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Session

Overview

A session manages state about a particular configuration. By default, a session is created for you when needed. However, it's possible and recommended that in some scenarios you maintain your own session. Sessions typically store the following:

  • Credentials
  • AWS Region
  • Other configurations related to your profile

Default session

Boto3 acts as a proxy to the default session. This is created automatically when you create a low-level client or resource client:

import boto3

# Using the default session
sqs = boto3.client('sqs')
s3 = boto3.resource('s3')

Custom session

You can also manage your own session and create low-level clients or resource clients from it:

import boto3
import boto3.session

# Create your own session
my_session = boto3.session.Session()

# Now we can create low-level clients or resource clients from our custom session
sqs = my_session.client('sqs')
s3 = my_session.resource('s3')

Session configurations

You can configure each session with specific credentials, AWS Region information, or profiles. The most common configurations you might use are:

  • aws_access_key_id - A specific AWS access key ID.
  • aws_secret_access_key - A specific AWS secret access key.
  • region_name - The AWS Region where you want to create new connections.
  • profile_name - The profile to use when creating your session.

Note

Only set the profile_name parameter when a specific profile is required for your session. To use the default profile, don’t set the profile_name parameter at all. If the profile_name parameter isn't set and there is no default profile, an empty config dictionary will be used.

For a detailed list of per-session configurations, see the Session core reference.

Multithreading or multiprocessing with sessions

Similar to Resource objects, Session objects are not thread safe and should not be shared across threads and processes. It's recommended to create a new Session object for each thread or process:

import boto3
import boto3.session
import threading

class MyTask(threading.Thread):
    def run(self):
        # Here we create a new session per thread
        session = boto3.session.Session()

        # Next, we create a resource client using our thread's session object
        s3 = session.resource('s3')

        # Put your thread-safe code here

Note

Note that boto3.client uses a single, shared session for all calls. This can lead to concurrency issues unexpectedly when done across parallelization primitives. We recommend managing your own session(s) with concurrent code.