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lobotomy

Boto3 low-level client mocking library.

    0.3.14

Maintainers
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Lobotomy

PyPI version build status coverage report Code style: black Code style: flake8 Code style: mypy Code style: pydocstyle Code style: radon PyPI - License

The lo•boto•my library allows one to mock the low-level boto3 client libraries efficiently, especially in more complex scenario testing situations, using configuration-based response definitions. The benefit is a separation of the configuration from the test execution, which cleans up the test invocation process.

Installation

lobotomy is available on pypi and installable via pip:

$ pip install lobotomy

or via poetry as a development dependency in a project:

$ poetry add lobotomy -D

Tl;dr Usage

Create a configuration file in YAML, TOML, or JSON format with a root clients key. Beneath that key add the desired client calls mocked responses by service as shown here for a session.client('s3').get_object() mocked response:

clients:
  s3:
    get_object:
      Body: 'The contents of my S3 file.'
      LastModified: '2020-12-01T01:02:03Z'

Then in the test notice that the lobotomy patching process handles the rest, including casting the configuration values specified in the call above into the more complex data types returned for the specific call.

import lobotomy
import pathlib
import boto3
import datetime

my_directory = pathlib.Path(__file__).parent

@lobotomy.patch(my_directory.joinpath("test_lobotomy.yaml"))
def test_lobotomy(lobotomized: lobotomy.Lobotomy):
    """
    Should return the mocked get_object response generated from the
    configuration data specified in the lobotomy patch above. By default
    the patch(...) applies to the boto3.Session object, so calling 
    boto3.Session() will create a lobotomy.Session instead of a normal
    boto3.Session. From there the low-level client interface is designed
    to match normal usage.
    """
    s3_client = boto3.Session().client("s3")
    
    # Lobotomy will validate that you have specified the required keys
    # in your request, so Bucket and Key have to be supplied here even though
    # they are not meaningful values in this particular test scenario.
    response = s3_client.get_object(Bucket="foo", Key="bar")

    expected = b"The contents of my S3 file."
    assert response["Body"].read() == expected, """
        Expected the mocked response body data to be returned as a
        StreamingBody object with blob/bytes contents. The lobotomy
        library introspects boto to properly convert the string body
        value in the configuration file into the expected return format
        for the particular call.
        """
    
    expected = datetime.datetime(2020, 12, 1, 1, 2, 3, 0, datetime.timezone.utc)
    assert response["LastModified"] == expected, """
        Expected the mocked response last modified value to be a timezone-aware
        datetime value generated from the string timestamp value in the
        configuration file to match how it would be returned by boto in
        an actual response.
        """

    call = lobotomized.get_service_call("s3", "get_object")
    assert call.request["Bucket"] == "foo", """
        Expected the s3.get_object method call arguments to have specified the bucket
        as "foo".
        """
    assert call.request["Key"] == "bar", """
        Expected the s3.get_object method call argumets to have specified the key as
        "bar".
        """

Usage

Configuration Files

Test scenarios can be written in YAML, TOML, or JSON formats. YAML or TOML are recommended unless copying output responses from JSON calls is easier for a given use-case. In the following example, we'll define the calls using YAML:

clients:
  sts:
    get_caller_identity:
      Account: '987654321'
  s3:
    get_object:
      Body: 'The contents of my S3 file.'
      LastModified: '2020-11-01T12:23:34Z'

All call responses are stored within the root clients attribute with service names as sub-attributes and then the service method call responses defined beneath the service name attributes. Multiple services and multiple methods per service are specified in this way by the hierarchical key lists.

If the contents of a method response definition are not a list, the same response will be returned for each call to that client method during the test. Specifying a list of responses will alter the behavior such that each successive call will iterate through that list of configured responses.

clients:
  s3:
    get_object:
    - Body: 'The contents of my S3 file.'
      LastModified: '2020-11-01T12:23:34Z'
    - Body: 'Another S3 file.'
      LastModified: '2020-11-03T12:23:34Z'

The lobotomy library dynamically inspects boto for its response structure and data types, such that they match the types for the normal boto response for each client method call. In the case of the session.client('s3').get_object() calls above, the Body would return a StreamingBody object with the string converted to bytes to match the normal output. Similarly, the LastModified would be converted to a timezone-aware datetime object by lobotomy as well. This makes it easy to specify primitive data types in the configuration file that are transformed into their more complex counterparts when returned within the execution of the actual code.

Test Patching

Once the configuration file has been specified, it is used within a test via a lobotomy.patch as shown below.

import lobotomy
import pathlib
import boto3
import datetime

my_directory = pathlib.Path(__file__).parent

@lobotomy.patch(my_directory.joinpath("test_lobotomy.yaml"))
def test_lobotomy(lobotomized: lobotomy.Lobotomy):
    """
    Should return the mocked get_object response generated from the
    configuration data specified in the lobotomy patch above. By default
    the patch(...) applies to the boto3.Session object, so calling 
    boto3.Session() will create a lobotomy.Session instead of a normal
    boto3.Session. From there the low-level client interface is designed
    to match normal usage.
    """
    s3_client = boto3.Session().client("s3")
    
    # Lobotomy will validate that you have specified the required keys
    # in your request, so Bucket and Key have to be supplied here even though
    # they are not meaningful values in this particular test scenario.
    response = s3_client.get_object(Bucket="foo", Key="bar")

    expected = b"The contents of my S3 file."
    assert response['Body'].read() == expected, """
        Expect the mocked response body data to be returned as a
        StreamingBody object with blob/bytes contents. The lobotomy
        library introspects boto to properly convert the string body
        value in the configuration file into the expected return format
        for the particular call.
        """
    
    expected = datetime.datetime(2020, 12, 1, 1, 2, 3, 0, datetime.timezone.utc)
    assert response['LastModified'] == expected, """
        Expect the mocked response last modified value to be a timezeon-aware
        datetime value generated from the string timestamp value in the
        configuration file to match how it would be returned by boto in
        an actual response.
        """

The patching process replaces the boto3.Session class with a lobotomy.Lobotomy object that contains the loaded configuration data. When patched in this fashion, boto3.Session() calls will actually be lobotomy.Lobotomy() calls that return lobotomy.Session objects. These sessions have the interface of the boto3.Session object, but behave in a way such that client responses are returned from the configuration data instead of through interactivity with AWS.

For simple cases with little configuration, it is also possible to patch data stored directly within the Python code. The above test could be rewritten in this way as:

import lobotomy

configuration = {
    "clients": {
        "s3": {
            "get_object": [
                { 
                    "Body": "The contents of my S3 file.",
                    "LastModified": "2020-11-01T12:23:34Z",
                },
                {
                    "Body": "Another S3 file.",
                    "LastModified": "2020-11-03T12:23:34Z",
                },
            ],
        },
    },
}


@lobotomy.patch(data=configuration)
def test_lobotomy(lobotomized: lobotomy.Lobotomy):
    """..."""

Although one of the benefits of lobotomy is the ability to streamline the tests files by reducing the response configuration, which can be a bit verbose inside Python files and that is the highly recommended approach.

A third option for simpler cases is to use the Lobotomy.add_call() method to register calls and responses.

import lobotomy


@lobotomy.patch()
def test_lobotomy(lobotomized: lobotomy.Lobotomy):
    """..."""
    lobotomized.add_call(
        service_name="s3", 
        method_name="get_object", 
        response={ 
            "Body": "The contents of my S3 file.",
            "LastModified": "2020-11-01T12:23:34Z",
        },
    )
    lobotomized.add_call(
        service_name="s3", 
        method_name="get_object", 
        response={
            "Body": "Another S3 file.",
            "LastModified": "2020-11-03T12:23:34Z",
        },
    )

In the case above no data or path was supplied to the lobotomy.patch() and instead call responses were registered within the test function itself. The response argument in the Lobotomy.add_call() method is optional. If omitted, a default one will be created instead in the same fashion as one would be created via the CLI (see below).

Note that it is also possible to mix loading data and adding calls within the test function.

YAML IO Modifiers

When using YAML files, lobotomy comes with custom YAML classes that can provide even more flexibility and ease in defining data. The following are the available modifiers and how to use them:

to_json

This modifier will convert YAML data into a JSON string in the creation of the response, which makes it easier to represent complex JSON data in the scenario data.

clients:
  secretsmanager:
    get_secret_value:
      SecretString: !lobotomy.to_json
        first: first_value
        second: second_value

In this example the !lobotomy.to_json YAML modifier instructs the lobotomy to converts the object data beneath the SecretString attribute into a JSON string as part of the response object. In this case then in the associated Python test:

import boto3
import lobotomy
import json


@lobotomy.patch(path="scenario.yaml")
def test_showing_to_json(lobotomized: lobotomy.Lobotomy):
    """Should expect a JSON string for the 'SecretString' value."""
    client = boto3.Session().client("secretsmanager")

    response = client.get_secret_value(SecretId="fake")
    
    expected = {"first": "first_value", "second": "second_value"}
    assert expected == json.loads(response["SecretValue"])

the value is returned as the expected JSON string.

inject_string

This modifier is used to inject the string contents of another file into the value of the associated attribute.

clients:
  s3:
    get_object:
      Body: !lobotomy.inject_string './body.txt'

Here the !lobotomy.inject_string YAML modifier instructs lobotomy to load the contents of the external file ./body.txt into the Body attribute value where the external file path is defined relative to the defining YAML file.

So in this case, if there's a body.txt file with the contents Hello lobotomy!, the Python test would find this in reading the body:

import boto3
import lobotomy


@lobotomy.patch(path="scenario.yaml")
def test_showing_inject_string(lobotomized: lobotomy.Lobotomy):
    """Should expect the body.txt to be injected into the Body response attribute."""
    client = boto3.Session().client("s3")
    response = client.get_object(Bucket="fake", Key="fake")
    assert response["Body"].read() == b"Hello lobotomy!"

Special Cases

The following are special case operations that are handled slightly differently than the regular lobotomy behaviors.

s3.download_fileobj

This is a special action that is not actually included in the botocore API. It's a convenience wrapper that has the special behavior of written data to a byte-buffer object instead of returning a value. To handle that, the "Fileobj" response should be populated with either a string or a bytes object. Lobotomy will internally handle writing that to the specified buffer like this:

import boto3
import lobotomy
import io


@lobotomy.Patch()
def test_s3_download_fileobj(lobotomized: lobotomy.Lobotomy):
    """Should write s3.download_fileobj outputs to the Fileobj argument buffer."""
    lobotomized.add_call("s3", "download_fileobj", {"Fileobj": "Hello World!"})
    client = boto3.Session().client("s3")

    data = io.BytesIO()
    client.download_fileobj(Bucket="bar", Key="baz", Fileobj=data)

    call = lobotomized.get_service_calls("s3", "download_fileobj")[0]
    assert call.request["Fileobj"] == data
    assert call.request["Bucket"] == "bar"
    assert call.request["Key"] == "baz"
    assert data.getvalue().decode() == "Hello World!"

Command Line Interface

The lobotomy library also has a command line interface to help streamline the process of creating configuration files. The CLI has an add command that can be used to auto-generate method call response configurations to a new or existing configuration file. The values are meant to be replaced and unused keys to be removed to streamline for testing, but it helps a lot to get the full structure of the response in place and work from there instead of having to look it up yourself.

For example, creating the file:

clients:
  sts:
    get_caller_identity:
      Account: '987654321'
  s3:
    get_object:
    - Body: 'The contents of my S3 file.'
      LastModified: '2020-11-01T12:23:34Z'
    - Body: 'Another S3 file.'
      LastModified: '2020-11-03T12:23:34Z'

could be done first through the CLI commands:

$ lobotomy add sts.get_caller_identity example.yaml

After that command is executed, the example.yaml file will be created and populated initially with:

clients:
  sts:
    get_caller_identity:
      Account: '...'
      Arn: '...'
      UserId: '...'

Notice the values are placeholders. We can adjust the values to what we want and remove the unnecessary keys for our particular case such that the file contents are then:

clients:
  sts:
    get_caller_identity:
      Account: '987654321'

Next add the first s3.get_object call:

$ lobotomy add s3.get_object example.yaml

The configuration file now looks like:

clients:
  s3:
    get_object:
      AcceptRanges: '...'
      Body: '...'
      CacheControl: '...'
      ContentDisposition: '...'
      ContentEncoding: '...'
      ContentLanguage: '...'
      ContentLength: 1
      ContentRange: '...'
      ContentType: '...'
      DeleteMarker: null
      ETag: '...'
      Expiration: '...'
      Expires: '2020-11-04T14:37:18.042821Z'
      LastModified: '2020-11-04T14:37:18.042821Z'
      Metadata: null
      MissingMeta: 1
      ObjectLockLegalHoldStatus: '...'
      ObjectLockMode: '...'
      ObjectLockRetainUntilDate: '2020-11-04T14:37:18.042821Z'
      PartsCount: 1
      ReplicationStatus: '...'
      RequestCharged: '...'
      Restore: '...'
      SSECustomerAlgorithm: '...'
      SSECustomerKeyMD5: '...'
      SSEKMSKeyId: '...'
      ServerSideEncryption: '...'
      StorageClass: '...'
      TagCount: 1
      VersionId: '...'
      WebsiteRedirectLocation: '...'
  sts:
    get_caller_identity:
      Account: '987654321'

Of course, this is a simple case because we don't need much of the response structure in our simplified use-case, but hopefully you can see the value of being able to add the response structure so easily for more complex cases. Once again, the new call is adjusted to fit our particular needs:

clients:
  s3:
    get_object:
      Body: 'The contents of my S3 file.'
      LastModified: '2020-11-01T12:23:34Z'
  sts:
    get_caller_identity:
      Account: '987654321'

Adding the second s3.get_object call is identical:

$ lobotomy add s3.get_object example.yaml

However, lobotomy notices the existing call there and so converts the get_object response configuration to a list of responses for you:

clients:
  s3:
    Body: 'The contents of my S3 file.'
    LastModified: '2020-11-01T12:23:34Z'
    get_object:
      AcceptRanges: '...'
      Body: '...'
      CacheControl: '...'
      ContentDisposition: '...'
      ContentEncoding: '...'
      ContentLanguage: '...'
      ContentLength: 1
      ContentRange: '...'
      ContentType: '...'
      DeleteMarker: null
      ETag: '...'
      Expiration: '...'
      Expires: '2020-11-04T14:42:51.077364Z'
      LastModified: '2020-11-04T14:42:51.077364Z'
      Metadata: null
      MissingMeta: 1
      ObjectLockLegalHoldStatus: '...'
      ObjectLockMode: '...'
      ObjectLockRetainUntilDate: '2020-11-04T14:42:51.077364Z'
      PartsCount: 1
      ReplicationStatus: '...'
      RequestCharged: '...'
      Restore: '...'
      SSECustomerAlgorithm: '...'
      SSECustomerKeyMD5: '...'
      SSEKMSKeyId: '...'
      ServerSideEncryption: '...'
      StorageClass: '...'
      TagCount: 1
      VersionId: '...'
      WebsiteRedirectLocation: '...'
  sts:
    get_caller_identity:
      Account: '987654321'

Finally, edit the new call response configuration and we end up with the configuration we were looking for:

clients:
  sts:
    get_caller_identity:
      Account: '987654321'
  s3:
    get_object:
    - Body: 'The contents of my S3 file.'
      LastModified: '2020-11-01T12:23:34Z'
    - Body: 'Another S3 file.'
      LastModified: '2020-11-03T12:23:34Z'

Advanced Usage

Key Prefixes

By default configuration files are rooted at the clients key within the file. However, it is possible to specify a different root key prefix, which is useful when co-locating lobotomy test configuration with other test configuration data in the same file, or when co-locating multiple lobotomy test configurations within the same file. To achieve that a prefix must be specified during patching.

As an example, consider the configuration file:

lobotomy:
  test_a:
    clients:
      sts:
        get_caller_identity:
          Account: '987654321'
  test_b:
    clients:
      sts:
        get_caller_identity:
          Account: '123456678'
          UserId: 'AIFASDJWISJAVHXME'

In this case the prefixes are lobotomy.test_a and lobotomy.test_b. To use these in a test the prefix must be specified in the patch:

import pathlib
import lobotomy

config_path = pathlib.Path(__file__).parent.joinpath("validation.yaml")


@lobotomy.patch(config_path, prefix="lobotomy.test_a")
def test_a(lobotomized: lobotomy.Lobotomy):
    """..."""


@lobotomy.patch(config_path, prefix="lobotomy.test_b")
def test_b(lobotomized: lobotomy.Lobotomy):
    """..."""

The prefix can be specified as a . delimited string, or as a list/tuple. The list/tuple is needed if the keys themselves contains ..

Patching Targets

By default, lobotomy will patch boto3.Session. There are scenarios where a different patch would be desired either to limit the scope of the patch or to patch another library wrapping the boto3.Session call. In those cases, specify the patch path argument:

import pathlib
import lobotomy

config_path = pathlib.Path(__file__).parent.joinpath("test.yaml")


@lobotomy.patch(config_path, patch_path="something.else.Session")
def test_another_patch_path(lobotomized: lobotomy.Lobotomy):
    """..."""

Session Configuration

In addition to the clients configurations described above, it is also possible to configure the session values as well with the session: attribute. The available configuration settings for the session are:

session:
  profile_name: some-profile
  region_name: us-west-2
  available_profiles:
    - some-profile
    - some-other-profile
  credentials:
    access_key: A123KEY
    secret_key: somesecretkeyvalue
    token: theaccesstokenifset
    method: how-the-credentials-were-loaded

clients:
  ...

All of these values are optional and in cases where they are omitted, but the session requires having them, they will be defaulted.

Client Overrides

There are cases where it is desirable to replace one or more service clients with non-lobotomy objects. Lobotomy supports that by adding client overrides to the patched lobotomy object:

from unittest.mock import MagicMock

import lobotomy


@lobotomy.patch()
def test_example(lobotomized: lobotomy.Lobotomy):
    """Should do something..."""
    mock_dynamo_db_client = MagicMock()
    lobotomized.add_client_override("dynamodb", mock_dynamo_db_client)
    # continue testing...

Error Handling

The lobotomy library mimics client error handling with a lobotomized version of the same interface used by the live clients. As such, handling and capturing errors works transparently. For example,

import boto3
import pytest

import lobotomy


@lobotomy.patch()
def test_client_errors(lobotomized: lobotomy.Lobotomy):
    """Should raise the specified error."""
    lobotomized.add_error_call(
        service_name="s3",
        method_name="list_objects",
        error_code="NoSuchBucket", 
        error_message="Hello...",
    )

    session = boto3.Session()
    client = session.client("s3")

    with pytest.raises(client.exceptions.NoSuchBucket):
        client.list_objects(Bucket="foo")

Or the generic client error handling with response codes can be used as well:

import boto3
import pytest

import lobotomy


@lobotomy.patch()
def test_client_errors(lobotomized: lobotomy.Lobotomy):
    """Should raise the specified error."""
    lobotomized.add_call(
        service_name="s3",
        method_name="list_objects",
        error_code="NoSuchBucket", 
        error_message="Hello...",
    )

    session = boto3.Session()
    client = session.client("s3")

    with pytest.raises(lobotomy.ClientError) as exception_info:
        client.list_objects(Bucket="foo")

    assert exception_info.value.response["Error"]["Code"] == "NoSuchBucket"

These errors can also be specified in YAML files like this:

clients:
  s3:
    list_objects: !lobotomy.error
      code: NoSuchBucket
      message: Hello...

The !lobotomy.error will load this as an error response with the same behavior as the examples shown above.

Callable Responses

For more advanced cases it is also possible to use any callable as the source for generating the response. For example, if I want the response to be different based on the arguments specified when making the call, I can create a function to return a result that reflects the inputs.

import typing

import boto3

import lobotomy


def _respond(*args, **kwargs) -> typing.Dict[str, typing.Any]:
    return {
        "Body": "{}.{}".format(kwargs["Bucket"], kwargs["Key"]).encode()
    }


@lobotomy.patch()
def test_callable_responses(lobotomized: "lobotomy.Lobotomy"):
    """Should return the expected body value from the callable response."""
    lobotomized.add_call("s3", "get_object", _respond)

    session = boto3.Session()
    client = session.client("s3")

    response = client.get_object(Bucket="foo", Key="bar")
    assert response["Body"].read() == b"foo.bar"

Any callable is supported as long as it accepts the *args, **kwargs appropriate to the calling client service request.

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