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| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 14:45:14 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 14:45:14 +0000 |
| commit | 5aa1bc2b2c32ae1ae3540958d93136e29f8d167b (patch) | |
| tree | 37d8c346f81dad09c834f93d5d177536a24a0897 | |
| parent | 38b125f7d437581b81242e08f32972a927247cc0 (diff) | |
automatic import of python-playback-studioopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-playback-studio.spec | 1217 | ||||
| -rw-r--r-- | sources | 1 |
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@@ -0,0 +1 @@ +/playback-studio-0.3.19.tar.gz diff --git a/python-playback-studio.spec b/python-playback-studio.spec new file mode 100644 index 0000000..3a25821 --- /dev/null +++ b/python-playback-studio.spec @@ -0,0 +1,1217 @@ +%global _empty_manifest_terminate_build 0 +Name: python-playback-studio +Version: 0.3.19 +Release: 1 +Summary: Record your service operations in production and replay them locally at any time in a sandbox +License: BSD License +URL: https://github.com/Optibus/playback +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/54/1d/3ecfd18ff2f547972cf4aa66b935da772d1e101c4c8749db4a540046ad80/playback-studio-0.3.19.tar.gz +BuildArch: noarch + +Requires: python3-parse +Requires: python3-jsonpickle +Requires: python3-six +Requires: python3-contextlib2 +Requires: python3-decorator +Requires: python3-mock +Requires: python3-moto +Requires: python3-pytest +Requires: python3-parameterized +Requires: python3-Werkzeug +Requires: python3-Flask +Requires: python3-flask-restplus +Requires: python3-future +Requires: python3-rsa +Requires: python3-jose +Requires: python3-pylint +Requires: python3-pylint-junit +Requires: python3-flake8 +Requires: python3-flake8-formatter-junit-xml +Requires: python3-pytest-cov + +%description +# playback [](https://circleci.com/gh/Optibus/playback) [](https://codecov.io/gh/Optibus/playback) [](https://pypi.python.org/pypi/playback-studio/) [](https://pypi.python.org/pypi/playback-studio/) + +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](https://github.com/Optibus/playback-ts) 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. + +## Background +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. + +## Features +* 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 + +# Installation +`pip install playback-studio` + +# Examples +There are two examples as part of this repo you can check out under the [examples](examples) directory: +* [basic service operation](examples/basic_service_operation.py/) - a simple example for in memory operation +* [Flask based service](examples/flask) - an end to end flask based example with persistent recording + +# 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: +```python +from flask import request +tape_cassette = S3TapeCassette('production-recordings', region='us-east-1', read_only=False) +tape_recorder = TapeRecorder(tape_cassette) +tape_recorder.enabled_recording() + + +class ServiceOperation(object): + + ... + + @tape_recorder.operation() + 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 + + @tape_recorder.intercept_input(alias='service_operation.get_request_data') + def get_request_data(self): + """ + Reads the required input for the operation + """ + # Get request data from flask + return request.data + + @tape_recorder.intercept_output(alias='service_operation.store_result') + 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` +```python +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 +tape_recorder.play(recording_id, 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 +```python +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 +```python +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 +```python +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. +```python +def get_recording(self, recording_id) +``` +Get recording is stored under the given ID + +```python +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 + +```python +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 +```python +# 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). +```python +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 +```python +# Creates an iterator over relevant recordings which are ready to be played +lookup_properties = RecordingLookupProperties(start_date=datetime.utcnow() - timedelta(days=7), + limit=5) +recording_ids = find_matching_recording_ids(tape_recorder, + ServiceOperation.__name__, + lookup_properties) + + +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 tape_recorder.play(recording_id, 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( + recording_id=comparison_result.playback.original_recording.id, + result=comparison_result.comparator_status)) +``` + +# 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. + +```python +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](examples/basic_service_operation.py/) 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. + +```python +# 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 = studio.play() +``` +Implementing an `EqualizerTuner` +```python +class MyEqualizerTuner(EqualizerTuner): + def create_category_tuning(self, category): + if category == 'ServiceOperationA': + return EqualizerTuning(operation_a_playback_function, + operation_a_result_extractor, + operation_a_comparator) + if category == 'ServiceOperationB': + return EqualizerTuning(operation_b_playback_function, + operation_b_result_extractor, + operation_b_comparator) +``` + +# 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. + +```python +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. + +```python +def create_category_tuning(self, category) +``` +Create a new `EqualizerTuning` for the given category + +```python +class EqualizerTuning(object): + def __init__(self, playback_function, result_extractor, comparator, + comparison_data_extractor=None): + self.playback_function = playback_function + self.result_extractor = result_extractor + self.comparator = comparator + self.comparison_data_extractor = comparison_data_extractor +``` + +# Contributions +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](https://github.com/Optibus/playback/blob/main/CONTRIBUTING.md). + +## 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`. + + + + +%package -n python3-playback-studio +Summary: Record your service operations in production and replay them locally at any time in a sandbox +Provides: python-playback-studio +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-playback-studio +# playback [](https://circleci.com/gh/Optibus/playback) [](https://codecov.io/gh/Optibus/playback) [](https://pypi.python.org/pypi/playback-studio/) [](https://pypi.python.org/pypi/playback-studio/) + +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](https://github.com/Optibus/playback-ts) 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. + +## Background +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. + +## Features +* 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 + +# Installation +`pip install playback-studio` + +# Examples +There are two examples as part of this repo you can check out under the [examples](examples) directory: +* [basic service operation](examples/basic_service_operation.py/) - a simple example for in memory operation +* [Flask based service](examples/flask) - an end to end flask based example with persistent recording + +# 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: +```python +from flask import request +tape_cassette = S3TapeCassette('production-recordings', region='us-east-1', read_only=False) +tape_recorder = TapeRecorder(tape_cassette) +tape_recorder.enabled_recording() + + +class ServiceOperation(object): + + ... + + @tape_recorder.operation() + 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 + + @tape_recorder.intercept_input(alias='service_operation.get_request_data') + def get_request_data(self): + """ + Reads the required input for the operation + """ + # Get request data from flask + return request.data + + @tape_recorder.intercept_output(alias='service_operation.store_result') + 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` +```python +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 +tape_recorder.play(recording_id, 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 +```python +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 +```python +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 +```python +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. +```python +def get_recording(self, recording_id) +``` +Get recording is stored under the given ID + +```python +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 + +```python +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 +```python +# 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). +```python +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 +```python +# Creates an iterator over relevant recordings which are ready to be played +lookup_properties = RecordingLookupProperties(start_date=datetime.utcnow() - timedelta(days=7), + limit=5) +recording_ids = find_matching_recording_ids(tape_recorder, + ServiceOperation.__name__, + lookup_properties) + + +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 tape_recorder.play(recording_id, 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( + recording_id=comparison_result.playback.original_recording.id, + result=comparison_result.comparator_status)) +``` + +# 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. + +```python +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](examples/basic_service_operation.py/) 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. + +```python +# 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 = studio.play() +``` +Implementing an `EqualizerTuner` +```python +class MyEqualizerTuner(EqualizerTuner): + def create_category_tuning(self, category): + if category == 'ServiceOperationA': + return EqualizerTuning(operation_a_playback_function, + operation_a_result_extractor, + operation_a_comparator) + if category == 'ServiceOperationB': + return EqualizerTuning(operation_b_playback_function, + operation_b_result_extractor, + operation_b_comparator) +``` + +# 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. + +```python +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. + +```python +def create_category_tuning(self, category) +``` +Create a new `EqualizerTuning` for the given category + +```python +class EqualizerTuning(object): + def __init__(self, playback_function, result_extractor, comparator, + comparison_data_extractor=None): + self.playback_function = playback_function + self.result_extractor = result_extractor + self.comparator = comparator + self.comparison_data_extractor = comparison_data_extractor +``` + +# Contributions +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](https://github.com/Optibus/playback/blob/main/CONTRIBUTING.md). + +## 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`. + + + + +%package help +Summary: Development documents and examples for playback-studio +Provides: python3-playback-studio-doc +%description help +# playback [](https://circleci.com/gh/Optibus/playback) [](https://codecov.io/gh/Optibus/playback) [](https://pypi.python.org/pypi/playback-studio/) [](https://pypi.python.org/pypi/playback-studio/) + +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](https://github.com/Optibus/playback-ts) 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. + +## Background +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. + +## Features +* 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 + +# Installation +`pip install playback-studio` + +# Examples +There are two examples as part of this repo you can check out under the [examples](examples) directory: +* [basic service operation](examples/basic_service_operation.py/) - a simple example for in memory operation +* [Flask based service](examples/flask) - an end to end flask based example with persistent recording + +# 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: +```python +from flask import request +tape_cassette = S3TapeCassette('production-recordings', region='us-east-1', read_only=False) +tape_recorder = TapeRecorder(tape_cassette) +tape_recorder.enabled_recording() + + +class ServiceOperation(object): + + ... + + @tape_recorder.operation() + 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 + + @tape_recorder.intercept_input(alias='service_operation.get_request_data') + def get_request_data(self): + """ + Reads the required input for the operation + """ + # Get request data from flask + return request.data + + @tape_recorder.intercept_output(alias='service_operation.store_result') + 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` +```python +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 +tape_recorder.play(recording_id, 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 +```python +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 +```python +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 +```python +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. +```python +def get_recording(self, recording_id) +``` +Get recording is stored under the given ID + +```python +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 + +```python +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 +```python +# 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). +```python +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 +```python +# Creates an iterator over relevant recordings which are ready to be played +lookup_properties = RecordingLookupProperties(start_date=datetime.utcnow() - timedelta(days=7), + limit=5) +recording_ids = find_matching_recording_ids(tape_recorder, + ServiceOperation.__name__, + lookup_properties) + + +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 tape_recorder.play(recording_id, 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( + recording_id=comparison_result.playback.original_recording.id, + result=comparison_result.comparator_status)) +``` + +# 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. + +```python +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](examples/basic_service_operation.py/) 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. + +```python +# 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 = studio.play() +``` +Implementing an `EqualizerTuner` +```python +class MyEqualizerTuner(EqualizerTuner): + def create_category_tuning(self, category): + if category == 'ServiceOperationA': + return EqualizerTuning(operation_a_playback_function, + operation_a_result_extractor, + operation_a_comparator) + if category == 'ServiceOperationB': + return EqualizerTuning(operation_b_playback_function, + operation_b_result_extractor, + operation_b_comparator) +``` + +# 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. + +```python +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. + +```python +def create_category_tuning(self, category) +``` +Create a new `EqualizerTuning` for the given category + +```python +class EqualizerTuning(object): + def __init__(self, playback_function, result_extractor, comparator, + comparison_data_extractor=None): + self.playback_function = playback_function + self.result_extractor = result_extractor + self.comparator = comparator + self.comparison_data_extractor = comparison_data_extractor +``` + +# Contributions +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](https://github.com/Optibus/playback/blob/main/CONTRIBUTING.md). + +## 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`. + + + + +%prep +%autosetup -n playback-studio-0.3.19 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-playback-studio -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.19-1 +- Package Spec generated @@ -0,0 +1 @@ +370d0e6e4757f86c0ab2d79a2365b367 playback-studio-0.3.19.tar.gz |