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Queues & Concurrency

You can use queues to run many workflows at once with managed concurrency. Queues provide flow control, letting you manage how many workflows run at once or how often workflows are started.

To create a queue, specify its name:

from dbos import Queue

queue = Queue("example_queue")

You can then enqueue any DBOS workflow or step. Enqueuing a function submits it for execution and returns a handle to it. Queued tasks are started in first-in, first-out (FIFO) order.

queue = Queue("example_queue")

@DBOS.workflow()
def process_task(task):
  ...

task = ...
handle = queue.enqueue(process_task, task)

Queue Example​

Here's an example of a workflow using a queue to process tasks concurrently:

from dbos import DBOS, Queue

queue = Queue("example_queue")

@DBOS.workflow()
def process_task(task):
  ...

@DBOS.workflow()
def process_tasks(tasks):
  task_handles = []
  # Enqueue each task so all tasks are processed concurrently.
  for task in tasks:
    handle = queue.enqueue(process_task, task)
    task_handles.append(handle)
  # Wait for each task to complete and retrieve its result.
  # Return the results of all tasks.
  return [handle.get_result() for handle in task_handles]

Managing Concurrency​

You can control how many workflows from a queue run simultaneously by configuring concurrency limits. This helps prevent resource exhaustion when workflows consume significant memory or processing power.

Worker Concurrency​

Worker concurrency sets the maximum number of workflows from a queue that can run concurrently on a single DBOS process. This is particularly useful for resource-intensive workflows to avoid exhausting the resources of any process. For example, this queue has a worker concurrency of 5, so each process will run at most 5 workflows from this queue simultaneously:

from dbos import Queue

queue = Queue("example_queue", worker_concurrency=5)

Global Concurrency​

Global concurrency limits the total number of workflows from a queue that can run concurrently across all DBOS processes in your application. For example, this queue will have a maximum of 10 workflows running simultaneously across your entire application.

warning

Worker concurrency limits are recommended for most use cases. Take care when using a global concurrency limit as any PENDING workflow on the queue counts toward the limit, including workflows from previous application versions

from dbos import Queue

queue = Queue("example_queue", concurrency=10)

Rate Limiting​

You can set rate limits for a queue, limiting the number of functions that it can start in a given period. Rate limits are global across all DBOS processes using this queue. For example, this queue has a limit of 50 with a period of 30 seconds, so it may not start more than 50 functions in 30 seconds:

queue = Queue("example_queue", limiter={"limit": 50, "period": 30})

Rate limits are especially useful when working with a rate-limited API, such as many LLM APIs.

In-Order Processing​

You can use a queue with concurrency=1 to guarantee sequential, in-order processing of events. Only a single event will be processed at a time. For example, this app processes events sequentially in the order of their arrival:

from fastapi import FastAPI
from dbos import DBOS, Queue

app = FastAPI()
DBOS(fastapi=app)

queue = Queue("in_order_queue", concurrency=1)

@DBOS.step()
def process_event(event: str):
   ...

@app.post("/events/{event}")
def event_endpoint(event: str):
   queue.enqueue(process_event, event)

Setting Timeouts​

You can set a timeout for an enqueued workflow with SetWorkflowTimeout. When the timeout expires, the workflow and all its children are cancelled. Cancelling a workflow sets its status to CANCELLED and preempts its execution at the beginning of its next step.

Timeouts are start-to-completion: a workflow's timeout does not begin until the workflow is dequeued and starts execution. Also, timeouts are durable: they are stored in the database and persist across restarts, so workflows can have very long timeouts.

Example syntax:

@DBOS.workflow()
def example_workflow():
    ...

queue = Queue("example-queue")

# If the workflow does not complete within 10 seconds after being dequeued, it times out and is cancelled
with SetWorkflowTimeout(10):
    queue.enqueue(example_workflow)

Deduplication​

You can set a deduplication ID for an enqueued workflow with SetEnqueueOptions. At any given time, only one workflow with a specific deduplication ID can be enqueued in the specified queue. If a workflow with a deduplication ID is currently enqueued or actively executing (status ENQUEUED or PENDING), subsequent workflow enqueue attempt with the same deduplication ID in the same queue will raise a DBOSQueueDeduplicatedError exception.

For example, this is useful if you only want to have one workflow active at a time per user—set the deduplication ID to the user's ID.

Example syntax:

from dbos import DBOS, Queue, SetEnqueueOptions
from dbos import error as dboserror

with SetEnqueueOptions(deduplication_id="my_dedup_id"):
    try:
        handle = queue.enqueue(example_workflow, ...)
    except dboserror.DBOSQueueDeduplicatedError as e:
        # Handle deduplication error

Priority​

You can set a priority for an enqueued workflow with SetEnqueueOptions. Workflows with the same priority are dequeued in FIFO (first in, first out) order. Priority values can range from 1 to 2,147,483,647, where a low number indicates a higher priority. If using priority, you must set priority_enabled=True on your queue.

tip

Workflows without assigned priorities have the highest priority and are dequeued before workflows with assigned priorities.

Example syntax:

queue = Queue("priority_queue", priority_enabled=True)

with SetEnqueueOptions(priority=10):
    # All workflows are enqueued with priority set to 10
    # They will be dequeued in FIFO order
    for task in tasks:
        queue.enqueue(task_workflow, task)

with SetEnqueueOptions(priority=1):
    queue.enqueue(first_workflow)

# first_workflow (priority=1) will be dequeued before all task_workflows (priority=10)

Enqueueing with DBOS Client​

You can use the DBOS Client to enqueue workflows from outside your DBOS application by connecting to Postgres directly.

Since the DBOS Client is designed to be used from outside your DBOS application, workflow and queue metadata must be specified explicitly.

Example:

from dbos import DBOSClient, EnqueueOptions

client = DBOSClient(os.environ["DBOS_DATABASE_URL"])

options: EnqueueOptions = {
  "queue_name": "process_task",
  "workflow_name": "example_queue",
}
handle = client.enqueue(options, task)
result = handle.get_result()