state store stable test [4242](https://github.com/dapr/components-contrib/pull/4242)\r\n- **RESOLVED** fix(state/oracledatabase): return per-key errors in BulkGet instead of HTTP 500 [4243](https://github.com/dapr/components-contrib/pull/4243)\r\n- **FIXED** fix(pubsub/pulsar): validate JSON messages against Avro schema before publishing [4244](https://github.com/dapr/components-contrib/pull/4244)\r\n- **ADDED** feat: warn when multiple redis stream trim strategies are specified [4252](https://github.com/dapr/components-contrib/pull/4252)\r\n- **FIXED** ci: Allow reruns of failing jobs in master [4260](https://github.com/dapr/components-contrib/pull/4260)\r\n- **SECURITY** fix(common/redis): stop unconditionally skipping TLS certificate verification [4261](https://github.com/dapr/components-contrib/pull/4261)\r\n- **RESOLVED** Update Kafka Sarama Client dependency [4264](https://github.com/dapr/components-contrib/issues/4264)\r\n- **FIXED** fix(pubsub/pulsar): decode Avro binary payload on subscribe path [4266](https://github.com/dapr/components-contrib/pull/4266)\r\n- **FIXED** fix(conversation): nil pointer dereference in langchaingokit LLM logger [4268](https://github.com/dapr/components-contrib/pull/4268)\r\n- **ADDED** feat(config): add config map as configuration [4275](https://github.com/dapr/components-contrib/pull/4275)\r\n- **ADDED** Connection pooling support for the Oracle state store component. [4276](https://github.com/dapr/components-contrib/issues/4276)\r\n- **FIXED** return err if tool calls were not called when required [4277](https://github.com/dapr/components-contrib/pull/4277)\r\n- **FIXED** Fix MongoDB state store to implement KeysLiker [4278](https://github.com/dapr/components-contrib/pull/4278)\r\n- **RESOLVED** test(cassandra): fix port collision in certification tests [4284](https://github.com/dapr/components-contrib/pull/4284)\r\n- **CHORE** fix(state/cockroachdb): update image from v21.2.3 to v23.2.29 [4291](https://github.com/dapr/components-contrib/pull/4291)\r\n- **ADDED** Bulk file transfer operations to AWS S3 binding [4292](https://github.com/dapr/components-contrib/pull/4292)\r\n- **ADDED** Azure Blob Storage binding: add bulk operations [4295](https://github.com/dapr/components-contrib/pull/4295)\r\n- **ADDED** pubsub/kafka: per message partition support [4298](https://github.com/dapr/components-contrib/pull/4298)\r\n- **ADDED** bindings/azure/blobstorage: Adds presign [4299](https://github.com/dapr/components-contrib/pull/4299)\r\n- **ADDED** feat(pubsub/pulsar): register CloudEvents envelope Avro schema with Pulsar Schema Registry [4302](https://github.com/dapr/components-contrib/pull/4302)\r\n- **FIXED** fix(pubsub/pulsar): validate JSON schema payloads against schema definition using goavro codec [4305](https://github.com/dapr/components-contrib/pull/4305)\r\n- **ADDED** fix(pubsub/pulsar): read processMode from component metadata and add async backpressure [4309](https://github.com/dapr/components-contrib/pull/4309)\r\n- **ADDED** fwd port: ollama: add missing endpoint metadata field to spec (#4287) [4314](https://github.com/dapr/components-contrib/pull/4314)\r\n- **CHORE** Use shared workflows [4315](https://github.com/dapr/components-contrib/pull/4315)\r\n- **FIXED** fix(pubsub/rabbitmq): use unique consumer tags to prevent subscription disruption on restart [4326](https://github.com/dapr/components-contrib/pull/4326)\r\n- **RESOLVED** [1.18] zeebe oauth support [4330](https://github.com/dapr/components-contrib/pull/4330)\r\n- **ADDED** Expose LLM model on converse response [4331](https://github.com/dapr/components-contrib/pull/4331)\r\n- **FIXED** fix(ci): create GitHub release with metadata bundle in create-release workflow [4333](https://github.com/dapr/components-contrib/pull/4333)\r\n- **UPDATED** [1.18] Azure appconfig - include contentType and label in metadata [4338](https://github.com/dapr/components-contrib/pull/4338)\r\n- **ADDED** feat(state/oracledatabase): add BulkGet chunking to avoid Oracle IN-list limit [4341](https://github.com/dapr/components-contrib/pull/4341)\r\n- **ADDED** feat(state/oracledatabase): add BulkGet chunking to avoid Oracle IN-list limit [4351](https://github.com/dapr/components-contrib/pull/4351)\r\n- **ADDED** Adds immediate bulk subscriptions to the Pulsar pubsub component [4364](https://github.com/dapr/components-contrib/pull/4364)\r\n- **ADDED** feat(state/sqlserver): native batched BulkGet for v2 [4365](https://github.com/dapr/components-contrib/pull/4365)\r\n- **ADDED** feat(pubsub/kafka): add PausableSubscriber + harden graceful shutdown [4366](https://github.com/dapr/components-contrib/pull/4366)\r\n- **CHORE** bump to go1.26 [4372](https://github.com/dapr/components-contrib/pull/4372)\r\n- **RESOLVED** chore: bump sarama to 1.48.0 [4373](https://github.com/dapr/components-contrib/pull/4373)\r\n- **UPDATED** Enable anthropic in microsoft foundry [4374](https://github.com/dapr/components-contrib/pull/4374)\r\n- **FIXED** fix(pubsub/kafka): reset await ticker after count-based bulk flush [4378](https://github.com/dapr/components-contrib/pull/4378)\r\n- **RESOLVED** fix(ci): update artifact transformations [4381](https://github.com/dapr/components-contrib/pull/4381)\r\n- **FIXED** fix(tests): scope influxdb to v2.9 [4382](https://github.com/dapr/components-contrib/pull/4382)\r\n- **ADDED** listenerName metadata field to Pulsar pubsub component for advertisedListeners support. [4386](https://github.com/dapr/components-contrib/pull/4386)\r\n- **RESOLVED** CI: fix tests [4389](https://github.com/dapr/components-contrib/pull/4389)\r\n- **RESOLVED** version bumps: kit/dapr/go-sdk [4391](https://github.com/dapr/components-contrib/pull/4391)\r\n\r\n### .NET SDK\r\n#### General\r\n- **ADDED** `Dapr.SecretsManagement` package [#1794](https://github.com/dapr/dotnet-sdk/pull/1794) to split secret management into its own client\r\n- **ADDED** `Dapr.StateManagement` package [#1806](https://github.com/dapr/dotnet-sdk/pull/1806) to split state management into its own client\r\n- **ADDED** Rich backwards-compatibility support added to the SDK to better support runtime API maturation lifecycles [#1816](https://github.com/dapr/dotnet-sdk/pull/1816)\r\n- **ADDED** Serializer refactored from `Dapr.Workflow` to `Dapr.Common` [#1805](https://github.com/dapr/dotnet-sdk/pull/1805) as a precursor to having common serialization across .NET SDK\r\n- **ADDED** Add optional parameter to configuration providers for non-blocking startup [#1844](https://github.com/dapr/dotnet-sdk/pull/1844)\r\n- **UPDATED** Making DaprDefaults public [#1845](https://github.com/dapr/dotnet-sdk/pull/1845)\r\n- **FIXED** Prevent race condition during GrpcProtocolHandler disposal [#1798](https://github.com/dapr/dotnet-sdk/pull/1798)\r\n\r\n#### Actors\r\n- **ADDED** `UnloadStateAsync` implementation [#1750](https://github.com/dapr/dotnet-sdk/pull/1750) to force actors to unload their currently cached in-memory state (no impact to persisted state). The next time `GetStateAsync` is called, the cache is repopulated as normal.\r\n\r\n#### PubSub\r\n- **FIXED** Subscriber resilience: `SubscribeAsync` now throws `DaprException` when the sidecar is unavailable instead of failing silently [#1803](https://github.com/dapr/dotnet-sdk/pull/1803)\r\n\r\n#### Metadata\r\n- **ADDED** Added new `Dapr.Metadata` package for retrieving strongly typed data from the [Dapr Metadata API](https://docs.dapr.io/reference/api/metadata_api/) [#1846](https://github.com/dapr/dotnet-sdk/pull/1846). \r\n    To use, add `Dapr.Metadata` package from NuGet, register in DI with `builder.Services.AddDaprMetadata()` and simply inject `IOptions<DaprMetadata>`, `IOptionsSnapshot<DaprMetadata>` or `IOptionsMonitor<DaprMetadata>` and read the values out of the injected value.\r\n\r\n#### Service Invocation\r\n- **UPDATED** `InvokeMethodAsync` family marked `[Obsolete]` aligning SDK with prior runtime guidance — recommended guidance is to use a native HTTP/gRPC client for service invocation [#1698](https://github.com/dapr/dotnet-sdk/pull/1698).\r\n\r\n#### Jobs\r\n- **UPDATED** Jobs API marked stable [#1804](https://github.com/dapr/dotnet-sdk/pull/1804).\r\n- **UPDATED** Minimum-version tags adjusted to reflect broader test coverage ([#1811](https://github.com/dapr/dotnet-sdk/pull/1811)).\r\n\r\n#### Workflows\r\n- **ADDED** Workflow history context propagation ([#1802](https://github.com/dapr/dotnet-sdk/pull/1802))\r\n- **UPDATED** Combined `Dapr.Workflow` meta-project ([#1822](https://github.com/dapr/dotnet-sdk/pull/1822)) and automatic workflow/activity registration ([#1823](https://github.com/dapr/dotnet-sdk/pull/1823))\r\n    This means that there is no longer a need to install `Dapr.Workflow.Versioning` or `Dapr.Workflow.Analyzers` separately. All are bundled in the one `Dapr.Workflow` package, no refactoring necessary (though you might want to remove the stale packages).\r\n- **UPDATED** No longer logging connection closed exception when app closes [#1841](https://github.com/dapr/dotnet-sdk/pull/1841)\r\n- **UPDATED** Use server-blocking WaitForInstance* RPCs instead of client polling [#1843](https://github.com/dapr/dotnet-sdk/pull/1843)\r\n- **ADDED** Timer origins and backwards-compatible optional timers ([#1790](https://github.com/dapr/dotnet-sdk/pull/1790))\r\n- **ADDED** Add workflow analyzer diagnostics on versioning ([#1815](https://github.com/dapr/dotnet-sdk/pull/1815)\r\n- **ADDED** Expose name on workflow state [#1836](https://github.com/dapr/dotnet-sdk/pull/1836)\r\n- **ADDED** Support history propagation API [#1825](https://github.com/dapr/dotnet-sdk/pull/1825), [#1833](https://github.com/dapr/dotnet-sdk/pull/1833)\r\n- **FIXED** Workflow patch ordering [#1807](https://github.com/dapr/dotnet-sdk/pull/1807), [#1809](https://github.com/dapr/dotnet-sdk/pull/1809)\r\n- **FIXED** Trace propagation fix for downstream calls from workflow user activities [#1808](https://github.com/dapr/dotnet-sdk/pull/1808)\r\n- **FIXED** Addressing \"no such instance exists\" errors [#1818](https://github.com/dapr/dotnet-sdk/pull/1818), [#1812](https://github.com/dapr/dotnet-sdk/pull/1812)\r\n- **FIXED** Fix workflow trace correlation [#1829](https://github.com/dapr/dotnet-sdk/pull/1829)\r\n- **FIXED** add missing FailureDetails property to proto converter [#1831](https://github.com/dapr/dotnet-sdk/pull/1831)\r\n\r\n#### Miscellaneous/Testing\r\n- **ADDED** Integration test project for `Actors.Generators` [#1791](https://github.com/dapr/dotnet-sdk/pull/1791).\r\n- **ADDED** Adding gRPC probe with HTTP/2 preface to eliminate some transient `Unavailable` errors during integration testing via Testcontainers [#1821](https://github.com/dapr/dotnet-sdk/pull/1821)\r\n- **UPDATED** Stabilization of flaky integration tests [#1813](https://github.com/dapr/dotnet-sdk/pull/1813).\r\n- **UPDATED** CI script more accurately auto-detects valid Dapr runtime versions to support automatic N-2 validation [#1810](https://github.com/dapr/dotnet-sdk/pull/1810)\r\n- **REMOVED** Removing deprecated analyzers/codefixes [#1834](https://github.com/dapr/dotnet-sdk/pull/1834)\r\n    \r\n\r\n### Java SDK\r\n- **ADDED** Add baggage support [1659](https://github.com/dapr/java-sdk/pull/1659)\r\n- **TEST** chore: Add test to verify workflow reconnection logic [1692](https://github.com/dapr/java-sdk/pull/1692)\r\n- **UPDATED** Use Java source code 17 for Dapr Java SDK 1.18 [1694](https://github.com/dapr/java-sdk/issues/1694)\r\n- **UPDATED** Use latest protobuf [1698](https://github.com/dapr/java-sdk/issues/1698)\r\n- **ADDED** Publish a dapr-sdk-bom artifact [1720](https://github.com/dapr/java-sdk/issues/1720)\r\n- **CHORE** chore: bump minimum Java version from 11 to 17 [1721](https://github.com/dapr/java-sdk/pull/1721)\r\n- **ADDED** feat: publish dapr-sdk-bom artifact for transitive dependency management [1722](https://github.com/dapr/java-sdk/pull/1722)\r\n- **ADDED** Adding trace propagation to local observations [1724](https://github.com/dapr/java-sdk/pull/1724)\r\n- **FIXED** fix(durabletask): detect shutdown and break gRPC worker loop [1727](https://github.com/dapr/java-sdk/pull/1727)\r\n- **ADDED** Implement timer origin [1729](https://github.com/dapr/java-sdk/issues/1729)\r\n- **ADDED** feat(durabletask): implement timer origin and backwards-compatible optional timers [1733](https://github.com/dapr/java-sdk/pull/1733)\r\n- **UPDATED** feat: upgrade Testcontainers from 1.21.4 to 2.0.5 and migrate JUnit 4 to JUnit 5 [1736](https://github.com/dapr/java-sdk/pull/1736)\r\n- **RESOLVED** Add support for workflow history propagation [1737](https://github.com/dapr/java-sdk/issues/1737)\r\n- **ADDED** feat: download attestation.proto and use Runtime 1.18.0-rc.3 [1738](https://github.com/dapr/java-sdk/pull/1738)\r\n- **RESOLVED** [Phase 1] Fix Flaky Integration Tests + Migrate some tests to Test Containers [1741](https://github.com/dapr/java-sdk/pull/1741)\r\n- **ADDED** feat: add DaprClient.invokeHttpClient(appId) factory [1742](https://github.com/dapr/java-sdk/pull/1742)\r\n- **ADDED** `DaprClient.invokeHttpClient(appId)` factory — an SDK-native HTTP client preconfigured for service invocation, mirroring .NET's `CreateInvokeHttpClient` and serving as the successor to the deprecated `invokeMethod` APIs. [1743](https://github.com/dapr/java-sdk/issues/1743)\r\n- **FIXED** fix(release): install dapr-sdk-bom locally before bumping dapr-spring-bom [1744](https://github.com/dapr/java-sdk/pull/1744)\r\n- **ADDED** Add support for dead-letter topics in streaming subscriptions [1746](https://github.com/dapr/java-sdk/pull/1746)\r\n- **RESOLVED** fix: reset gRPC channel backoff between worker reconnect attempts [1757](https://github.com/dapr/java-sdk/pull/1757)\r\n\r\n### Python SDK\r\n#### General\r\n- **REFACTOR** durabletask-python merged into the SDK [#963](https://github.com/dapr/python-sdk/pull/963)\r\n- **ADDED** Python 3.14 support and removal of remaining Python 3.9 vestiges [#916](https://github.com/dapr/python-sdk/pull/916)\r\n- **UPDATED** Centralize versions into a single file and remove development builds [#1020](https://github.com/dapr/python-sdk/pull/1020)\r\n- **UPDATED** No 60s timeout by default [#956](https://github.com/dapr/python-sdk/pull/956); default `drain_ongoing_call_timeout` to None [#1016](https://github.com/dapr/python-sdk/pull/1016)\r\n- **ADDED** Configurable max inbound message size via `DAPR_GRPC_MAX_INBOUND_MESSAGE_SIZE_BYTES` [#1024](https://github.com/dapr/python-sdk/pull/1024)\r\n- **ADDED** Add `aio` module and update mypy configuration to include aio files [#942](https://github.com/dapr/python-sdk/pull/942)\r\n- **UPDATED** Use single `v*` tags to release [#970](https://github.com/dapr/python-sdk/pull/970)\r\n\r\n#### Workflows\r\n- **ADDED** Workflow history context propagation [#1025](https://github.com/dapr/python-sdk/pull/1025); align history propagation API with go-sdk [#1047](https://github.com/dapr/python-sdk/pull/1047)\r\n- **ADDED** Populate timer origin field [#976](https://github.com/dapr/python-sdk/pull/976)\r\n- **ADDED** Pydantic model support on workflow and activity inputs [#982](https://github.com/dapr/python-sdk/pull/982)\r\n- **FIXED** when-all error handling [#978](https://github.com/dapr/python-sdk/pull/978)\r\n- **FIXED** Apply `DAPR_API_TIMEOUT_SECONDS` to workflow gRPC connections [#954](https://github.com/dapr/python-sdk/pull/954)\r\n- **UPDATED** Worker waits indefinitely for start [#983](https://github.com/dapr/python-sdk/pull/983)\r\n- **FIXED** Align workflow multi-app naming convention [#932](https://github.com/dapr/python-sdk/pull/932)\r\n- **REMOVED** Deprecated workflow operations [#943](https://github.com/dapr/python-sdk/pull/943)\r\n\r\n#### MCP\r\n- **ADDED** Dapr MCP client [#997](https://github.com/dapr/python-sdk/pull/997)\r\n- **FIXED** Retry connect calls on transient gRPC errors [#1062](https://github.com/dapr/python-sdk/pull/1062)\r\n\r\n#### Pub/Sub\r\n- **ADDED** Bulk pub/sub support [#915](https://github.com/dapr/python-sdk/pull/915)\r\n\r\n#### Jobs\r\n- **UPDATED** Jobs API marked stable [#1060](https://github.com/dapr/python-sdk/pull/1060)\r\n\r\n#### Miscellaneous/Testing\r\n- **ADDED** Integration tests on DaprClient responses [#981](https://github.com/dapr/python-sdk/pull/981)\r\n- **REFACTOR** Remove Mechanical Markdown from examples validation [#977](https://github.com/dapr/python-sdk/pull/977)\r\n- **FIXED** Fix CI build [#979](https://github.com/dapr/python-sdk/pull/979)\r\n\r\n\r\n\r\n### JavaScript SDK\r\n#### General\r\n- **UPDATED** docs(actors): add JSDoc for actor runtime models and state metadata [815](https://github.com/dapr/js-sdk/pull/815)\r\n- **UPDATED** Comprehensive JSDoc Annotations Added to All Public Interfaces [816](https://github.com/dapr/js-sdk/pull/816)\r\n- **UPDATED** docs(jsdoc): annotate exported type definitions [817](https://github.com/dapr/js-sdk/pull/817)\r\n- **UPDATED** docs(jsdoc): annotate client and server implementations [818](https://github.com/dapr/js-sdk/pull/818)\r\n- **REMOVED** Removing dapr-client from published builds [773](https://github.com/dapr/js-sdk/pull/773)\r\n- **FIXED** fix: correct types path in published package [806](https://github.com/dapr/js-sdk/pull/806)\r\n- **UPDATED** Update buf configuration to emit CommonJS modules instead of ESM [826](https://github.com/dapr/js-sdk/pull/826)\r\n\r\n#### Workflow\r\n- **UPDATED** Renaming workflow methods for consistency with other SDKs [783](https://github.com/dapr/js-sdk/pull/783)\r\n\r\n\r\n\r\n### Documentation\r\n- **RESOLVED** Docs: only one Redis stream trimming strategy supported [5061](https://github.com/dapr/docs/pull/5061)\r\n- **RESOLVED** Updates resiliency of streaming HTTP Service Invocation [5068](https://github.com/dapr/docs/pull/5068)\r\n- **RESOLVED** docs(agents): update with v1.0 rcs out [5071](https://github.com/dapr/docs/pull/5071)\r\n- **RESOLVED** workflow: retention policy is not retroactive [5074](https://github.com/dapr/docs/pull/5074)\r\n- **RESOLVED** Workflow: `wf purge --all-older-than --all-filter-status` [5075](https://github.com/dapr/docs/pull/5075)\r\n- **RESOLVED** docs(pulsar): add CloudEvents schema wrapping, rawschema option, and processMode improvements [5084](https://github.com/dapr/docs/pull/5084)\r\n- **RESOLVED** chore: update Azure Static Web App base hostname to new account [5095](https://github.com/dapr/docs/pull/5095)\r\n- **RESOLVED** chore: fix Azure Static Web App staging URL region suffix [5096](https://github.com/dapr/docs/pull/5096)\r\n- **RESOLVED** chore: update Azure Static Web App to new instance [5097](https://github.com/dapr/docs/pull/5097)\r\n- **RESOLVED** revert: restore original Azure Static Web App configuration [5098](https://github.com/dapr/docs/pull/5098)\r\n- **RESOLVED** Fix duplicate archived_version key in hugo.yaml [5101](https://github.com/dapr/docs/pull/5101)\r\n- **RESOLVED** Documented `allowedServiceAccounts` [5105](https://github.com/dapr/docs/pull/5105)\r\n- **RESOLVED** Add OAuth configuration fields to Zeebe command and JobWorker binding [5106](https://github.com/dapr/docs/pull/5106)\r\n- **RESOLVED** docs(agents): add MistralChatClient to supported LLMs [5108](https://github.com/dapr/docs/pull/5108)\r\n- **DOCS** [1.18] Add docs for global and per-name workflow concurrency limits [5109](https://github.com/dapr/docs/pull/5109)\r\n- **RESOLVED** [1.18] WorkflowAccessPolicy [5114](https://github.com/dapr/docs/pull/5114)\r\n- **RESOLVED** 1.18 Add response metadata section for Azure App Configuration [5118](https://github.com/dapr/docs/pull/5118)\r\n- **RESOLVED** fix: correct broken ref shortcode link in [5126](https://github.com/dapr/docs/pull/5126)\r\n- **RESOLVED** Update filename numbering to match quickstarts/ on dapr-agents [5143](https://github.com/dapr/docs/pull/5143)\r\n- **RESOLVED** Revert \"Documentation for native-sidecar (#5041)\" [5146](https://github.com/dapr/docs/pull/5146)\r\n- **RESOLVED** Add Reo.dev and cookie banner [5147](https://github.com/dapr/docs/pull/5147)\r\n- **RESOLVED** docs(agents): document replay-aware logging for DurableAgent [5149](https://github.com/dapr/docs/pull/5149)\r\n- **RESOLVED** docs(outbox): document outboxInternalTopic metadata field [5155](https://github.com/dapr/docs/pull/5155)\r\n- **RESOLVED** fix gcp storage bucket binding docs [5157](https://github.com/dapr/docs/pull/5157)\r\n- **RESOLVED** Improve ASP.NET Core docs and reference NuGet library [5164](https://github.com/dapr/docs/pull/5164)\r\n- **RESOLVED** docs: Sentry Ed25519 workload identity keys [5181](https://github.com/dapr/docs/pull/5181)\r\n- **RESOLVED** docs: Clarify max-body-size applies to requests and responses [5186](https://github.com/dapr/docs/pull/5186)\r\n\r\n## Upgrading to Dapr 1.18\r\n\r\nTo upgrade to this release of Dapr, follow the steps below to ensure a smooth upgrade.\r\n\r\n### Local Machine / Self-hosted\r\n\r\nUninstall Dapr using the CLI you currently have installed. Note that this will remove the default `$HOME/.dapr` directory, binaries and all containers `dapr_redis`, `dapr_placement` and `dapr_zipkin`. Linux users need to run `sudo` if `docker` command needs sudo:\r\n\r\n```bash\r\ndapr uninstall --all\r\n```\r\n\r\nDownload the latest release from [here](https://github.com/dapr/cli/releases) and put the `dapr` binary in your `PATH`.\r\n\r\nOnce you have installed the CLI, run:\r\n\r\n```bash\r\ndapr init --runtime-version=1.18\r\n```\r\n\r\nWait for the update to finish, then ensure you are using the latest version of Dapr (1.18) with:\r\n\r\n```bash\r\n$ dapr --version\r\n\r\nCLI version: 1.18\r\nRuntime version: 1.18\r\n```\r\n\r\n### Kubernetes\r\n\r\n#### Upgrading from previous version\r\n\r\nYou can perform zero-downtime upgrades using both Helm 3 and the Dapr CLI.\r\n\r\n##### Upgrade using the CLI\r\n\r\nDownload the latest release from [here](https://github.com/dapr/cli/releases) and put the `dapr` binary in your `PATH`.\r\n\r\nTo upgrade Dapr, run:\r\n\r\n```\r\ndapr upgrade --runtime-version 1.18 -k\r\n```\r\n\r\nTo upgrade with high availability mode:\r\n\r\n```\r\ndapr upgrade --runtime-version 1.18 --enable-ha=true -k\r\n```\r\n\r\nWait until the operation is finished and check your status with `dapr status -k`.\r\n\r\n*Note: Make sure your deployments are restarted to pick the latest version of the Dapr sidecar.*\r\n\r\n##### Upgrade using Helm\r\n\r\nTo upgrade Dapr using Helm, run:\r\n\r\n```\r\nhelm repo add dapr https://dapr.github.io/helm-charts/\r\nhelm repo update\r\n\r\nhelm upgrade dapr dapr/dapr --version 1.18 --namespace=dapr-system --wait\r\n```\r\n\r\nWait until the operation is finished and check your status with `dapr status -k`.\r\n\r\n*Note: Make sure your deployments are restarted to pick the latest version of the Dapr sidecar.*\r\n\r\n#### Starting a fresh install on a cluster\r\n\r\nSee [how to deploy Dapr on a Kubernetes cluster](https://docs.dapr.io/operations/hosting/kubernetes/kubernetes-deploy/) for a complete guide to installing Dapr on Kubernetes.\r\n\r\nYou can use Helm 3 to install Dapr:\r\n\r\n```\r\nhelm repo add dapr https://dapr.github.io/helm-charts/\r\nhelm repo update\r\n\r\nkubectl create namespace dapr-system\r\n\r\nhelm install dapr dapr/dapr --version 1.18 --namespace dapr-system --wait\r\n```\r\n\r\nAlternatively, you can use the latest version of CLI:\r\n\r\n```\r\ndapr init --runtime-version=1.18 -k\r\n```\r\n\r\n##### Post Installation\r\n\r\nVerify the control plane pods are running and are healthy:\r\n\r\n```\r\n$ dapr status -k\r\n  NAME                   NAMESPACE    HEALTHY  STATUS   REPLICAS  VERSION  AGE  CREATED\r\n  dapr-sidecar-injector  dapr-system  True     Running  1         1.18   15s  2026-05-26 13:07.39\r\n  dapr-sentry            dapr-system  True     Running  1         1.18   15s  2026-05-26 13:07.39\r\n  dapr-operator          dapr-system  True     Running  1         1.18   15s  2026-05-26 13:07.39\r\n  dapr-placement         dapr-system  True     Running  1         1.18   15s  2026-05-26 13:07.39\r\n  dapr-scheduler         dapr-system  True     Running  1         1.18   15s  2026-05-26 13:07.39\r\n```\r\n\r\nAfter Dapr 1.18 has been installed, perform a rolling restart for your deployments to pick up the new version of the sidecar:\r\n\r\n```\r\nkubectl rollout restart deploy/<deployment-name>\r\n```\r\n\r\n## Downgrading to Earlier Versions\r\n\r\n> [!warning] Do not roll back from 1.18 directly to 1.17.6 or earlier.\r\n\r\nDapr 1.18 introduces [Ed25519 workload identity keys](#sentry-ed25519-workload-identity-keys), and Sentry persists the Ed25519-keyed CA into the `dapr-trust-bundle` secret. Versions of Sentry before **1.17.7** cannot parse this bundle and will crash on startup with:\r\n\r\n```\r\nfatal: error creating CA: failed to get CA bundle:\r\nfailed to verify CA bundle: unsupported key type ed25519.PrivateKey\r\n```\r\n\r\nWhen Sentry is down, no new workload certificates can be issued; existing sidecars with unexpired certificates keep working, but any sidecar restart or certificate expiry will fail to obtain a new identity.\r\n\r\n### Compatibility matrix\r\n\r\n| From | To | Result |\r\n|---|---|---|\r\n| 1.18 | **1.17.7+** | ✅ Safe. 1.17.7 backports the Ed25519 PEM-decoder fix ([dapr/dapr#9904](https://github.com/dapr/dapr/pull/9904)) so Sentry can read the 1.18 trust bundle as-is. |\r\n| 1.18 | 1.17.6 or earlier | ❌ Sentry crash-loops at startup. Do **not** attempt this rollback. |\r\n| 1.17.6 or earlier | 1.18 | ✅ Safe. 1.18 Sentry accepts CSRs of any algorithm; existing ECDSA P-256 bundles are read transparently. |\r\n\r\n### Safe rollback path\r\n\r\nIf you must downgrade below 1.17.7, **go through 1.17.7 first**:\r\n\r\n1. Roll the control plane back to **1.17.7** (which includes the Ed25519 parsing fix and is the earliest version that can read a 1.18-issued trust bundle).\r\n2. Allow Sentry to stabilise and confirm `dapr status -k` is healthy.\r\n3. From 1.17.7, downgrade to your target version. Note that you may want to first rotate the CA back to an algorithm the target version generates natively — see your Sentry CA rotation runbook.\r\n\r\n### Rolling back the control plane\r\n\r\nUse Helm rollback (preferred) or re-install at the target version:\r\n\r\n```bash\r\n# Helm: list revisions, then roll back to the desired one\r\nhelm history dapr -n dapr-system\r\nhelm rollback dapr <REVISION> -n dapr-system --wait\r\n\r\n# Or pin a specific version\r\nhelm upgrade dapr dapr/dapr --version 1.17.7 --namespace dapr-system --wait\r\n```\r\n\r\nAfter the control plane is back, perform a rolling restart of your application Deployments so sidecars pick up the older injector image and re-issue their identities against the rolled-back Sentry.\r\n\r\n### What does NOT need to be rotated\r\n\r\nGoing **1.18 → 1.17.7** does not require manual CA rotation. The Ed25519-keyed bundle remains valid; 1.17.7 reads it, continues to issue *new* CSRs against the existing trust anchor, and ECDSA/RSA CSRs from 1.17.x sidecars are signed normally. Only re-rotate the CA if your downgrade target is below 1.17.7 or if your security posture requires it.\r\n\r\n## Breaking Changes\r\n\r\n> [!warning] Review carefully before upgrading\r\n\r\n- **`WorkflowsRemoteActivityReminder` default-on.** Cross-app workflow activity results are now delivered via Scheduler reminders by default. See [dapr/docs#5128](https://github.com/dapr/docs/pull/5128) and [dapr/dapr#9789](https://github.com/dapr/dapr/pull/9789).\r\n- **HotReload default-on.** Components, Subscriptions, MCPServers, Configurations, HTTPEndpoints, Resiliencies, and WorkflowAccessPolicies are hot-reloaded by default. To restore the pre-1.18 behavior, disable the `HotReload` feature in the Dapr `Configuration` (`spec.features` with `enabled: false`). See [dapr/dapr#9811](https://github.com/dapr/dapr/pull/9811).\r\n- **Sidecar probe defaults changed.** Liveness is more lenient (~230 s before kubelet restart); readiness is tighter (~3 s control-plane / ~5 s daprd). Custom Helm overrides may need re-tuning. See [dapr/dapr#9818](https://github.com/dapr/dapr/pull/9818).\r\n- **Workflow ID reuse semantics changed.** The pre-1.18 workflow ID reuse policy has been removed; creating a workflow with an instance ID that already has an active instance now fails with a conflict error (`an active workflow with ID '<id>' already exists`) instead of silently overwriting or ignoring it. Callers that previously relied on the silent-overwrite or ignore behavior must adapt. See [dapr/docs#5102](https://github.com/dapr/docs/pull/5102).\r\n- **Hop-by-hop headers stripped on service invocation.** Standard hop-by-hop HTTP headers (`Connection`, `Keep-Alive`, `Proxy-Authenticate`, `Transfer-Encoding`, etc.) are now stripped during service invocation, per RFC 7230. Apps relying on these passing through must move to non-hop-by-hop equivalents. See [dapr/docs#5172](https://github.com/dapr/docs/pull/5172) and [dapr/dapr#9759](https://github.com/dapr/dapr/pull/9759).\r\n- **JS SDK: `dapr-client` removed.** The standalone `dapr-client` npm package is no longer published. Consumers must move to the unified `@dapr/dapr` package. See [dapr/js-sdk#773](https://github.com/dapr/js-sdk/pull/773).\r\n- **Java SDK: minimum Java version is now 17.** The Dapr Java SDK no longer supports Java 11. See [dapr/java-sdk#1721](https://github.com/dapr/java-sdk/pull/1721) and tracking issue [#1694](https://github.com/dapr/java-sdk/issues/1694).\r\n- **Rollback floor is 1.17.7.** Dapr 1.18 writes an Ed25519-keyed CA into the trust bundle. Sentry versions before 1.17.7 cannot parse this bundle and will crash on startup. Operators downgrading from 1.18 must go through **1.17.7** (or later 1.17.x) before any further rollback. See the [Downgrading to Earlier Versions](#downgrading-to-earlier-versions) section and [dapr/dapr#9598](https://github.com/dapr/dapr/pull/9598) / [#9904](https://github.com/dapr/dapr/pull/9904).\r\n\r\n## Deprecation Notices\r\n\r\n- **Workflows**: Workflow instance ID reuse policy removed. The IGNORE and TERMINATE actions on instance ID conflicts are no longer supported; creating a workflow whose instance ID belongs to a running instance always fails with a conflict error. An instance ID whose workflow has reached a terminal state (completed, failed, or terminated) remains reusable; as of 1.18.2 this additionally requires every child workflow of the previous execution, checked recursively, to also be terminal. To free up an instance ID unconditionally, purge the existing workflow via the purge API or configure a workflow retention policy. [dapr/dapr#9739]\r\n- **Java SDK: Spring Boot 3.4 and 3.5 deprecated.** See [dapr/java-sdk#1718](https://github.com/dapr/java-sdk/issues/1718).\r\n- **Java SDK: `invokeMethod` APIs (deprecated).** Use the new `DaprClient.invokeHttpClient(appId)` factory introduced in this release. See [dapr/java-sdk#1743](https://github.com/dapr/java-sdk/issues/1743).\r\n- **.NET SDK: service-invocation `InvokeMethodAsync` family marked `[Obsolete]`.** Recommended guidance is to use a native HTTP or gRPC client for service invocation. See [dapr/dotnet-sdk#1698](https://github.com/dapr/dotnet-sdk/pull/1698).\r\n- **JS SDK: workflow methods renamed.** `get` / `start` / `raise` are renamed to `getWorkflowState` / `scheduleNewWorkflow` / `raiseEvent` for consistency with the other SDKs. The old names remain as deprecated aliases and continue to work, with removal scheduled for Dapr 1.20. See [dapr/js-sdk#783](https://github.com/dapr/js-sdk/pull/783).\r\n- **Dapr Dashboard deprecated** repository archived. The [dapr/dashboard](https://github.com/dapr/dashboard) repo has been archived and is no longer maintained as it had drifted well behind daprd and the maintainers chose to wind it down rather than ship a stale UI. For a maintained alternative, see the [Diagrid Dev Dashboard](https://docs.diagrid.io/develop/local-development/dev-dashboard/); otherwise use kubectl, the dapr CLI subcommands, or your observability stack (Grafana/Prometheus) to inspect Dapr 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Dapr 1.17.10\r\n\r\nThis update contains the following bug fix:\r\n- [Resiliency retry `matching` is ignored on the pubsub publish path](#resiliency-retry-matching-is-ignored-on-the-pubsub-publish-path)\r\n\r\n## Resiliency retry `matching` is ignored on the pubsub publish path\r\n\r\n### Problem\r\n\r\nA resiliency retry policy that configures `matching` (`httpStatusCodes` / `gRPCStatusCodes`) on a pubsub component's outbound policy has no effect on publish.\r\nEvery `Publish` / `BulkPublish` error is retried up to `maxRetries` regardless of the configured codes, so non-retriable (terminal) errors are retried wastefully and the configured `matching` is silently ignored.\r\n\r\n### Impact\r\n\r\nAny deployment with a `Resiliency` configuration that sets `matching` on a retry policy targeting a pubsub component's `outbound` retry is affected.\r\n\r\nVisible symptoms include:\r\n- A clearly terminal publish error (for example an invalid topic or an unauthorized request) is retried up to `maxRetries` instead of failing fast.\r\n- The status codes listed in `matching.httpStatusCodes` / `matching.gRPCStatusCodes` have no effect on which publish errors are retried.\r\n\r\n### Root Cause\r\n\r\nThe retry runner in `pkg/resiliency/policy.go` only consults the configured codes when the operation error is a `resiliency.CodeError`.\r\nEvery other policy runner — service invocation, the gRPC proxy, output bindings, and inbound subscriber delivery — constructs a `CodeError` before handing the error to the runner, but the component-outbound publish path returned the broker's plain error verbatim.\r\nAs a result the publish error was never recognized as a `CodeError`, the configured codes were never consulted, and it fell through to the runner as an ordinary retryable error.\r\n\r\n### Solution\r\n\r\nThe `Publish` and `BulkPublish` paths now wrap the component error in a `resiliency.CodeError` when it carries a gRPC status (via `status.FromError`), mirroring the other policy runners.\r\nErrors that do not carry a status are returned unchanged and keep retrying exactly as before, so behavior is unchanged unless retry `matching` is configured.\r\n\r\nThis is the runtime half of the fix; pubsub components in `components-contrib` emit gRPC status codes on publish errors so the runtime can classify them as retriable or 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is the release candidate 1.18.0-rc.5\r\n\r\n## What's Changed\r\n* [Backport release-1.18] fix: update Go SSH crypto dependency by @dapr-bot in https://github.com/dapr/dapr/pull/10001\r\n* [Backport release-1.18] chore(ci): update vm sizes by @dapr-bot in https://github.com/dapr/dapr/pull/9989\r\n* [Backport release-1.18] CI: run macOS tests natively on arm64 by @dapr-bot in https://github.com/dapr/dapr/pull/9988\r\n* [Backport release-1.18] fix dep review: net by @dapr-bot in https://github.com/dapr/dapr/pull/10006\r\n* [Backport release-1.18] Integration: stdio helper binary and move helpers. by @dapr-bot in https://github.com/dapr/dapr/pull/9982\r\n* [Backport release-1.18] chore: Update durabletask-go and components-contrib by @dapr-bot in https://github.com/dapr/dapr/pull/9998\r\n* [Backport release-1.18] Fix test flakes by @dapr-bot in https://github.com/dapr/dapr/pull/10009\r\n* [Backport release-1.18] workflow: save inbox event before creating wake-up reminder by @dapr-bot in https://github.com/dapr/dapr/pull/10010\r\n* [1.18 backport] fix(ci): use Hyper-V Gen 1-compatible Windows VM size for AKS test po… by @cicoyle in https://github.com/dapr/dapr/pull/10004\r\n* [Backport release-1.18] docs: list new workflow payload size ratio metrics by @dapr-bot in https://github.com/dapr/dapr/pull/10011\r\n* [Backport release-1.18] github.com/dapr/kit: update 0.18.1 by @dapr-bot in https://github.com/dapr/dapr/pull/10016\r\n* [Backport release-1.18] fix critical vulns: bump durabletask-go to v0.12.1 and bump pgx by @dapr-bot in https://github.com/dapr/dapr/pull/10017\r\n* [Backport release-1.18] Dependency update: govulncheck by @dapr-bot in https://github.com/dapr/dapr/pull/10022\r\n* [Backport release-1.18] tests: stabilize flaky CI tests by @dapr-bot in https://github.com/dapr/dapr/pull/10018\r\n* [Backport release-1.18] Workflow: retentioner ignore not completed errors by @dapr-bot in https://github.com/dapr/dapr/pull/10026\r\n* [Backport release-1.18] Sentry: fix OIDC X-Forwarded-Host issuer/jwks_uri injection by @dapr-bot in https://github.com/dapr/dapr/pull/10029\r\n* [Backport release-1.18] ci(e2e): rename Azure Key Vault resource in workflow by @dapr-bot in https://github.com/dapr/dapr/pull/10031\r\n* [1.18] Use contrib v1.18.0-rc.1 and bump kit (#10024) + cherrypick jobs api perf tests by @cicoyle in https://github.com/dapr/dapr/pull/10033\r\n* [Backport release-1.18] fix(mcp): gate /healthz/outbound on mcpserver wf registration by @dapr-bot in https://github.com/dapr/dapr/pull/10036\r\n* [Backport release-1.18] ci: pin azure/login to v3 by @dapr-bot in https://github.com/dapr/dapr/pull/10035\r\n* [Backport release-1.18] feat: add config map configuration store component by @dapr-bot in https://github.com/dapr/dapr/pull/10040\r\n* [Backport release-1.18] feat: register nameformat nameresolution component by @dapr-bot in https://github.com/dapr/dapr/pull/10047\r\n* [Backport release-1.18] fix(grpc): remove singleton connection limit for local app by @dapr-bot in https://github.com/dapr/dapr/pull/10048\r\n* [Backport release-1.18] Clean up references to the archived dapr/dashboard by @dapr-bot in https://github.com/dapr/dapr/pull/10044\r\n* [Backport release-1.18] fix vulns and IT flake by @dapr-bot in https://github.com/dapr/dapr/pull/10049\r\n* [Backport release-1.18] Workflow: skip purge deletes for non-persisted optional keys by @dapr-bot in https://github.com/dapr/dapr/pull/10051\r\n\r\n\r\n**Full Changelog**: 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Dapr 1.17.9\r\n\r\nThis update contains the following bug fix:\r\n- [Workflow retention purge fails on Azure Cosmos DB when `customStatus` is not persisted](#workflow-retention-purge-fails-on-azure-cosmos-db-when-customstatus-is-not-persisted)\r\n\r\n## Workflow retention purge fails on Azure Cosmos DB when `customStatus` is not persisted\r\n\r\n### Problem\r\n\r\nA completed workflow whose `customStatus` row does not exist in the actor state store cannot be purged by the retention reminder when the state store is Azure Cosmos DB.\r\nThe retention reminder fires every second indefinitely and the workflow stays in the `Completed` state past its configured retention TTL.\r\n\r\n### Impact\r\n\r\nAny deployment using the Azure Cosmos DB state store (`state.azure.cosmosdb`) for the workflow actor state store is affected whenever a workflow reaches a terminal state without a `customStatus` row persisted in the store.\r\n\r\nThis includes:\r\n- Workflows that were first saved by a pre-`customStatus` daprd version and have since been upgraded.\r\n- Workflows whose `customStatus` row was removed out of band (manual cleanup, partial restore from backup, etc.).\r\n- Workflows scheduled but never advanced past the initial state, so no history delta ever triggered the `customStatus` upsert.\r\n\r\nVisible symptoms include:\r\n- A completed workflow stays in the `Completed` state past the configured `stateRetentionPolicy.anyTerminal` TTL.\r\n- The scheduler retains the retentioner reminder and re-fires it once per second indefinitely.\r\n- The `dapr_runtime_workflow_operation_count{operation=purge_workflow,status=failed}` metric increments once per second per affected workflow.\r\n- daprd logs `failed to invoke scheduled actor reminder named: retention due to: transaction failed` at one tick per second per affected workflow.\r\n- The workflow recovers only after an operator manually deletes the retentioner job out of the scheduler.\r\n\r\n### Root Cause\r\n\r\n`GetPurgeRequest` in `pkg/runtime/wfengine/state/state.go` unconditionally emitted a delete for the `customStatus` key alongside the metadata, inbox, and history deletes, regardless of whether the row was actually persisted.\r\n\r\nThe Azure Cosmos DB state store (`components-contrib/state/azure/cosmosdb`) translates `state.TransactionalStore.Multi` into a single Cosmos transactional batch.\r\nCosmos batches are atomic: if any operation in the batch fails, the whole batch is rolled back and every operation returns `FailedDependency`.\r\nA delete for a row that does not exist returns `NotFound` and aborts the batch.\r\nThe state component's \"tolerate `NotFound` on etag-less delete\" path only applies to single-operation calls, not to batched ones, so the purge transaction was rolled back and the workflow state stayed in place.\r\n\r\nThe retentioner reminder is created with a failure policy of `Constant{Interval: 1s, MaxRetries: nil}` (retry every second, forever), so the scheduler retried the same doomed batch once per second indefinitely.\r\n\r\n### Solution\r\n\r\n`State` now tracks `customStatusPersisted` as an explicit observation, set from the bulk-get ETag at load time and maintained in `ResetChangeTracking` to reflect whichever upserts the most recent save committed.\r\n`GetPurgeRequest` consults this flag and only emits the `customStatus` delete when the row is known to exist in the store.\r\n\r\nWorkflows already stuck in this state on existing 1.17 deployments recover automatically once the sidecar is upgraded to 1.17.9.\r\nOn the next retention reminder fire after restart, daprd reloads the workflow state from Cosmos, observes the missing `customStatus` row from its absent ETag, omits the delete, and Cosmos accepts the batch.\r\nThe reminder drains and the workflow is purged in the normal way.\r\nNo operator intervention or manual scheduler delete is required after the 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Dapr 1.17.8\r\n\r\nThis update contains the following bug and security fix:\r\n- [Workflow stuck and unrecoverable after re-scheduling an instance ID that has already completed](#workflow-stuck-and-unrecoverable-after-re-scheduling-an-instance-id-that-has-already-completed)\r\n- [Security: Sentry OIDC discovery document poisonable via `X-Forwarded-Host` (CWE-346)](#security-sentry-oidc-discovery-document-poisonable-via-x-forwarded-host-cwe-346)\r\n\r\n## Workflow stuck and unrecoverable after re-scheduling an instance ID that has already completed\r\n\r\n### Problem\r\n\r\nWhen an application schedules a workflow with a deterministic instance ID and a later request re-schedules the same ID after the previous run has reached a terminal state, the previous run's retention reminder can keep firing in the scheduler indefinitely instead of draining.\r\n\r\n### Impact\r\n\r\nAny deployment running workflows that are scheduled with stable, repeatable instance IDs is affected whenever the same ID is scheduled again within the configured retention window.\r\nThe most visible trigger is an at-least-once delivery against an idempotent handler that uses `REUSE_ID_ACTION_TERMINATE`, but plain `ScheduleNewWorkflow` calls against an already-completed instance ID follow the same path.\r\n\r\nVisible symptoms include:\r\n\r\n- A workflow appears stuck: subsequent re-scheduling attempts for the same ID do not run to completion on their own, and the workflow's status oscillates between non-terminal and terminal states without ever settling.\r\n- The scheduler accumulates one retention reminder per affected workflow that does not drain after the configured retention TTL elapses.\r\n- The `dapr_runtime_workflow_operation_count{operation=purge_workflow,status=failed}` metric increments at exactly one tick per second per affected workflow, for as long as the bug is active.\r\n- The workflow recovers only after an operator manually deletes the retentioner job out of the scheduler.\r\n\r\n### Root Cause\r\n\r\nWhen a workflow is scheduled with an instance ID that already corresponds to a completed run, the workflow runtime resets the existing state and starts a fresh run.\r\nThe retention reminder queued by the previous run is anchored to the previous run's completion time and is intentionally left in the scheduler so it can fire on the previous run's terminal-state retention TTL.\r\n\r\nWhen that retention reminder fires, the workflow has been put back into a non-completed state by the fresh run, so the workflow runtime's purge path correctly refuses to delete state that is in flight, and signals \"this run has not completed\".\r\nThe retentioner treated that signal as a hard failure and re-raised it.\r\nThe retention reminder is created with a failure policy of `Constant{Interval: 1s, MaxRetries: nil}` (retry every second, forever), so the scheduler then retried the same reminder once per second indefinitely.\r\nOnly an operator-driven scheduler delete could clear it.\r\n\r\n### Solution\r\n\r\nThe retentioner now treats the \"this run has not completed\" signal the same way it already treats \"this run no longer exists\": as a drain-silently signal that the retention reminder is no longer applicable.\r\nEither the run has been purged (nothing to retain) or the run has been superseded by a fresh run (the fresh run will queue its own retention reminder on completion).\r\nEither way, the previous run's retention reminder is obsolete and the scheduler can let it drop.\r\n\r\nWorkflows already stuck in this state on existing 1.17 deployments recover automatically once the sidecar is upgraded to 1.17.8: the next retention reminder fire drains, the reminder is removed from the scheduler, and the workflow proceeds normally.\r\nNo operator intervention or manual scheduler delete is required after the upgrade.\r\n\r\n## Security: Sentry OIDC discovery document poisonable via `X-Forwarded-Host` (CWE-346)\r\n\r\n### Problem\r\n\r\nWhen the Dapr Sentry OIDC HTTP server was started without a statically configured JWT issuer (`--jwt-issuer`) and without an allowed-hosts list (`--oidc-allowed-hosts`), the `X-Forwarded-Host` request header fully controlled the `issuer` and `jwks_uri` fields returned by `/.well-known/openid-configuration`.\r\nAn attacker with network reach to the OIDC endpoint (typically via a reverse proxy or ingress that forwards `X-Forwarded-Host`) could inject an arbitrary issuer URL on a single request.\r\nBecause the discovery response is served with `Cache-Control: public, max-age=3600`, the poisoned document could be cached by HTTP intermediaries for up to an hour.\r\n\r\n### Impact\r\n\r\nAny deployment that enabled the Sentry OIDC server (`--oidc-enabled --jwt-enabled`) without configuring either a static issuer (`--jwt-issuer`) or an allowed-hosts list (`--oidc-allowed-hosts`) was affected.\r\nThis configuration is most likely on operators who turned on OIDC federation for Sentry-issued JWTs (e.g. to allow AWS IAM, GCP Workload Identity, or Azure AD relying parties to validate Dapr-issued tokens) without yet pinning the external hostname.\r\n\r\nVisible exposure included:\r\n\r\n- A discovery response whose `issuer` and `jwks_uri` pointed at an attacker-controlled host after a single request carrying `X-Forwarded-Host: attacker.example`.\r\n- Relying parties performing dynamic OIDC discovery (without a pinned expected issuer) fetching JWKS from the attacker's server, opening a window in which attacker-signed JWTs would validate against the attacker's keys.\r\n- HTTP intermediaries caching the poisoned response for up to an hour, amplifying the window beyond the original attack request.\r\n\r\nSetting either `--jwt-issuer` or `--oidc-allowed-hosts` already mitigated the issue on affected versions; relying parties that pinned the expected issuer rejected the forged token regardless.\r\n\r\n### Root Cause\r\n\r\n`handleDiscovery` in `pkg/sentry/server/oidc/oidc.go` derives the OIDC issuer dynamically when no static `--jwt-issuer` is configured, as a convenience for operators who have not yet pinned an external hostname.\r\nThe derivation read `X-Forwarded-Host` unconditionally and substituted it for `r.Host` whenever the header was present, with no validation.\r\nThe companion `allowedHostsValidationHandler` middleware would have rejected unknown hosts, but is a no-op when `--oidc-allowed-hosts` is unset (the default), so neither layer authenticated the header in the default configuration.\r\n\r\n### Solution\r\n\r\n`handleDiscovery` now only honors `X-Forwarded-Host` when `--oidc-allowed-hosts` is configured, i.e. when the allowed-hosts middleware has already validated the header against the operator's allowlist.\r\nWith no allowlist, the issuer and `jwks_uri` are derived from `r.Host` only, and the header is ignored.\r\n\r\nOperators who deliberately rely on `X-Forwarded-Host` to advertise the proxy's public hostname in the discovery document should set `--oidc-allowed-hosts` to the set of hostnames they expect (e.g. `--oidc-allowed-hosts=sentry.example.com`); this is consistent with the existing guidance for reverse-proxy deployments and is now also the precondition for the header to influence the discovery document.\r\n\r\nOperators who cannot upgrade immediately can mitigate by setting either `--jwt-issuer` (preferred, pins the issuer statically) or `--oidc-allowed-hosts` (restricts which hostnames may be reflected 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Dapr 1.17.7\r\n\r\nThis update contains the following bug fixes:\r\n- [Workflow GetWorkItems gRPC stream torn down when the history payload exceeds the max body size](#workflow-getworkitems-grpc-stream-torn-down-when-the-history-payload-exceeds-the-max-body-size)\r\n- [Workflows orphaned or not purged after scheduler pod restart under load](#workflows-orphaned-or-not-purged-after-scheduler-pod-restart-under-load)\r\n- [Workflow inbox accumulates duplicate completion events under pod migration, driving an SDK spin loop](#workflow-inbox-accumulates-duplicate-completion-events-under-pod-migration-driving-an-sdk-spin-loop)\r\n- [Sentry fails to start with \"unsupported key type\" when the issuer key is Ed25519 or RSA](#sentry-fails-to-start-with-unsupported-key-type-when-the-issuer-key-is-ed25519-or-rsa)\r\n- [Kafka in-flight pub/sub messages abandoned during graceful shutdown](#kafka-in-flight-pubsub-messages-abandoned-during-graceful-shutdown)\r\n- [Kafka bulk subscriber partial batches flushed early after a count-based flush](#kafka-bulk-subscriber-partial-batches-flushed-early-after-a-count-based-flush)\r\n- [RabbitMQ pub/sub subscription restart kills sibling subscriptions and amplifies duplicate processing](#rabbitmq-pubsub-subscription-restart-kills-sibling-subscriptions-and-amplifies-duplicate-processing)\r\n- [Long actor drainOngoingCallTimeout blocks placement dissemination and resets the placement stream](#long-actor-drainongoingcalltimeout-blocks-placement-dissemination-and-resets-the-placement-stream)\r\n- [Workflow activity body executes multiple times in parallel during scheduler or daprd reconnect](#workflow-activity-body-executes-multiple-times-in-parallel-during-scheduler-or-daprd-reconnect)\r\n- [daprd and scheduler crash with \"could not determine host IP address\" on IPv6-only clusters](#daprd-and-scheduler-crash-with-could-not-determine-host-ip-address-on-ipv6-only-clusters)\r\n- [No observability for workflow payload proximity to the gRPC max body size before stalling](#no-observability-for-workflow-payload-proximity-to-the-grpc-max-body-size-before-stalling)\r\n- [Scheduler stops delivering job triggers after periodic etcd compaction under workflow load](#scheduler-stops-delivering-job-triggers-after-periodic-etcd-compaction-under-workflow-load)\r\n- [Workflows freeze in RUNNING after placement rebalance](#workflows-freeze-in-running-after-placement-rebalance)\r\n- [daprd shuts down and stays unready after a brief scheduler outage](#daprd-shuts-down-and-stays-unready-after-a-brief-scheduler-outage)\r\n\r\n## Workflow GetWorkItems gRPC stream torn down when the history payload exceeds the max body size\r\n\r\n### Problem\r\n\r\nWhen the proto-encoded `WorkItem` that the orchestrator sends to a connected SDK worker on the `GetWorkItems` gRPC stream grew larger than the dapr API gRPC server's `MaxSendMsgSize` (which is the same as `--max-body-size`, default 4 MiB), the underlying `stream.Send` returned `ResourceExhausted` and the entire stream was cancelled.\r\nEvery other workflow that happened to be pending on the same stream was cancelled along with it, and the SDK reconnected only to repeat the same failure on the same offending workflow.\r\n\r\n### Impact\r\n\r\nAny long-running workflow whose accumulated history (`PastEvents` + `NewEvents` + propagated history) crossed the configured `--max-body-size` could trigger a stream tear-down loop.\r\nVisible symptoms included:\r\n\r\n- The offending workflow appeared frozen with no diagnostic in its history.\r\n- Other workflows that shared the worker's stream were repeatedly cancelled mid-execution and replayed.\r\n- The SDK logged repeated reconnects to the dapr sidecar.\r\n- An activity dispatched with a very large `PropagatedHistory` could exhibit the same tear-down on the activity work-item path.\r\n\r\n### Root Cause\r\n\r\nNeither the orchestrator nor the durabletask gRPC executor measured the size of the `WorkItem` proto before pushing it onto the stream.\r\nOnce the message reached `stream.Send`, gRPC enforced `MaxSendMsgSize` and aborted the entire `GetWorkItems` server stream with `ResourceExhausted`.\r\nBecause the failure was at the transport layer, the runtime had no place to record a structured signal back to the user, and there was no terminal state for an orchestration that could not legally be dispatched.\r\n\r\n### Solution\r\n\r\nThe orchestrator now precomputes the proto size of the `WorkItem` it is about to dispatch and compares it to a 95% safety threshold of `--max-body-size` (the headroom covers the engine's `WorkflowStarted` event injection plus gRPC framing overhead).\r\nIf the threshold would be crossed:\r\n\r\n- For a workflow dispatch, `runWorkflow` short-circuits before the work item is handed to the durabletask scheduler.\r\n- For an activity dispatch, `callActivity` short-circuits before the activity actor is invoked, and the parent workflow is stalled.\r\n\r\nEither path appends an `ExecutionStalled` event to the workflow's history with the new `StalledReason` value `PAYLOAD_SIZE_EXCEEDED` and transitions the workflow into the existing `STALLED` state.\r\nThe orchestrator's stallable lock is held until the actor is deactivated, so the next activation re-evaluates: if the operator has purged or terminated the workflow, or restarted daprd with a larger `--max-body-size`, the workflow resumes; otherwise it re-stalls without disturbing other instances on the stream.\r\n\r\n## Workflows orphaned or not purged after scheduler pod restart under load\r\n\r\n### Problem\r\n\r\nWhen a scheduler pod is killed during workflow execution under load, some workflows become orphaned: they remain in `RUNNING` state with no further execution, or they reach a terminal state but are never purged despite a configured retention policy.\r\n`dapr workflow history` shows nothing abnormal, execution simply stops.\r\n`dapr workflow list` reports the affected completed workflows as much older than the configured retention window.\r\n\r\n### Impact\r\n\r\nAny deployment running workflows with a multi-replica scheduler is affected when scheduler pods restart during load.\r\nThis is most visible during routine operations such as Kubernetes rolling updates, node drains, or OOM-driven scheduler restarts.\r\n\r\n### Root Cause\r\n\r\nThe actor state-store transaction that persists workflow state was not coordinated with the gRPC call that registers the corresponding wake-up reminder in the scheduler service.\r\nThese are two independent operations against two different systems with no atomic boundary between them.\r\n\r\nWhen a scheduler pod was killed mid-RPC, the state save had typically completed and the reminder Create was lost.\r\nThe reminder failure policy retries an *already-persisted* reminder forever; it cannot recover a reminder whose Create RPC never reached durable storage.\r\n\r\nFor completed workflows, the retention path was particularly fragile: the workflow's firing reminder was deleted before the retention reminder was created.\r\nIf the retention Create then failed, no reminder remained to drive a retry, leaving the workflow terminal-but-not-purged.\r\n\r\n### Solution\r\n\r\nThree changes close the loss windows:\r\n\r\n1. **In-process retry on reminder creation**.\r\n   Every reminder Create now retries with bounded exponential backoff (up to 60 seconds total) before returning to the caller.\r\n   Retries reuse the same reminder Name; the scheduler's overwrite-by-name semantics keep them idempotent.\r\n   A typical scheduler-pod failover completes in seconds, so the retry transparently heals the failure without surfacing it to the workflow.\r\n\r\n2. **Retention reminder created before deletion**.\r\n   In the completion path, the retention reminder is now registered before the workflow's own reminders are deleted.\r\n   If the retention Create still fails after the in-process retry, the firing reminder remains alive and its failure-policy retry brings execution back to the completion path.\r\n\r\n3. **Idempotent retention recovery on re-fire**.\r\n   When a reminder fires for a workflow whose state is already terminal but whose inbox is empty, the runtime now re-issues the retention reminder Create.\r\n   The retention reminder name is deterministic, so this is a safe overwrite rather than a duplicate.\r\n   This recovers workflows whose completion was persisted in a prior run but whose retention reminder Create was lost.\r\n\r\nThe retention reminder's due time is now anchored to the workflow's actual completion time rather than the moment of the Create call, so retries converge on a single reminder at a stable due time instead of pushing retention back on every retry.\r\n\r\n## Workflow inbox accumulates duplicate completion events under pod migration, driving an SDK spin loop\r\n\r\n### Problem\r\n\r\nWhen the workflow actor on one pod was cancelled mid-flight (typically during a rolling deployment) after dispatching an activity but before its state save committed, the activity actor still completed normally and posted its `TaskCompleted` event back to the workflow actor's inbox.\r\nOn the next workflow activation, the orchestrator re-yielded the same `ScheduleTask` because its replay state did not yet reflect the dispatch, so the activity actor ran a second time and posted a second `TaskCompleted` for the same `taskScheduledId`.\r\nThe same shape applied to `TaskFailed`, `TimerFired`, and child-workflow completions delivered through the inbox.\r\n\r\nThe language SDK's `process_event` handlers for these event kinds silently `return` when no matching pending task is found, producing zero new actions, so dapr re-fired the wake-up reminder against the same un-cleared inbox and the cycle repeated.\r\n\r\n### Impact\r\n\r\nAny deployment running workflows whose hosting pods are restarted during load is affected.\r\nThis is most visible during routine operations such as Kubernetes rolling updates or node drains.\r\n\r\nVisible symptoms include:\r\n\r\n- A workflow appears stuck in `RUNNING` while its persisted history grows steadily with full activity payloads.\r\n- Sidecar logs show repeated `dropping duplicate event: executionStarted` warnings on the dapr side, paired with thousands of `Ignoring unexpected taskCompleted event with ID = N` warnings on the SDK side for the same instance.\r\n- An activity executes more times than the workflow function calls it, because the activity actor re-runs each time the orchestrator re-yields the schedule.\r\n\r\n### Root Cause\r\n\r\nTwo layers were missing safeguards.\r\n\r\nFirst, the workflow actor's `addWorkflowEvent` (the inbox-write boundary called by the activity actor and by sub-workflow completion delivery) did not deduplicate task-resolution events.\r\nA redelivered completion was appended to the inbox, persisted, and a new wake-up reminder was created, even when the same resolution was already committed to history or queued in the inbox from an earlier delivery.\r\n\r\nSecond, the orchestrator's `callActivities` did not check whether the activity it was about to dispatch had already resolved.\r\nWhen the orchestrator re-yielded a `ScheduleTask` because its replay state was missing the corresponding `TaskScheduled` (e.g. after a partial save was lost on cancellation), the activity actor was invoked again, ran the activity body again, and posted yet another `TaskCompleted` to the inbox.\r\nThe two layers compounded: the inbox grew because the dispatch produced new completions, the orchestrator re-ran because the inbox grew, and the SDK silently spun on the unmatched events.\r\n\r\n### Solution\r\n\r\nTwo complementary checks were added in the workflow actor, both backed by a shared dedup helper:\r\n\r\n1. **Inbox-write dedup in `addWorkflowEvent`**.\r\n   A `TaskCompleted` / `TaskFailed` / `TimerFired` / `ChildWorkflowInstance{Completed,Failed}` whose correlator (`taskScheduledId` or `timerId`) already appears in either `state.History` or `state.Inbox` is dropped before it reaches `state.AddToInbox`, the transactional save, and the new-event reminder.\r\n   `EventRaised` and `ExecutionTerminated` are intentionally excluded: `EventRaised` is a user signal that may legitimately repeat, and `ExecutionTerminated` is idempotent.\r\n\r\n2. **Dispatch-skip in `callActivities`**.\r\n   Before invoking the activity actor for a `TaskScheduled`, the workflow actor checks whether a matching `TaskCompleted` or `TaskFailed` for the same `taskScheduledId` is already in `state.History` or `state.Inbox`.\r\n   If it is, the dispatch is suppressed; the orchestrator's stale re-yield no longer triggers a second activity run.\r\n\r\nThe underlying engine in `durabletask-go` was hardened in lockstep: `runtimestate.AddEvent` now also rejects a resolution event whose correlator is already present, providing defence in depth for any caller that bypasses the actor's inbox-write path.\r\nThe `Stalled`-clear logic runs only on a successful add, so a duplicate-rejection error preserves a prior stalled state.\r\n\r\nAfter upgrading, persisted histories from older daprd versions that already accumulated duplicates are silently truncated on next workflow load (the duplicate entries are not re-added to the in-memory `OldEvents`), so the upgrade is one-way for that state.\r\n\r\n## Sentry fails to start with \"unsupported key type\" when the issuer key is Ed25519 or RSA\r\n\r\n### Problem\r\n\r\nOperators who downgraded a control plane from 1.18 back to 1.17 saw `dapr-sentry` crash on startup with:\r\n\r\n```\r\nfatal: error creating CA: failed to get CA bundle: failed to verify CA bundle: unsupported key type ed25519.PrivateKey\r\n```\r\n\r\nThe same failure mode also rejected RSA-keyed issuer bundles. The crash is hit before sentry serves any traffic, so every sidecar that depends on sentry for its identity certificate stops being able to obtain or rotate one.\r\n\r\n### Impact\r\n\r\nAny 1.17 control plane whose `dapr-trust-bundle` secret was generated by, or migrated through, a newer Dapr release that issues Ed25519 (or RSA) issuer keys is affected. In practice this includes:\r\n\r\n- Downgrade from Dapr 1.18 to 1.17 against the same cluster.\r\n- Existing 1.17 deployments where the issuer key was rotated or replaced with an Ed25519 / RSA key by the operator.\r\n\r\nSentry crash-loops, no new mTLS identities are issued, and existing certificates are not rotated. Sidecars whose certs have not yet expired keep working; sidecars that come up fresh, restart, or hit cert expiry start failing to obtain identities.\r\n\r\n### Root Cause\r\n\r\n`dapr/kit`'s `crypto/pem.EncodePrivateKey` (used by sentry to re-encode the issuer key it just decoded from the trust bundle) only matched `*ecdsa.PrivateKey` and `*ed25519.PrivateKey` in its type switch. `ed25519.PrivateKey` is itself a `[]byte` alias rather than a struct, so the `*ed25519.PrivateKey` case never matched a real Ed25519 key. RSA private keys were never listed at all.\r\n\r\nWhen sentry called `EncodePrivateKey` on an Ed25519 or RSA issuer key it fell through to the default branch and returned `unsupported key type %T`, which the CA initialiser surfaced as a fatal error.\r\n\r\n### Solution\r\n\r\n`dapr/kit`'s `EncodePrivateKey` now matches `ed25519.PrivateKey` (value form) and `*rsa.PrivateKey` alongside `*ecdsa.PrivateKey`. All three round-trip through PKCS#8 unchanged. Dapr 1.17.7 picks up this fix by bumping `github.com/dapr/kit` to `v0.17.1`, which also includes table-driven roundtrip tests for ECDSA P-256, RSA-2048, and Ed25519 to guard the regression.\r\n\r\nNo operator action is required beyond upgrading sentry to 1.17.7. Existing trust bundles are read as-is; the issuer key is not regenerated.\r\n\r\n## Kafka in-flight pub/sub messages abandoned during graceful shutdown\r\n\r\n### Problem\r\n\r\nWhen a sidecar received SIGTERM, Kafka pub/sub subscriptions tore down their consumer group session before the messages already fetched from the broker had been delivered to the application.\r\nThe contrib retry loop observed `context canceled`, the runtime logged `Too many failed attempts at processing Kafka message ... Error: context canceled`, and the broker handed the same offsets to whichever consumer won the rebalance.\r\n\r\n### Impact\r\n\r\nAny deployment running Kafka pub/sub through a multi-replica subscriber was affected on rolling restarts, node drains, or any other graceful-shutdown event.\r\nVisible symptoms included:\r\n\r\n- Repeated `Too many failed attempts at processing Kafka message` and `kafka: tried to use a consumer group that was closed` errors during shutdown.\r\n- The application processed the same message twice across pods (once via a partial in-flight call that got cancelled, again after rebalance).\r\n- Latency-sensitive workloads (e.g. financial transactions) experienced retry-driven tail latency on every pod restart.\r\n\r\n### Root Cause\r\n\r\nThe runtime's `Subscription.Stop()` set its `closed` flag immediately on entry, which caused the handler closure to reject any further deliveries from contrib with `errors.New(\"subscription is closed\")`.\r\nContrib treated that as an error and retried inside an already-closing session, eventually giving up and surrendering the partition to the rebalance.\r\n\r\nThe \"in-flight\" definition was also too narrow: only handlers already inside the closure counted, while messages that contrib had pulled from the broker but not yet handed to the handler were considered absent and got the rejection path.\r\n\r\n### Solution\r\n\r\nA new `pubsub.PausableSubscriber` capability lets the runtime ask a component to stop fetching from the broker without tearing down the consumer group session.\r\nOn graceful shutdown the runtime now:\r\n\r\n1. Pauses the underlying component (Kafka's implementation calls Sarama's `PauseAll`, which stops broker fetches while keeping the session and partition assignments alive).\r\n2. Leaves `closed=false` during a bounded drain window so handlers continue delivering buffered messages to the application via postman.\r\n3. Polls an `inflight` counter with a stable-quiet predicate (100 ms of consecutive zero readings on the paused path) so the drain does not seal in the sub-millisecond gap between handler return and the next claim-buffer read.\r\n4. Caps the drain at 30 seconds so a misbehaving application that keeps returning RETRY cannot block `StopAllSubscriptionsForever` and prevent the block-shutdown timer from starting.\r\n5. On ceiling hit, force-cancels the subscription context so stuck handlers' HTTP/gRPC calls error out via context propagation rather than running indefinitely.\r\n6. Falls back to the previous close-first behavior for non-pausable components and non-graceful Stop calls.\r\n\r\nThe components-contrib Kafka component additionally gates `consumerGroup.Close()` on the last subscription exiting (so multi-topic pubsubs no longer race a sibling subscription's reload into a closed group) and demotes the `Too many failed attempts` log to debug when the cause is shutdown rather than real retry exhaustion.\r\n\r\n## Kafka bulk subscriber partial batches flushed early after a count-based flush\r\n\r\n### Problem\r\n\r\nWhen a Kafka bulk subscriber's buffer filled to `maxMessagesCount` and was flushed before its `maxAwaitDurationMs` window had elapsed, the await ticker continued firing on its original schedule.\r\nAny subsequent partial batch was then flushed within (often well under) one period of the count-based flush instead of waiting for a fresh `maxAwaitDurationMs` window from the moment the buffer was last drained.\r\n\r\n### Impact\r\n\r\nAny deployment using Kafka bulk pub/sub subscriptions with both `maxMessagesCount` and `maxAwaitDurationMs` configured was affected.\r\nVisible symptoms included:\r\n\r\n- Partial batches delivered to the application much sooner than the configured `maxAwaitDurationMs` after a count-based flush.\r\n- Effective batch sizes lower than expected during steady-state traffic, because the await window was shortened by however much of the original window had already elapsed before the count threshold was hit.\r\n- Workloads tuned to amortize per-batch overhead (large bulk handlers, batched downstream writes) seeing more invocations than the configuration implied.\r\n\r\n### Root Cause\r\n\r\nIn `ConsumeClaim`, the bulk path used a single `time.Ticker` constructed from `maxAwaitDurationMs` to trigger time-based flushes.\r\nWhen the count threshold (`len(messages) >= maxMessagesCount`) was reached and `flushBulkMessages` was called, the ticker was not reset.\r\nThe next tick still fired at its original wall-clock schedule, so a partial batch arriving just after a count-flush was eligible for flush after only the residual portion of the original ticker period rather than a full `maxAwaitDurationMs`.\r\n\r\n### Solution\r\n\r\nAfter a count-based flush in `ConsumeClaim`, the await ticker is now reset to a fresh `maxAwaitDurationMs` window via `ticker.Reset`, anchoring the next time-based flush to the moment of the count-flush.\r\nGo 1.23+ guarantees that `Ticker.Reset` discards any tick that was queued before the call, so no stale tick can fire immediately after the reset and short-circuit the new window.\r\nPartial batches now consistently wait a full `maxAwaitDurationMs` after the most recent flush, regardless of whether that flush was triggered by the count threshold or the timer.\r\n\r\n## RabbitMQ pub/sub subscription restart kills sibling subscriptions and amplifies duplicate processing\r\n\r\n### Problem\r\n\r\nOn a `pubsub.rabbitmq/v1` subscription restart — most commonly triggered by an app health check transitioning from unhealthy back to healthy, but also by any streaming-subscription stop/start or runtime-driven re-subscribe path — the RabbitMQ broker rejected the new subscription with `AMQP 530 NOT_ALLOWED - attempt to reuse consumer tag '<queue-name>'`. Because all RabbitMQ subscriptions on a single daprd shared the same broker connection state, that rejection cascaded into every sibling subscription, tearing them down even when their own subscriptions were healthy.\r\n\r\n### Impact\r\n\r\nAny deployment running `pubsub.rabbitmq/v1` with two or more subscriptions on a single daprd was affected on any event that triggered a subscription restart. The most common triggers were Dapr app health-check flaps, streaming subscription stop/start, and bursts of `dapr-scheduler` or `dapr-placement` re-elections that knocked the AMQP connection over.\r\n\r\nVisible symptoms included:\r\n\r\n- Repeated `NOT_ALLOWED - attempt to reuse consumer tag '<queue-name>'` lines in daprd logs immediately followed by `channel/connection is not open` errors on sibling subscriptions.\r\n- A leaking RabbitMQ connection count (the reconnect counter stepping up multiple times per minute) alongside cross-topic `Exception (503) unexpected command received` lines in the same log window.\r\n- Logs in which an error attributed to one topic referenced a *different* topic's queue name — a side-effect of the cascading tear-down on a shared broker connection, not a Dapr binding mismatch.\r\n- **Duplicate processing of in-flight messages.** RabbitMQ + Dapr pub/sub is at-least-once: every cascading tear-down opens a redelivery window for messages whose handler completed but whose ack did not reach the broker before the connection died. This bug expanded that window from \"real broker or network failures\" to \"any subscription restart\", so handlers without idempotent side-effects observed the same message processed two or more times during a storm.\r\n\r\n### Root Cause\r\n\r\nThe RabbitMQ pub/sub component reused the same broker-side consumer identifier for every subscription attempt on a given queue, and did not explicitly tell the broker to release the prior registration when a subscription restarted. The second registration therefore collided with the still-live first one on the broker, which raised a connection-level exception that disrupted every other RabbitMQ subscription sharing that connection.\r\n\r\n### Solution\r\n\r\nThe RabbitMQ pub/sub component now uses a unique broker-side consumer identifier per subscription attempt and explicitly releases the prior registration with the broker on subscription teardown. A subscription restart no longer collides with a stale registration, sibling subscriptions on the same connection keep running, and the amplified redelivery window closes back to \"real connection failures only\".\r\n\r\nApplication code does not need to change. Handlers should remain idempotent because Dapr + RabbitMQ pub/sub is at-least-once even with this fix in place — a real broker or network failure between handler completion and ack will still cause a redelivery — but the everyday subscription-restart path no longer manufactures redeliveries.\r\n\r\n## Long actor drainOngoingCallTimeout blocks placement dissemination and resets the placement stream\r\n\r\n### Problem\r\n\r\nWhen an application supplied an actor `drainOngoingCallTimeout` that met or exceeded the daprd-side placement dissemination timeout (default 30 seconds), drain held the placement `LOCK -> UPDATE -> UNLOCK` round long enough for daprd to reset its own placement stream.\r\nThe most visible trigger was the Python SDK shipping a 60-second drain default while the runtime defaulted dissemination to 30 seconds, but any explicit override above the dissemination budget produced the same behaviour.\r\n\r\nIn addition, per-actor-type `drainOngoingCallTimeout` configured via `entitiesConfig` was parsed but never applied: only the top-level `drainOngoingCallTimeout` reached the placement layer, so per-type values silently fell back to the global drain.\r\n\r\n### Impact\r\n\r\n- Routine placement disseminations (a peer joining or leaving) ran for the full configured drain, exceeding the dissemination budget.\r\n- daprd reset its placement stream mid-round, halting hosted actors until the stream re-established and the next dissemination round completed.\r\n- Per-type drain settings configured by users had no effect on actual drain behaviour.\r\n\r\n### Root Cause\r\n\r\nThe configured drain timeout was passed straight into `placement.SetDrainOngoingCallTimeout` with no validation against the dissemination budget; daprd then waited the full drain in `inflight.CancelClaimsForTypes` during the `UPDATE` phase.\r\nOn the per-entity path, `api.TranslateEntityConfig` parsed `drainOngoingCallTimeout` into the domain `EntityConfig` but `actors.RegisterHosted` only forwarded the global value to placement.\r\n\r\n### Solution\r\n\r\n`api.ClampDrainOngoingCallTimeout` now bounds the configured drain at registration time.\r\nWhen drain meets or exceeds the dissemination timeout, the runtime logs a warning naming the source (`global config` or `entities=<types>`) and clamps the value to `disseminationTimeout * 0.8`, floored at `DefaultOngoingCallTimeout` (2 seconds).\r\nThe clamp applies to both the global drain in `actors.RegisterHosted` and the per-entity drain in `api.TranslateEntityConfig`; the inverted-conditional bugs in `TranslateEntityConfig` are also corrected as part of this change.\r\n\r\nPer-actor-type `drainOngoingCallTimeout` is now wired through to placement.\r\nA new `placement.SetEntityDrainOngoingCallTimeouts` plumbs an `entityDrainTimeouts` map onto `Inflight`, and the lock loop's `handleCancelTypes` groups in-flight claims by actor type and drains each type in parallel against its own timer, falling back to the global drain when no per-type override is set.\r\nTotal drain wall-clock for a round is therefore bounded by the largest per-type drain rather than the sum.\r\n\r\n## Workflow activity body executes multiple times in parallel during scheduler or daprd reconnect\r\n\r\n### Problem\r\n\r\nWhen the gRPC `WatchJobs` stream between daprd and the scheduler reconnected while an activity was in flight (typical triggers: a scheduler pod restart, a daprd pod restart, an application pod restart, or a network blip), the same activity reminder could be redelivered several times in a tight burst before the connection stabilised.\r\nEach redelivery that reached daprd's reminder handler dispatched a fresh `ActivityWorkItem` to the application's SDK worker, so the activity callback ran 2..N times concurrently for a single scheduled activity.\r\n\r\nThe workflow's logical state remained correct because the orchestrator deduplicated the resulting `TaskCompleted` events, but the user-facing application code observed real parallel executions of an activity that the workflow had only scheduled once.\r\n\r\n### Impact\r\n\r\nAny deployment whose application or scheduler pods restart during workflow activity execution is affected.\r\nThis is most visible during routine operations such as Kubernetes rolling updates, node drains, or OOM-driven restarts, and during transient scheduler-daprd network instability.\r\n\r\nVisible symptoms include:\r\n\r\n- The same activity body running concurrently in the application, sometimes mutating the same downstream resources multiple times.\r\n- SDK telemetry showing repeated activity invocations (often with `error: operation aborted`) for a single workflow `taskScheduledId`.\r\n\r\n### Root Cause\r\n\r\nThe activity actor lock is context-scoped: when the gRPC stream that delivered the activity reminder dropped, the actor's reminder context was cancelled, the in-flight `executeActivity` returned `ctx.Err()`, and the actor lock released.\r\nThe next reminder retry could then acquire the lock and call `a.scheduler(ctx, wi)` again, pushing a second `ActivityWorkItem` into the durabletask worker queue.\r\nThe work-item worker had no per-activity dedup, so each work item was streamed to the SDK and ran the activity callback.\r\n\r\n### Solution\r\n\r\nThe activity actor now keeps an in-memory inflight guard per `(activityActorID, taskExecutionId)`.\r\nThe first reminder for a given activity scheduling becomes the owner: it dispatches the work item, waits on the SDK callback, publishes the result back to the workflow actor, and finalises the inflight entry.\r\nConcurrent reminder retries become followers: they observe the owner's outcome via the inflight entry and surface that outcome to their caller, so the scheduler acks `SUCCESS` for each retry without re-dispatching the activity to the SDK.\r\n\r\nThe cache key includes the durabletask `TaskExecutionId` so that retries of the same scheduling share the entry while a new workflow run that reuses the same instance ID (for example after a `Purge` followed by a fresh `ScheduleNewWorkflow`) gets a fresh entry.\r\nCached outcomes are released after a 60 second TTL.\r\n\r\n## daprd and scheduler crash with \"could not determine host IP address\" on IPv6-only clusters\r\n\r\n### Problem\r\n\r\n`GetHostAddress()` only worked on IPv4 networks: it dialed `8.8.8.8` (IPv4 only) and the fallback only accepted addresses where `To4() != nil`. On IPv6-only clusters, daprd and the scheduler crashed at startup with \"could not determine host IP address\".\r\n\r\n### Impact\r\n\r\nAny deployment running on an IPv6-only network was unable to start daprd or the scheduler.\r\n\r\n### Root Cause\r\n\r\nThe UDP dial target was hardcoded to `8.8.8.8:80`, which requires IPv4 connectivity. The fallback interface enumeration filtered for `To4() != nil`, excluding all IPv6 addresses. Several downstream call sites also built `host:port` strings with plain concatenation (`host + \":\" + port`), which produces invalid addresses for IPv6 literals (missing brackets).\r\n\r\n### Solution\r\n\r\nReplaced the hardcoded `8.8.8.8` probe with UDP dials to RFC documentation addresses (`192.0.2.1` for IPv4 and `2001:db8::1` for IPv6). The interface fallback now considers both IPv4 and IPv6 addresses with priority ordering: public IPv4, IPv6 GUA, private IPv4/CGNAT, IPv6 ULA, and link-local as last resort.\r\n\r\nCall sites that consume the address were updated to use `net.JoinHostPort` (actor placement, `IsActorLocal`) and RFC 7239 IPv6 quoting in the `Forwarded` header (direct messaging).\r\n\r\n## No observability for workflow payload proximity to the gRPC max body size before stalling\r\n\r\n### Problem\r\n\r\nThe orchestrator stalls workflows whose payloads would exceed 95% of the dapr API gRPC server's `--max-body-size` on the way to the SDK (the `PAYLOAD_SIZE_EXCEEDED` precheck added earlier in this release).\r\nOperators had no way to see how close payloads were to that threshold until a workflow actually tripped it.\r\nBy then the workflow was stuck in `STALLED` and the only remediation was to raise `--max-body-size` and restart daprd, or force purge the workflow all together.\r\n\r\n### Impact\r\n\r\n- Capacity planning was reactive: operators could not predict which workflows or activities were trending toward the stall before they tripped.\r\n- Post-incident triage required reading sidecar logs to confirm that the stall fired on a genuinely oversize payload rather than another failure mode.\r\n- Fleets running the same workflow with different `--max-body-size` settings could not be compared on a single dashboard.\r\n\r\n### Root Cause\r\n\r\nThe precheck computed `proto.Size(...)` of the workflow and activity payloads inline with the stall decision, but the value was never exported.\r\nThere was no metric describing the size distribution of payloads sent to the SDK.\r\n\r\n### Solution\r\n\r\nTwo new histograms are exported from `DefaultWorkflowMonitoring`, recorded from the same code path that performs the stall precheck, so the value fed into the threshold comparison is the value exported as a metric:\r\n\r\n- `dapr_runtime_workflow_payload_size_ratio`, tagged by `app_id`, `namespace`, `workflow_name`. Recorded once per workflow dispatch.\r\n- `dapr_runtime_workflow_activity_payload_size_ratio`, tagged by `app_id`, `namespace`, `workflow_name`, `activity_name`. Recorded once per activity dispatch, including the dispatch that trips the stall.\r\n\r\nBoth express payload size as a fraction of the configured `--max-body-size` rather than absolute bytes.\r\nThe ratio is portable across daprds with different `--max-body-size` settings, scales beyond the absolute-size distribution's 4 GiB ceiling, and makes proximity-to-stall queries trivial: `histogram_quantile(0.99, ...) > 0.95` fires before the next stall.\r\n\r\nBuckets are concentrated near the 0.95 stall threshold (`0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 1.0, 1.5, 2.0`).\r\nRecording is skipped when `--max-body-size` is not configured, since the ratio is undefined without a limit.\r\n\r\n## Scheduler stops delivering job triggers after periodic etcd compaction under workflow load\r\n\r\n### Problem\r\n\r\nUnder sustained workflow load, the scheduler service would intermittently stop delivering job triggers to connected daprds.\r\nThe pod stayed healthy: its embedded etcd still served reads and writes, its gRPC API still returned, and the leader did not change.\r\nThe only observable difference was that new jobs (workflow timers, activity dispatches) created on the scheduler were never streamed to the daprds that had subscribed for them.\r\nThe only known remediation was to restart the scheduler pod.\r\n\r\n### Impact\r\n\r\nAny deployment running workflows at a sufficiently high job-churn rate to outrun the embedded etcd's compaction retention could be affected.\r\nThis was most visible on long-running scheduler pods with many concurrent workflows; short-lived deployments or low-throughput jobs rarely tripped it.\r\n\r\nVisible symptoms included:\r\n\r\n- Workflows that scheduled correctly but never fired their next activity or timer.\r\n- `dapr workflow history` showing the orchestration paused at a `TaskScheduled` with no corresponding `TaskCompleted`.\r\n- The scheduler's etcd metrics showing normal write throughput while the informer goroutine was idle.\r\n\r\n### Root Cause\r\n\r\nThe scheduler's job pipeline is driven by an informer that opens a single long-lived etcd `Watch` against the jobs prefix and re-emits each event onto an internal channel for the queue to consume.\r\n\r\nThree weaknesses in that watch interacted under workflow write rates:\r\n\r\n1. The watcher's tracked revision only advanced when an event actually arrived.\r\n   With periodic compaction at a 10-minute retention, an idle watcher's cursor could fall behind the compaction window; the next event then returned `ErrCompacted` rather than being delivered.\r\n2. The receive arm of the informer's select only ranged over `WatchResponse.Events`.\r\n   It did not distinguish a closed channel from a delivery, and never inspected `Canceled`, `Err()`, or `CompactRevision` on the response.\r\n   When any of those happened (a compaction race, a server-side cancel, a fatal client error), the goroutine kept selecting against a dead channel and silently delivered nothing.\r\n3. Several embedded etcd defaults were inherited from etcd's general-purpose settings rather than tuned for the high-write, high-watch profile workflows produce: `SnapshotCount=10000` (high WAL/snapshot churn), `BackendBatchInterval=50ms` / `BackendBatchLimit=5000` (more frequent fsyncs than necessary), 10-minute periodic compaction (unpredictable MVCC growth), and an unset `MaxTxnOps` (etcd default of 128, below the size of fan-out transactions).\r\n\r\n### Solution\r\n\r\nThe scheduler's informer watch is now hardened against all three weaknesses:\r\n\r\n- The watch is opened with `WithProgressNotify`, so the etcd client advances the watcher's internal revision cursor on idle watches and resumes from a fresh revision on transient reconnects, keeping the cursor inside the compaction retention window in the common case.\r\n- The informer's receive arm now distinguishes a closed channel via the receive's `ok` value, and explicitly checks `Canceled`, `Err()`, and `CompactRevision` on each `WatchResponse`.\r\n  Any of these now logs and returns, unwinding to the cron leadership loop which performs a fresh `SyncBase` + `SyncUpdates` cycle instead of leaving a silently-dead watcher in place.\r\n\r\nThe scheduler's embedded etcd defaults are retuned for the workflow read/write profile:\r\n\r\n| Setting | Old | New |\r\n|---|---|---|\r\n| `--etcd-snapshot-count` | `10000` | `100000` |\r\n| `--etcd-compaction-mode` | `periodic` | `revision` |\r\n| `--etcd-compaction-retention` | `10m` | `1000000` |\r\n| `--etcd-backend-batch-limit` | `5000` | `10000` |\r\n| `--etcd-backend-batch-interval` | `50ms` | `100ms` |\r\n| `--etcd-max-txn-ops` (new flag) | (etcd's `128`) | `10000` |\r\n\r\nCLI flag defaults and helm chart values are kept in sync; the StatefulSet passes every flag explicitly so K8s and standalone scheduler behave the same.\r\n\r\nThe helm chart's `cluster.storageSize` default is also raised from `1Gi` to `16Gi` for fresh installs.\r\nBecause `StatefulSet.spec.volumeClaimTemplates` is immutable in Kubernetes, the chart uses a new `dapr_scheduler.storageSize` helper that detects the live StatefulSet via `lookup` and pins storage to the existing PVC size on upgrade; fresh installs and offline `helm template` fall through to the new default.\r\nExisting 1Gi clusters are not disrupted; operators wanting more space on an existing install must expand the PVC directly on the cluster.\r\n\r\n## Workflows freeze in RUNNING after placement rebalance\r\n\r\n### Problem\r\n\r\nWhen placement rebalances actors (during a placement pod restart, leader election, node drain, or network blip), two daprds can briefly believe they each own the same workflow actor.\r\nEach daprd loaded the workflow's state, appended its own changes locally, and saved back to the state store without coordinating with the other.\r\nWhichever daprd committed second races with the first's history rows at the same offset.\r\nThe workflow was left with a `TaskScheduled` but no matching `TaskCompleted`: its history was missing the event that the activity had already produced.\r\n\r\n### Impact\r\n\r\nAny deployment running workflows on a multi-replica daprd is affected whenever placement loses its leader.\r\nThis is most visible during routine operations such as placement pod rolling updates, node drains, OOM-driven restarts, or transient daprd-placement network instability.\r\n\r\nEven after placement stabilises, the affected workflow does not recover.\r\n\r\nVisible symptoms include:\r\n\r\n- `dapr workflow list` shows a workflow stuck in `RUNNING` with no recent activity.\r\n- `dapr workflow history` shows the orchestration paused on a `TaskScheduled` with no following `TaskCompleted`.\r\n- The activity body itself ran successfully and its result reached `addWorkflowEvent`; the row only disappeared after the colliding save.\r\n- `dapr scheduler list` shows no pending reminders for the affected workflow, because the wake-up reminder was consumed by the colliding save's transaction.\r\n- The only known remediation was to terminate and restart the workflow.\r\n\r\n### Root Cause\r\n\r\nThe workflow state save path performed a blind upsert on every row.\r\nWhen daprd A loaded at row version E1, modified state locally, and saved, the state store bumped the version to E2.\r\nIf daprd B's load began before daprd A's commit was visible, B's state-store snapshot also saw E1; its later save wrote without a version check.\r\nBoth daprds computed `history-NNNNNN` offsets against the same history each thought was authoritative, so the two writes targeted the same row.\r\nThe later write replaced the earlier write byte-for-byte, dropping whatever event the earlier daprd had appended at that offset.\r\n\r\n### Solution\r\n\r\nWorkflow state saves now use optimistic concurrency on a single version anchor: the metadata row's state-store ETag.\r\n\r\n- `LoadWorkflowState` captures the metadata row's ETag returned by the state store on every load and caches it alongside the rest of the state.\r\n- `GetSaveRequest` attaches that ETag to the metadata `TransactionalUpsert` inside the transactional save request.\r\n  Other rows in the same transaction (history events, custom status, signatures) stay blind, but the state-store `Multi` is atomic, so a mismatch on metadata aborts the entire transaction without touching any row.\r\n- When a save returns `ETagMismatch`, the orchestrator invalidates its cached state and surfaces the error as recoverable.\r\n  The existing reminder-retry path re-fires the operation, the next load reads fresh state with the new ETag, and the retry succeeds against the up-to-date base state.\r\n- After a successful save, the orchestrator refreshes the metadata ETag via a single-key Get so the next save has the correct token without paying for a full state reload.\r\n\r\n## daprd shuts down and stays unready after a brief scheduler outage\r\n\r\n### Problem\r\n\r\nWhen the scheduler control plane was unreachable at the moment daprd opened (or re-opened) its `WatchHosts` stream, the first response from the scheduler could be a gRPC error code such as `Unavailable`, `Internal`, or a server-side `Canceled` rather than a hosts list.\r\nThe runtime's `WatchHosts` loop returned that error directly instead of treating it as a transient peer issue, so the failure propagated up the runtime's top-level `RunnerCloserManager`.\r\nBecause that manager treats any runner exit as terminal for the whole process, daprd then tore down the actor runtime, the workflow backend, and the gRPC servers.\r\n\r\nIf a closer on that path was waiting on a control-plane peer that was also still unreachable (placement, the state store, an actor halt), the shutdown did not complete cleanly.\r\ndaprd stayed in the process table with its readiness probe failing on `dapr is not ready: [grpc-internal-server grpc-api-server]`, but never exited cleanly enough for Kubernetes to restart it on its own.\r\n\r\n### Impact\r\n\r\nAny deployment whose scheduler pods can experience a brief outage at the same time daprd happens to re-open its `WatchHosts` stream was affected.\r\nThis was most visible during routine operations that disrupt the scheduler and placement control planes simultaneously, including rolling control-plane updates, node drains, OOM-driven control-plane restarts, and transient daprd-control-plane network instability.\r\n\r\nVisible symptoms included:\r\n\r\n- daprd logging `Actor runtime shutting down`, `Placement client shutting down`, and `Dapr is shutting down` shortly after the control plane became reachable again, despite no SIGTERM or explicit shutdown trigger from the operator.\r\n- Sidecar readiness probes failing with `dapr is not ready: [grpc-internal-server grpc-api-server]` and not recovering.\r\n\r\n### Root Cause\r\n\r\n`pkg/runtime/scheduler/internal/watchhosts/watchhosts.go` only recognised two gRPC status codes from the first `Recv()` on a freshly opened `WatchHosts` stream: `Unimplemented` (the old-server compatibility path) and `Canceled` (which it interpreted as the daprd-side context being cancelled).\r\nEvery other code returned from the function.\r\n\r\n`Scheduler.Run` wrapped that loop in a plain `RunnerManager`, so the error reached the runtime's top-level `RunnerCloserManager` unmodified.\r\nThe top-level manager treats any runner exit as terminal: every sibling runner was cancelled (`actors.Run`, `wfengine.Run`, `jobsManager.Run`, both gRPC servers), and the closer chain ran before the control plane finished stabilising.\r\nIf the state store, placement loop, or actor table closers blocked waiting on a peer that was also still unreachable, the process stalled with its servers already torn down.\r\n\r\n### Solution\r\n\r\n`WatchHosts.Run` now treats any non-`Unimplemented` error from the first `Recv()` as a transient peer issue.\r\nThe connection is closed and the loop reconnects after a one second pause, matching the existing behaviour for errors observed on the long-lived second `Recv()`.\r\nThe loop only returns when daprd's own context is cancelled (real shutdown), so a brief scheduler outage no longer cascades into a runtime 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WorkItem dispatch by @dapr-bot in https://github.com/dapr/dapr/pull/9920\r\n* [Backport release-1.18] fix: add IPv6 and dual-stack support to GetHostAddress() by @dapr-bot in https://github.com/dapr/dapr/pull/9922\r\n* [Backport release-1.18] fix(pubsub): restore bulk subscribe batching, add immediate-flush feature dispatch by @dapr-bot in https://github.com/dapr/dapr/pull/9928\r\n* [Backport release-1.18] feat(mcp): add built-in mcp wf orchestration engine for mcpserver crd by @dapr-bot in https://github.com/dapr/dapr/pull/9931\r\n* [Backport release-1.18] Workflow payload size metrics by @dapr-bot in https://github.com/dapr/dapr/pull/9935\r\n\r\n\r\n**Full Changelog**: https://github.com/dapr/dapr/compare/v1.18.0-rc.2...v1.18.0-rc.3","mentions_count":1}]