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playback CircleCI codecov PyPi Version Python Versions

A Python decorator-based framework that lets you "record" and "replay" operations (e.g. API requests, workers consuming jobs from queues).

a java-script / type script version is in the works

Main uses

  • Regression testing - replay recorded production traffic on modified code before pushing it
  • Debug production issues locally
  • Access many "real data" scenarios to test/validate new features/behaviours

The framework intercepts all decorated inputs and outputs throughout the recorded operation, which are used later to replay the exact operation in a controlled isolated sandbox, as well as to compare the output of the recorded operation vs the replayed operation.


The motivation for this framework was to be able to test new code changes on actual data from production while doing it not in production, when the alternative of canary deployment is not a viable option. Some examples when this might happen include:

  • When detecting a regression is based on intimate knowledge of the service output
  • When the service amount of possible input permutations is large while the number of users per permutation is low, resulting in a statistical sample that is not large enough to rely on in production in order to detect regression early enough to rollback

On top of this, the ability for the developer to check and get an accurate comparison of his/her code vs production then debug it during development increases productivity by detecting issues right away. The quality of the released code improves significantly by covering many edge cases that are hard to predict in tests.


  • Create a standalone "recording" of each intercepted operation, with all the relevant inputs and outputs, and save it to AWS S3
  • Replay a recorded operation anywhere via code execution
  • Run an extensive comparison of recorded vs replayed operations


pip install playback-studio


There are two examples as part of this repo you can check out under the examples directory:

Usage and examples - interception and replay

Intercepting an operation

In order to intercept an operation, you need to explicitly declare the recorded operation entry point by decorating it with the TapeRecorder.operation decorator and explicitly declare what inputs and outputs need to be intercepted by using the TapeRecorder.intercept_input and TapeRecorder.intercept_output decorators, as demonstrated below:

from flask import request
tape_cassette = S3TapeCassette('production-recordings', region='us-east-1', read_only=False)
tape_recorder = TapeRecorder(tape_cassette)

class ServiceOperation(object):


    def execute(self):
        Executes the operation and return the key of where the result is stored
        data = self.get_request_data()
        result = self.do_something_with_input(data)
        storage_key = self.store_result(result)
        return storage_key

    def get_request_data(self):
        Reads the required input for the operation
        # Get request data from flask

    def store_result(self, result):
        Stores the operation result and return the key that can be used to fetch the result
        result_key = self.put_result_in_mongo(result)
        return result_key

Replaying an intercepted operation

In order to replay an operation, you need the specific recording ID. Typically, you would add this information to your logs output. Later, we will demonstrate how to look for recording IDs using search filters, the Equalizer, and the PlaybackStudio

tape_cassette = S3TapeCassette('production-recordings', region='us-east-1')
tape_recorder = TapeRecorder(tape_cassette)

def playback_function(recording):
    Given a recording, starts the execution of the recorded operation
    operation_class = recording.get_metadata()[TapeRecorder.OPERATION_CLASS]
    return operation_class().execute()

# Will replay recorded operation, injecting and capturing needed data in all of the intercepted inputs and outputs, playback_function)

Framework classes - recording and replaying

TapeRecorder class

This class is used to "record" an operation and "replay" (rerun) the recorded operation on any code version. The recording is done by placing different decorators that intercept the operation and its inputs and outputs by using decorators.

operation decorator

def operation(self, metadata_extractor=None)

Decorates the operation entry point. Every decorated input and output that is being executed within this scope is being intercepted and recorded or replayed, depending on whether the current context is recording or playback.

  • metadata_extractor - an optional function that can be used to add metadata to the recording. The metadata can be used as a search filter when fetching recordings, hence it can be used to add properties specific to the operation received parameters that make sense to filter by when you wish to replay the operation.

intercept_input decorator

def intercept_input(self, alias, alias_params_resolver=None, data_handler=None, capture_args=None, run_intercepted_when_missing=True)

Decorates a function that acts as an input to the operation. The result of the function is the recorded input, and the combined passed arguments and alias are used as the key that uniquely identifies the input. Upon playback, an invocation to the intercepted method will fetch the input from the recording by combining the passed arguments and alias as the lookup key. If no recorded value is found, a RecordingKeyError will be raised.

  • alias - Input alias, used to uniquely identify the input function, hence the name should be unique across all relevant inputs this operation can reach. This should be renamed as it will render previous recording useless
  • alias_params_resolver - Optional function that resolve parameters inside alias if such are given. This is useful when you have the same input method invoked many times with the same arguments on different class instances
  • data_handler - Optional data handler that prepares and restores the input data for and from the recording when default pickle serialization is not enough. This needs to be an implementation of InputInterceptionDataHandler class
  • capture_args - If a list is given, it will annotate which arg indices and/or names should be captured as part of the intercepted key (invocation identification). If None, all args are captured
  • run_intercepted_when_missing - If no matching content is found on recording during playback, run the original intercepted method. This is useful when you want to use existing recording to play a code flow where this interception didn't exist

When intercepting a static method, static_intercept_input should be used.

intercept_output decorator

def intercept_output(self, alias, data_handler=None, fail_on_no_recorded_result=True)

Decorates a function that acts as an output of the operation. The parameters passed to the function are recorded as the output and the return value is recorded as well. The alias combined with the invocation number are used as the key that uniquely identifies this output. Upon playback, an invocation to the intercepted method will construct the same identification key and capture the outputs again (which can be used later to compare against the recorded output), and the recorded return value will be returned.

  • alias - Output alias, used to uniquely identify the input function, hence the name should be unique across all relevant inputs this operation can reach. This should be renamed as it will render previous recording useless
  • data_handler - Optional data handler that prepares and restores the output data for and from the recording when default pickle serialization is not enough. This needs to be an implementation of OutputInterceptionDataHandler class
  • fail_on_no_recorded_result - Whether to fail if there is no recording of a result or return None. Setting this to False is useful when there are already pre-existing recordings and this is a new output interception where we want to be able to playback old recordings and the return value of the output is not actually used. Defaults to True

The return value of the operation is always intercepted as an output implicitly using TapeRecorder.OPERATION_OUTPUT_ALIAS as the output alias.

When intercepting a static method, static_intercept_output should be used.

TapeCassette class

An abstract class that acts as a storage driver for TapeRecorder to store and fetch recordings, the class has three main methods that need to be implemented.

def get_recording(self, recording_id)

Get recording is stored under the given ID

def create_new_recording(self, category)

Creates a new recording object that is used by the tape recorded

  • category - Specifies under which category to create the recording and represent the operation type
def iter_recording_ids(self, category, start_date=None, end_date=None, metadata=None, limit=None)

Creates an iterator of recording IDs matching the given search parameters

  • category - Specifies in which category to look for recordings
  • start_date - Optional earliest date of when recordings were captured
  • end_date - Optional latest date of when recordings were captured
  • metadata - Optional dictionary to filter captured metadata by
  • limit - Optional limit on how many matching recording IDs to fetch

The framework comes with two built-in implementations:

  • InMemoryTapeCassette - Saves recording in a dictionary, its main usage is for tests
  • S3TapeCassette - Saves recording in AWS S3 bucket

S3TapeCassette class

# Instantiate the cassette connected to bucket 'production-recordings'
# under region 'us-east-1' in read/write mode
tape_cassette = S3TapeCassette('production-recordings', region='us-east-1', read_only=False)

Instantiating this class relies on being able to connect to AWS S3 from the current terminal/process and have read/write access to the given bucket (for playback, only read access is needed).

def __init__(self, bucket, key_prefix='', region=None, transient=False, read_only=True,
             infrequent_access_kb_threshold=None, sampling_calculator=None)
  • bucket - AWS S3 bucket name
  • key_prefix - Each recording is saved under two keys, one containing full data and the other just for fast lookup and filtering of recordings. The key structure used for recording is 'tape_recorder_recordings/{key_prefix}<full/metadata>/{id}', this gives the option to add a prefix to the key
  • region - This value is propagated to the underline boto client
  • transient - If this is a transient cassette, all recording under the given prefix will be deleted when closed (only if not read-only). This is useful for testing purposes and clean-up after tests
  • read_only - If True, this cassette can only be used to fetch recordings and not to create new ones. Any write operations will raise an assertion.
  • infrequent_access_kb_threshold - Threshold in KB. When above the threshhold, the object will be saved in STANDARD_IA (infrequent access storage class), None means never (default)
  • sampling_calculator - Optional sampling ratio calculator function. Before saving the recording, this function will be triggered with (category, recording_size, recording) and the function should return a number between 0 and 1 which specifies its sampling rate

Usage and examples - comparing replayed vs recorded operations

Using the Equalizer

In order to run a comparison, we can use the Equalizer class and provide it with relevant playable recordings. In this example, we will look for five recordings from the last week using the find_matching_recording_ids function. The Equalizer relies on:

  • playback_function to replay the recorded operation
  • result_extractor to extract the result that we want to compare from the captured outputs
  • comparator to compare the extracted result
# Creates an iterator over relevant recordings which are ready to be played
lookup_properties = RecordingLookupProperties(start_date=datetime.utcnow() - timedelta(days=7),
recording_ids = find_matching_recording_ids(tape_recorder,

def result_extractor(outputs):
    Given recording or playback outputs, find the relevant output which is the result that
    needs to be compared
    # Find the relevant captured output
    output = next(o for o in outputs if 'service_operation.store_result' in o.key)
    # Return the captured first arg as the result that needs to be compared
    return output.value['args'][0]

def comparator(recorded_result, replay_result):
    Compare the operation captured output result
    if recorded_result == replay_result:
        return ComparatorResult(EqualityStatus.Equal, "Value is {}".format(recorded_result))
    return ComparatorResult(EqualityStatus.Different,
                            "{recorded_result} != {replay_result}".format(
                                recorded_result=recorded_result, replay_result=replay_result))

def player(recording_id):
    return, playback_function)

# Run comparison and output comparison result using the Equalizer
equalizer = Equalizer(recording_ids, player, result_extractor, comparator)

for comparison_result in equalizer.run_comparison():
    print('Comparison result {recording_id} is: {result}'.format(,

Framework classes - comparing replayed vs recorded operations

Equalizer class

The Equalizer is used to replay multiple recordings of a single operation and conduct a comparison between the recorded results (outputs) vs the replayed results. Underline it uses the TapeRecorder to replay the operations and the TapeCassette to look for and fetch relevant recordings.

def __init__(self, recording_ids, player, result_extractor, comparator,
             comparison_data_extractor=None, compare_execution_config=None)
  • recording_ids - An iterator of recording IDs to play and compare the results
  • player - A function that plays a recording given an ID
  • result_extractor - A function used to extract the results that need to be compared from the recording and playback outputs
  • comparator - A function used to create the comparison result by comparing the recorded vs replayed result
  • comparison_data_extractor - A function used to extract optional data from the recording that will be passed to the comparator
  • compare_execution_config - A configuration specific to the comparison execution flow

For more context, you can look at the basic service operation example.

Usage and examples - comparing multiple recorded vs replayed operations in one flow

When a code change may affect multiple operations, or when you want to have a general regression job running, you can use the PlaybackStudio and EqualizerTuner to run multiple operations together and aggregate the results. Moreover, the EqualizerTuner can be used as a factory to create the relevant plugin functions required to set up an Equalizer to run a comparison of a specific operation.

# Will run 10 playbacks per category
lookup_properties = RecordingLookupProperties(start_date, limit=10)
catagories = ['ServiceOperationA', 'ServiceOperationB']
equalizer_tuner = MyEqualizerTuner()

studio = PlaybackStudio(categories, equalizer_tuner, tape_recorder, lookup_properties)
categories_comparison =

Implementing an EqualizerTuner

class MyEqualizerTuner(EqualizerTuner):
    def create_category_tuning(self, category):
        if category == 'ServiceOperationA':
            return EqualizerTuning(operation_a_playback_function,
        if category == 'ServiceOperationB':
            return EqualizerTuning(operation_b_playback_function,

Framework classes - comparing replayed vs recorded operations

PlaybackStudio class

The studio runs many playbacks for one or more categories (operations), and uses the Equalizer to conduct a comparison between the recorded outputs and the playback outputs.

def __init__(self, categories, equalizer_tuner, tape_recorder, lookup_properties=None,
             recording_ids=None, compare_execution_config=None)
  • categories - The categories (operations) to conduct comparison for
  • equalizer_tuner - Given a category, returns a corresponding equalizer tuning to be used for playback and comparison
  • tape_recorder - The tape recorder that will be used to play the recordings
  • lookup_properties - Optional RecordingLookupProperties used to filter recordings by
  • recording_ids - Optional specific recording IDs. If given, the categories and lookup_properties are ignored and only the given recording IDs will be played
  • compare_execution_config - A configuration specific to the comparison execution flow

EqualizerTuner class

An abstract class that is used to create an EqualizerTuning per category that contains the correct plugins (functions) required to play the operation and compare its results.

def create_category_tuning(self, category)

Create a new EqualizerTuning for the given category

class EqualizerTuning(object):
    def __init__(self, playback_function, result_extractor, comparator,
        self.playback_function = playback_function
        self.result_extractor = result_extractor
        self.comparator = comparator
        self.comparison_data_extractor = comparison_data_extractor


Feel free to send pull requests and raise issues. Make sure to add/modify tests to cover your changes. Please squash your commits in the pull request to one commit. If there is a good logical reason to break it into few commits, multiple pull requests are preferred unless there is a good logical reason to bundle the commits to the same pull request.

Please note that as of now this framework is compatible with both Python 2 and 3, hence any changes should keep that. We use the ״six״ framework to help keep this support.

To contribute, please review our contributing policy.

Running tests

Tests are automatically run in the CI flow using CircleCI. In order to run them locally, you should install the development requirements: pip install -e .[dev] and then run pytest tests.


Record your service operations in production and replay them locally at any time in a sandbox







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