Home NewsX Dapr v1.14.4 now available in the Dapr extension for AKS and Arc-enabled Kubernetes

Dapr v1.14.4 now available in the Dapr extension for AKS and Arc-enabled Kubernetes

by info.odysseyx@gmail.com
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Dapr extension for Kubernetes with AKS and Arc support now supports Dapr v1.14.4.

Daphne A developer framework for building cloud-native applications, making it easier to run multiple microservices on Kubernetes and interact with external state stores/databases, secret stores, pub/sub brokers, other cloud services, and self-hosted solutions.

The Dapr v1.14 release brings several new features, including a new Jobs API for scheduling and executing jobs, a new Scheduler control plane service, streaming subscriptions, Actor multi-tenancy via namespaces, outbox pattern prediction, HTTP metric path matching, and many fixes to core runtime and components.

Review the level of support provided by the Dapr extension.

highlights

The v1.14 release includes several important highlights:

Predicting incoming messages (stable)

Now stable Transactional Outbox characteristic A single transaction can be committed across multiple pub/sub brokers and databases.

Service calls to non-Dapr endpoints (stable)

The ability to call non-Dapr endpoints using the Dapr service invocation API is now stable.

Jobs API and Scheduler Service (Preview)

Many applications require job scheduling for scenarios including automated database backups, regular data processing and ETL, email notifications, maintenance tasks, system updates, and batch processing. The new Jobs API is an orchestrator for scheduling these future jobs at specific times or at specific intervals.

Scheduler Service (Preview)

that Scheduler Service It’s new Control Plane Services Used for scheduling tasks running in self-hosted mode or on Kubernetes. Install using Dapr CLI to manage scheduled tasks.

Improved throughput and scalability for actors and workflows (preview)

You can use the new Scheduler service as a backend for actor notifications to increase throughput and reduce latency for both actors and workflows. All you need to do is enable the preview service. In Dapr configuration Resources like:

apiVersion: dapr.io/v1alpha1
kind: Configuration
metadata:
  name: featureconfig
spec:
  features:
    - name: SchedulerReminders
      enabled: true

memo: Previous notification data is not compatible with the scheduler service.

Streaming Subscriptions (Preview)

Streaming Subscriptions A new type of dynamic subscription written in code. Streaming subscriptions allow you to add or remove subscriptions at runtime. They do not require:
  • This can be easily configured in code using the application’s subscription HTTP endpoint (typically required for current programmatic and declarative subscriptions).
  • This is an app that allows you to receive messages by configuring it as a sidecar.

Actor multi-tenancy using namespaces

Dapr’s namespace provides: isolationAnd therefore multi-tenancy. no wayctor namespaceThe same actor type can be deployed in different namespaces. Then instances of these actors can be called within the same namespace.

memo: Each namespace actor deployment should use its own separate state store, especially if the same actor type is used across multiple namespaces.

Filtering HTTP metrics by path matching

When calling Dapr using HTTP, Metric Basically, it is generated for each method requested and can contain error rate, latency and throughput numbers. This can result in high cardinality, which can impact memory usage and CPU.

HTTP Metric Path Match Dapr allows you to manage and control the base of HTTP metrics. Instead of having metrics for each event, you can reduce the number of metric events and report the overall number.

Dapr Share

Although not part of the Dapr runtime v1.14 release, Dapr Shared provides an alternative distribution option for Dapr.By default, Dapr is injected as a sidecar. To provide the best availability and reliability for the Dapr API, applications. Dapr Shared enables two alternative deployment strategies to reduce resource usage in the cluster and provide a simpler test environment.
  • Using Kubernetes Daemonset for per-node deployment
  • Using Cluster-wise distribution deployment.

Feature and component updates

This release includes updates to many features and component statuses. See the release notes for a full list.





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