​Worker ​Group

How to configure Worker Groups in Kestra Enterprise Edition.

Worker Group is a set of workers that can be targeted specifically for a task execution or a polling trigger evaluation.

Creating Worker Groups

To create a new worker group, navigate to the Cluster page under the Administration section in the UI, go to the Worker Groups tab and click on the + Create button. Then, set a key (and optionally, also a description) for that worker group. You can accomplish the same via the API, CLI, or Terraform.

Starting Workers for a Worker Group

Once a worker group key is created, you can start a worker with the --worker-group workerGroupKey flag to assign it to that worker group. You can also assign a default worker group at the namespace and tenant level.

The Worker Groups UI tracks the health of worker groups, showing how many workers are polling for tasks within each worker group. This gives you visibility into which worker groups are active, and the number of active workers.

Using Worker Groups

To assign a worker group, add the workerGroup.key property to the task or the polling trigger. A default worker group can also be configured at the namespace or tenant level.

The flow editor validates worker group keys when creating flows from the UI. If the provided key doesn’t exist, the syntax validation will prevent the flow from being saved.

Example flow configuration with a worker group:

id: worker_group
namespace: dev

tasks:
  - id: wait
    type: io.kestra.plugin.scripts.shell.Commands
    taskRunner:
      type: io.kestra.plugin.core.runner.Process
    commands:
      - sleep 10
    workerGroup:
      key: gpu

If the workerGroup.key property is not provided, all tasks and polling triggers are executed on the default worker group. That default worker group doesn't have a dedicated key.

When to use Worker Groups

Here are common use cases in which Worker Groups can be beneficial:

• Execute tasks and polling triggers on specific compute instances (e.g., a VM with a GPU and preconfigured CUDA drivers).
• Execute tasks and polling triggers on a worker with a specific Operating System (e.g., a Windows server).
• Restrict backend access to a set of workers (firewall rules, private networks, etc.).
• Execute tasks and polling triggers close to a remote backend (region selection).

Load balancing

Whether you leverage worker groups or not, Kestra will balance the load across all available workers. The primary difference is that with worker groups, you can target specific workers for task execution or polling trigger evaluation.

A worker is part of a worker group if it is started with --worker-group workerGroupKey.

There's a slight difference between Kafka and JDBC architectures in terms of load balancing:

• The Kafka architecture relies on Kafka consumer group protocol — each worker group will use a different consumer group protocol, therefore each worker group will balance the load independently.
• For JDBC, each worker within a group will poll the queues database table using the same poll query. All workers within the same worker group will poll for task runs and polling triggers in a FIFO manner.

Central Queue to distribute task runs and polling triggers

In both JDBC and Kafka architectures, we leverage a Central Queue to ensure that tasks and polling triggers are executed only once and in the right order.

Here's how it works:

• Jobs (task runs and polling triggers) are submitted to a centralized queue. The queue acts as a holding area for all incoming jobs.
• Workers periodically poll the central queue to check for available jobs. When a worker becomes free, it requests the next job from the queue.
• Kestra backend keeps track of assignment of jobs to workers to ensure reliable execution and prevent duplicate processing.

What if multiple workers from the same worker group poll for jobs from the central queue?

Whether the jobs (task runs and polling triggers) are evenly distributed among workers depends on several factors:

1. The order in which workers poll the queue will affect distribution — workers that poll the queue first will get jobs first (FIFO).
2. Variations in worker compute capabilities (and their processing speeds) can cause uneven job distribution. Faster workers will complete jobs and return to poll the queue more quickly than slower workers.