- Added support for the following APIs:
- put_configuration_aggregator
- describe_configuration_aggregators
- delete_configuration_aggregator
- put_aggregation_authorization
- describe_aggregation_authorizations
- delete_aggregation_authorization
Model: OrganizationsBackend
Method: create_organization
create_organization now creates master account, root ou, and a
default service control policy objects and adds them to the
OrganizationsBackend object. the policy is attached to both
the master account and the root ou. any subsiquently created
accounts or OU also have the default policy attached.
* Add option to call batch submit_job with job definition name only
* Fix bug which causes register_job_definition not to increment job
revision number after a second revision
If a user is attempted to be added to a pool that already contains a user with that username, the UsernameExistsException is thrown, to match AWS behaviour.
* Add INTELLIGENT_TIERING, GLACIER and DEEP_ARCHIVE as valid storage
classes for objects
* Add ObjectNotInActiveTierError error on PUT object copy for GLACIER
and DEEP_ARCHIVE storage class objects
Adding support on DynamoDB Streams for AT_SEQUENCE_NUMBER
and AFTER_SEQUENCE_NUMBER ShardIteratorType
Change SequenceNumber type to string instead of int to match documentation
This test is useful because before the last commit using copy_image
would not set the owner_id to the same one used when calling
describe_images.
For example, this code
conn = boto3.client("ec2")
copy_resp = conn.copy_image(
SourceImageId="ami-whatever",
...
)
describe_resp = conn.describe_images(
Owners=["self"]
)
Would result in describe_resp being empty, when it should contain the
image from the copy_resp before it.
By ensuring the owner ids are the same (see ce4059f6) the code example
now works as expected.
Previously there were a couple models which used different owner ids by
default, which could make tests relying on them fail if someone wasn't
expecting that. This change ensures a uniform owner id between
resources.
There is no reason to use 5M chunks, especially with the reduced_part_size
decorator. Also made part_size to be variable to add extra layer of
testing to make sure that moto is handling that scenario correctly.
Signed-off-by: Ruslan Kuprieiev <ruslan@iterative.ai>
