This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.
Dependency Management #
Java Dependency #
If third-party Java dependencies are used, you can specify the dependencies with the following Python DataStream APIs or through command line arguments directly when submitting the job.
# Use the add_jars() to add local jars and the jars will be uploaded to the cluster. # NOTE: Only local file URLs (start with "file://") are supported. stream_execution_environment.add_jars("file:///my/jar/path/connector.jar", ...) # Use the add_classpaths() to add the dependent jars URL into # the classpath. And the URL will also be added to the classpath of the cluster. # NOTE: The Paths must specify a protocol (e.g. file://) and users should ensure that the # URLs are accessible on both the client and the cluster. stream_execution_environment.add_classpaths("file:///my/jar/path/connector.jar", ...)
Note: These APIs could be called multiple times.
Python Dependency #
If third-party Python dependencies are used, you can specify the dependencies with the following Python DataStream APIs or through command line arguments directly when submitting the job.
Please make sure the uploaded python environment matches the platform that the cluster is running on. Currently only zip-format is supported. i.e. zip, jar, whl, egg, etc.
Adds python file dependencies which could be python files, python packages or local directories. They will be added to the PYTHONPATH of the python UDF worker.
set_python_requirements(requirements_file_path, requirements_cache_dir=None) #
Specifies a requirements.txt file which defines the third-party dependencies. These dependencies will be installed to a temporary directory and added to the PYTHONPATH of the python UDF worker. For the dependencies which could not be accessed in the cluster, a directory which contains the installation packages of these dependencies could be specified using the parameter “requirements_cached_dir”. It will be uploaded to the cluster to support offline installation.
# commands executed in shell echo numpy==1.16.5 > requirements.txt pip download -d cached_dir -r requirements.txt --no-binary :all: # python code stream_execution_environment.set_python_requirements("/path/to/requirements.txt", "cached_dir")
Please make sure the installation packages matches the platform of the cluster and the python version used. These packages will be installed using pip, so also make sure the version of Pip (version >= 7.1.0) and the version of Setuptools (version >= 37.0.0).
add_python_archive(archive_path, target_dir=None) #
Adds a python archive file dependency. The file will be extracted to the working directory of python UDF worker. If the parameter “target_dir” is specified, the archive file will be extracted to a directory named “target_dir”. Otherwise, the archive file will be extracted to a directory with the same name of the archive file.
# command executed in shell # assert the relative path of python interpreter is py_env/bin/python zip -r py_env.zip py_env # python code stream_execution_environment.add_python_archive("/path/to/py_env.zip") # or stream_execution_environment.add_python_archive("/path/to/py_env.zip", "myenv") # the files contained in the archive file can be accessed in UDF def my_func(): with open("myenv/py_env/data/data.txt") as f: ...