资源，客户端和会话之间的boto3差异？

I am using Python 2.7.12 in Ubuntu 16.04 LTS. I’m learning how to use boto3 from the following link: https://boto3.readthedocs.io/en/latest/guide/quickstart.html#using-boto-3. My doubt is when to use resource, client, or session, and their respective functionality.

Here’s some more detailed information on what Client, Resource, and Session are all about.

Client:

• low-level AWS service access
• generated from AWS service description
• exposes botocore client to the developer
• typically maps 1:1 with the AWS service API
• all AWS service operations are supported by clients
• snake-cased method names (e.g. ListBuckets API => list_buckets method)

Here’s an example of client-level access to an S3 bucket’s objects (at most 1000**):

import boto3

client = boto3.client('s3')
response = client.list_objects_v2(Bucket='mybucket')
for content in response['Contents']:
    obj_dict = client.get_object(Bucket='mybucket', Key=content['Key'])
    print(content['Key'], obj_dict['LastModified'])

** you would have to use a paginator, or implement your own loop, calling list_objects() repeatedly with a continuation marker if there were more than 1000.

Resource:

• higher-level, object-oriented API
• generated from resource description
• uses identifiers and attributes
• has actions (operations on resources)
• exposes subresources and collections of AWS resources
• does not provide 100% API coverage of AWS services

Here’s the equivalent example using resource-level access to an S3 bucket’s objects (all):

import boto3

s3 = boto3.resource('s3')
bucket = s3.Bucket('mybucket')
for obj in bucket.objects.all():
    print(obj.key, obj.last_modified)

Note that in this case you do not have to make a second API call to get the objects; they’re available to you as a collection on the bucket. These collections of subresources are lazily-loaded.

You can see that the Resource version of the code is much simpler, more compact, and has more capability (it does pagination for you). The Client version of the code would actually be more complicated than shown above if you wanted to include pagination.

Session:

• stores configuration information (primarily credentials and selected region)
• allows you to create service clients and resources
• boto3 creates a default session for you when needed

A useful resource to learn more about these boto3 concepts is the introductory re:Invent video.

I’ll try and explain it as simple as possible. So there is no guarantee of the accuracy of the actual terms.

Session is where to initiate the connectivity to AWS services. E.g. following is default session that uses the default credential profile(e.g. ~/.aws/credentials, or assume your EC2 using IAM instance profile )

sqs = boto3.client('sqs')
s3 = boto3.resource('s3')

Because default session is limit to the profile or instance profile used, sometimes you need to use the custom session to override the default session configuration (e.g. region_name, endpoint_url, etc. ) e.g.

# custom resource session must use boto3.Session to do the override
my_west_session = boto3.Session(region_name = 'us-west-2')
my_east_session = boto3.Session(region_name = 'us-east-1')
backup_s3 = my_west_session.resource('s3')
video_s3 = my_east_session.resource('s3')

# you have two choices of create custom client session. 
backup_s3c = my_west_session.client('s3')
video_s3c = boto3.client("s3", region_name = 'us-east-1')

Resource : This is the high-level service class recommended to be used. This allows you to tied particular AWS resources and passes it along, so you just use this abstraction than worry which target services are pointed to. As you notice from the session part, if you have a custom session, you just pass this abstract object than worrying about all custom regions,etc to pass along. Following is a complicated example E.g.

import boto3 
my_west_session = boto3.Session(region_name = 'us-west-2')
my_east_session = boto3.Session(region_name = 'us-east-1')
backup_s3 = my_west_session.resource("s3")
video_s3 = my_east_session.resource("s3")
backup_bucket = backup_s3.Bucket('backupbucket') 
video_bucket = video_s3.Bucket('videobucket')

# just pass the instantiated bucket object
def list_bucket_contents(bucket):
   for object in bucket.objects.all():
      print(object.key)

list_bucket_contents(backup_bucket)
list_bucket_contents(video_bucket)

Client is a low level class object. For each client call, you need to explicitly specify the targeting resources, the designated service target name must be pass long. You will lose the abstraction ability.

For example, if you only deal with the default session, this looks similar to boto3.resource.

import boto3 
s3 = boto3.client('s3')

def list_bucket_contents(bucket_name):
   for object in s3.list_objects_v2(Bucket=bucket_name) :
      print(object.key)

list_bucket_contents('Mybucket') 

However, if you want to list objects from a bucket in different regions, you need to specify the explicit bucket parameter required for the client.

import boto3 
backup_s3 = my_west_session.client('s3',region_name = 'us-west-2')
video_s3 = my_east_session.client('s3',region_name = 'us-east-1')

# you must pass boto3.Session.client and the bucket name 
def list_bucket_contents(s3session, bucket_name):
   response = s3session.list_objects_v2(Bucket=bucket_name)
   if 'Contents' in response:
     for obj in response['Contents']:
        print(obj['key'])

list_bucket_contents(backup_s3, 'backupbucket')
list_bucket_contents(video_s3 , 'videobucket')