Flyte Data Lineage Python Application and Library
Initial support for Flyte -> (DataHub, AWS Glue Catalog)
Data lineage is extracted from Flyte by consuming events sent from FlyetAdmin during a workflow run. A workflow is ingested into one or more target systems. Supported targets include DataHub and the AWS Glue Catalog. The DataHUb target overwrites any previous versions and Glue supports schema versioning if enabled.
The data model consists of entities: Datasets, Pipelines & Tasks
Entities contain one or more aspects: Tags, Owners, etc
For example a pipeline can consist of many directed tasks with associated input and output datasets. The DataHub ingestion does not need to be captured in one pass by providing a complete pipeline data model as input. Individual entities can be ingested separately and wired together later by linking the entity ids (urns) together. Entity aspects can also be added as and when they become known in the system. This reflects reality where by an ingestion point only has local information such as the current workflow or task being processed and enables cross system dependencies to be wired together.
Datasets are ingested into the Glue Catalog. A table is created for the dataset and optionally a database created to contain the table. The database name if not supplied will be named based on the flyte using flyte__.
Flyte publishes SNS topics to an SQS queue for different workflow lifecycle events. The events of interest for a workflow are grouped and processed to produce data lineage. The lineage includes the chain of workflow tasks and any datasets used within the flow. Only successfully run workflows are captured and datasets that are not fetched from the flyte cache. The lineage is then ingested by the configured targets.
traitlets from Jupyter is used for the application configuration which can be configured within with defaults in classes, as cmd line args or in a python config file (./flytelineage_config.py).
$ flytelineage --help-all
Flyte data lineage
Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
<cmd> --help-all
--debug
Set log-level to debug, for the most verbose logging.
Equivalent to: [--Application.log_level=10]
--show-config
Show the application's configuration (human-readable format)
Equivalent to: [--Application.show_config=True]
--show-config-json
Show the application's configuration (json format)
Equivalent to: [--Application.show_config_json=True]
--log-level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
Equivalent to: [--Application.log_level]
Class options
=============
The command-line option below sets the respective configurable class-parameter:
--Class.parameter=value
This line is evaluated in Python, so simple expressions are allowed.
For instance, to set `C.a=[0,1,2]`, you may type this:
--C.a='range(3)'
Application(SingletonConfigurable) options
------------------------------------------
--Application.log_datefmt=<Unicode>
The date format used by logging formatters for %(asctime)s
Default: '%Y-%m-%d %H:%M:%S'
--Application.log_format=<Unicode>
The Logging format template
Default: '[%(name)s]%(highlevel)s %(message)s'
--Application.log_level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
--Application.show_config=<Bool>
Instead of starting the Application, dump configuration to stdout
Default: False
--Application.show_config_json=<Bool>
Instead of starting the Application, dump configuration to stdout (as JSON)
Default: False
FlyteLineage(Application) options
---------------------------------
--FlyteLineage.aws_region=<Unicode>
aws region
Default: 'us-east-1'
--FlyteLineage.config_file=<Unicode>
The config file to load
Default: 'flytelineage_config.py'
--FlyteLineage.log_datefmt=<Unicode>
The date format used by logging formatters for %(asctime)s
Default: '%Y-%m-%d %H:%M:%S'
--FlyteLineage.log_format=<Unicode>
The Logging format template
Default: '[%(name)s]%(highlevel)s %(message)s'
--FlyteLineage.log_level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
--FlyteLineage.show_config=<Bool>
Instead of starting the Application, dump configuration to stdout
Default: False
--FlyteLineage.show_config_json=<Bool>
Instead of starting the Application, dump configuration to stdout (as JSON)
Default: False
--FlyteLineage.sqs_queue=<Unicode>
sqs queue name or url
Default: '' ''
$ git clone https://github.com/unionai/flyteevents-datahub.git
$ cd flyteevents-datahub
$ python -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt
$ python setup.py develop
Fork the project from https://github.com/unionai/flyteevents-datahub
Keep your fork up to date by tracking the original "upstream" repo that you forked
$ git remote add upstream https://github.com/unionai/flyteevents-datahub.git
$ git remote -v
origin https://github.com/erowan/flyteevents-datahub.git (fetch)
origin https://github.com/erowan/flyteevents-datahub.git (push)
upstream https://github.com/unionai/flyteevents-datahub.git (fetch)
upstream https://github.com/unionai/flyteevents-datahub.git (push)
Whenever you want to update your fork with the latest upstream changes, you'll need to first fetch the upstream repo's branches and latest commits to bring them into your repository:
# Fetch from upstream remote
git fetch upstream
# View all branches, including those from upstream
git branch -va
Now, checkout your own main branch and merge the upstream repo's main branch:
# Checkout your main branch and merge upstream
git checkout main
git merge upstream/main
Doing Your Work - Create a Branch
As this is a public repo you need need to generate a token from your github account and use that for the password.
Remember to change to your personal user info
$ git config user.name "joe"
$ git config user.email "[email protected]"
Create a PR against the upstream repo
$ pip install -r requirements-dev.txt
$ pytest -v
# Run tests with coverage reporting
$ pytest --cov
$ pytest --cov=flytelineage --cov-report term-missing flytelineage
# Run tests with logging
$ pytest --log-level=DEBUG
$ black .
$ autoflake -i --remove-all-unused-imports <files>
$ flake8 flytelineage --count --select=E9,F63,F7,F82 --show-source --statistics
$ flake8 flytelineage --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics