This page describes how to create and customize a push queue, and how to examine the contents of a queue.
Using a queue configuration file to create queues
To process a task, you must add it to a push queue. App Engine provides a
default push queue, named
default, which is configured and
ready to use with default settings. If you want, you can just add all your tasks
to the default queue, without having to create and configure other queues.
To add queues or change the default configuration, edit the queue configuration file for your application, which you upload to App Engine. You can create up to 100 queues. Queues cannot be created dynamically.
This queue configuration file defines two queues:
queue: - name: queue-blue target: v2.task-module rate: 5/s - name: queue-red rate: 1/s
To upload the file:
gcloud app deploy queue.yaml
All tasks added to
queue-blue are sent to the target module
The refresh rate of
queue-red is changed from 5/s to 1/s. Tasks will be
dequeued and sent to their targets at the rate of 1 task per second.
If you delete a queue, you must wait approximately 7 days before creating a new queue with the same name.
There are many other parameters that can be added to the configuration file to customize the behavior of a push queue. For more information, see the queue configuration file reference.
Defining the push queue processing rate
The task queue uses token buckets to
control the rate of task execution. Each named queue has a token bucket that
holds tokens, up to the maximum specified by the
bucket_size, or a maximum of
5 tokens if you don't specify the bucket size.
Each time your application executes a task, a token is removed from the bucket.
Your app continues processing tasks in the queue until the queue's bucket runs
out of tokens. App Engine refills the bucket with new tokens continuously based
rate that you specified for the queue.
If your queue contains tasks to process, and the queue's bucket contains tokens, App Engine simultaneously processes as many tasks as there are tokens. This can lead to bursts of processing, consuming system resources and competing with user-serving requests.
If you want to prevent too many tasks from running at once or to prevent
datastore contention, use
The following sample shows how to set
max_concurrent_requests to limit
tasks and also shows how to adjust the bucket size and rate based on your
application's needs and available resources:
queue: - name: queue-blue rate: 20/s bucket_size: 40 max_concurrent_requests: 10
Setting storage limits for all queues
You can use your queue configuration file to define the total amount of storage that task data
can consume over all queues. To define the total storage limit, include an
at the top level:
# Set the total storage limit for all queues to 120MB total_storage_limit: 120M queue: - name: queue-blue rate: 35/s
The value is a number followed by a unit:
B for bytes,
K for kilobytes,
G for gigabytes,
T for terabytes. For example,
specifies a limit of 100 kilobytes. If adding a task would cause the queue to
exceed its storage limit, the call to add the task will fail. The default limit
500M (500 megabytes) for free apps. For billed apps there is no limit until
you explicitly set one. You can use this limit to protect your app from a fork
programming error in which each task adds multiple other tasks during its
If your app is receiving errors for insufficient quota when adding tasks, increasing the total storage limit can help. If you are using this feature, we strongly recommend setting a limit that corresponds to the storage required for several days' worth of tasks. This allows the queues to be temporarily backed up and continue to accept new tasks while working through the backlog while still being protected from a fork bomb programming error.
Configuring the maximum number of concurrent requests
You can control the processing rate by setting
max_concurrent_requests, which limits the number of tasks that can execute
If your application queue has a rate of 20/s and a bucket size of 40, tasks in that queue execute at a rate of 20/s and can burst up to 40/s briefly. These settings work fine if task latency is relatively low; however, if latency increases significantly, you'll end up processing significantly more concurrent tasks. This extra processing load can consume extra instances and slow down your application.
For example, let's assume that your normal task latency is 0.3 seconds. At this
latency, you'll process at most around 40 tasks simultaneously. But if your task
latency increases to 5 seconds, you could easily have over 100 tasks processing
at once. This increase forces your application to consume more instances to
process the extra tasks, potentially slowing down the entire application and
interfering with user requests. You can avoid this possibility by setting
max_concurrent_requests to a lower value.
For example, if you set
max_concurrent_requests to 10, our
example queue maintains about 20 tasks/second when latency is 0.3 seconds.
When the latency increases over 0.5 seconds, this setting throttles the
processing rate to ensure that no more than 10 tasks run simultaneously.
queue: # Set the max number of concurrent requests to 50 - name: optimize-queue rate: 20/s bucket_size: 40 max_concurrent_requests: 10
Monitoring queues in the Google Cloud console
In the Google Cloud console, go to the Cloud Tasks page.
Note that if you go to the App Engine Task queue page, there will be instructions that guide you to the Cloud Tasks page. This update in the Google Cloud console does not change how Task queues function.
Enable the Cloud Tasks API.
Once you're in the Cloud Tasks page, you will see a list of all of the queues in the application. Clicking on a the name of a queue brings up the Queue Details page, which shows all of the tasks in the selected queue.
Learn about creating tasks.