Collections

Overview

A collection provides an iterable interface to a group of resources. Collections behave similarly to Django QuerySets and expose a similar API. A collection seamlessly handles pagination for you, making it possible to easily iterate over all items from all pages of data. Example of a collection:

# SQS list all queues
sqs = boto3.resource('sqs')
for queue in sqs.queues.all():
    print(queue.url)

When Collections Make Requests

Collections can be created and manipulated without any request being made to the underlying service. A collection makes a remote service request under the following conditions:

  • Iteration:

    for bucket in s3.buckets.all():
        print(bucket.name)
    
  • Conversion to list():

    buckets = list(s3.buckets.all())
    
  • Batch actions (see below):

    s3.Bucket('my-bucket').objects.delete()
    

Filtering

Some collections support extra arguments to filter the returned data set, which are passed into the underlying service operation. Use the filter() method to filter the results:

# S3 list all keys with the prefix 'photos/'
s3 = boto3.resource('s3')
for bucket in s3.buckets.all():
    for obj in bucket.objects.filter(Prefix='photos/'):
        print('{0}:{1}'.format(bucket.name, obj.key))

Warning

Behind the scenes, the above example will call ListBuckets, ListObjects, and HeadObject many times. If you have a large number of S3 objects then this could incur a significant cost.

Chainability

Collection methods are chainable. They return copies of the collection rather than modifying the collection, including a deep copy of any associated operation parameters. For example, this allows you to build up multiple collections from a base which they all have in common:

# EC2 find instances
ec2 = boto3.resource('ec2')
base = ec2.instances.filter(InstanceIds=['id1', 'id2', 'id3'])

filters = [{
    'name': 'tenancy',
    'value': 'dedicated'
}]
filtered1 = base.filter(Filters=filters)

# Note, this does NOT modify the filters in ``filtered1``!
filters.append({'name': 'instance-type', 'value': 't1.micro'})
filtered2 = base.filter(Filters=filters)

print('All instances:')
for instance in base:
    print(instance.id)

print('Dedicated instances:')
for instance in filtered1:
    print(instance.id)

print('Dedicated micro instances:')
for instance in filtered2:
    print(instance.id)

Limiting Results

It is possible to limit the number of items returned from a collection by using either the limit() method:

# S3 iterate over first ten buckets
for bucket in s3.buckets.limit(10):
    print(bucket.name)

In both cases, up to 10 items total will be returned. If you do not have 10 buckets, then all of your buckets will be returned.

Controlling Page Size

Collections automatically handle paging through results, but you may want to control the number of items returned from a single service operation call. You can do so using the page_size() method:

# S3 iterate over all objects 100 at a time
for obj in bucket.objects.page_size(100):
    print(obj.key)

By default, S3 will return 1000 objects at a time, so the above code would let you process the items in smaller batches, which could be beneficial for slow or unreliable internet connections.

Batch Actions

Some collections support batch actions, which are actions that operate on an entire page of results at a time. They will automatically handle pagination:

# S3 delete everything in `my-bucket`
s3 = boto3.resource('s3')
s3.buckets('my-bucket').objects.delete()

Danger

The above example will completely erase all data in the my-bucket bucket! Please be careful with batch actions.