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Status

Current state: "Under Discussion"

Discussion thread: 

JIRA: here (<- link to https://issues.apache.org/jira/browse/FLINK-XXXX)

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We can write an easy example to understand the difference between Cython and your module created with Python.

This example (from 

Table of Contents
Cython official web) is about the integration of the function f (x) in [a, b].

...

We need to add one step at the beginning to install the relational dependencies for compiling cython code in the progress of building sdist and wheel packages in building from source code doc page[1].

cd flink-python

# pip install dependencies

pip install -r dev/requirements.txt


Next, we can build sdist and wheel package in flink-python directory

python setup.py sdist bdist_wheel


The sdist and wheel package will be found under ./flink-python/dist/. Either of them could be used for pip installation, such as:

python -m pip install dist/*.tar.gz

Release Changes for Deploy Python artifacts to PyPI

We need to add a step to download wheels from the Artifacts page of the Azure Pipeline built results and upload corresponding wheels to PyPI[2].

# 1. Downloads wheels from Azure Artifacts

Image Modified

# 2. Put the downloaded wheel packages in dist folder of flink-python module

cd flink-python

mkdir dist(optional)

## move all downloaded wheel packages to the dist folder(manually)

# 3. Run the supported script to restore executable permission

## Script files in packages downloaded from Azure will lose executable permissions

dev/restore-executable.sh

#3 Upload the wheel packages to PyPI, e.g.

twine upload --repository-url https://upload.pypi.org/legacy/ dist/*.whl

Solutions For Packages Building

...

  1. Creates another project to build wheel packages. Apache-beam created a beam-wheels repository for the sole purpose of building wheel packages.
  2. Introduces github actions to build wheel packages.
  3. Adds building wheel packages logic to current Azure CI of Flink

Props

Cons

Solution 1

We can learn from beam-wheels

1. Beam have already discussed about to change this solution to github actions as solution 2 

2. Need to create another repository

Solution 2

1.Github actions comes with a strong level of integration with GitHub

2.We can build our wheel packages very simply by using many action tools,such as 

(actions/setup-java,

actions/setup-python,

actions/upload-artifact)

1. It introduces another build CI system of github actions which increases the burden of maintaining

2. Github action is still very young

Solution 3

The logic of building wheel package could be integrated into the current Azure CI directly

N/A

Solution 3 is preferred now as we already have built stable Azure CI in Flink and it is convenient to add the logic of building wheel packages to Azure CI.

...

fast_coder_impl.pxd will define the corresponding declaration of coder and fast_coder_impl.pyx will provide specific implementation.

# fast_coder_impl.pxd

cdef class FlattenRowCoderImpl(StreamCoderImpl):

    cdef list _input_field_coders

    cdef list _output_field_coders

    cdef unsigned char* _input_field_type

    cdef unsigned char* _output_field_type

    cdef libc.stdint.int32_t _input_field_count

    cdef libc.stdint.int32_t _output_field_count

    cdef libc.stdint.int32_t _input_leading_complete_bytes_num

    cdef libc.stdint.int32_t _output_leading_complete_bytes_num

    cdef libc.stdint.int32_t _input_remaining_bits_num

    cdef libc.stdint.int32_t _output_remaining_bits_num

    cdef bint*_null_mask

    cdef unsigned char*_null_byte_search_table

    cdef char* _output_data

    cdef char* _output_row_data

    cdef size_t _output_buffer_size

    cdef size_t _output_row_buffer_size

    cdef size_t _output_pos

    cdef size_t _output_row_pos

    cdef size_t _input_pos

    cdef size_t _input_buffer_size

    cdef char* _input_data

    cdef list row

    cpdef _init_attribute(self)

    cdef _consume_input_data(self, WrapperInputElement wrapper_input_element, size_t size)

    cpdef _write_null_mask(self, value)

    cdef _read_null_mask(self)

    cdef _copy_before_data(self, WrapperFuncInputStream wrapper_stream, OutputStream out_stream)

    cdef _copy_after_data(self, OutputStream out_stream)

    cpdef _dump_field(self, unsigned char field_type, CoderType field_coder, item)

    cdef _dump_row(self)

    cdef _dump_byte(self, unsigned char val)

    cdef _dump_smallint(self, libc.stdint.int16_t v)

    cdef _dump_int(self, libc.stdint.int32_t v)

    cdef _dump_bigint(self, libc.stdint.int64_t v)

    cdef _dump_float(self, float v)

    cdef _dump_double(self, double v)

    cdef _dump_bytes(self, char*b)

    cpdef _load_row(self)

    cpdef _load_field(self, unsigned char field_type, CoderType field_coder)

    cdef unsigned char _load_byte(self) except? -1

    cdef libc.stdint.int16_t _load_smallint(self) except? -1

    cdef libc.stdint.int32_t _load_int(self) except? -1

    cdef libc.stdint.int64_t _load_bigint(self) except? -1

    cdef float _load_float(self) except? -1

    cdef double _load_double(self) except? -1

    cdef bytes _load_bytes(self)

Operation Cython Implementation

...

fast_operations.pxd will define the corresponding declaration of Operations and fast_operations.pyx will provide specific implementation.

# fast_operations.pxd

cdef class StatelessFunctionOperation(Operation):

    cdef Operation consumer

    cdef StreamCoderImpl _value_coder_impl

    cdef dict variable_dict

    cdef list user_defined_funcs

    cdef libc.stdint.int32_t _func_num

    cdef libc.stdint.int32_t _constant_num

    cdef object func

    cpdef generate_func(self, udfs)

    @cython.locals(func_args=str, func_name=str)

    cpdef str _extract_user_defined_function(self, user_defined_function_proto)

    @cython.locals(args_str=list)

    cpdef str _extract_user_defined_function_args(self, args)

    @cython.locals(j_type=libc.stdint.int32_t, constant_value_name=str)

    cpdef str _parse_constant_value(self, constant_value)

cdef class ScalarFunctionOperation(StatelessFunctionOperation):

    pass

cdef class TableFunctionOperation(StatelessFunctionOperation):

    pass

Pipeline of Building Wheel Packages

We need to add a pipeline to build python wheel packages to Azure CI of Flink.

# build-python-wheels.yml

 # 1. compile Flink source code

jobs:

- job compie

   - script: STAGE=compile ${{parameters.environment}} ./tools/azure_controller.sh compile

     displayName: Build

   - task: PublishPipelineArtifact@1

     inputs:

       path: $(CACHE_FLINK_DIR)

       artifact: FlinkCompileCacheDir-${{parameters.stage_name}}

 # 2. build wheel packages

- job: BuildWheels

    dependsOn: compile_${{parameters.stage_name}}

  strategy:

     matrix:

       linux:

         vm-label: 'ubuntu-16.04'

       mac:

         vm-label: 'macOS-10.15'

  pool:

     vmImage: $(vm-label)

  steps:

    # download artifacts

    - task: DownloadPipelineArtifact@2

      inputs:

        path: $(CACHE_FLINK_DIR)

        artifact: FlinkCompileCacheDir-${{parameters.stage_name}}

    # recreate "build-target" symlink for python tests

    - script: |

        mkdir -p flink-dist/target/flink-$(VERSION)-bin

        ln -snf $(CACHE_FLINK_DIR)/flink-dist/target/flink-$(VERSION)-bin/flink-$(VERSION) `pwd`/flink-dist/target/flink-$(VERSION)-bin/flink-$(VERSION)

      displayName: Recreate 'build-target' symlink

    - script: |

        cd flink-python

        bash dev/build-wheels.sh

      displayName: Build wheels

    - task: PublishPipelineArtifact@0

      inputs:

        artifactName: 'wheel_$(Agent.OS)_$(Agent.JobName)'

        targetPath: 'flink-python/dist'

We will include the build-python-wheels.yml into the nightly builds, so that we can collect daily build wheel packages information

/tools/azure-pipelines/build-apache-repo.yml

jobs:

- stage: cron_build

  … # other jobs

- template: build-python-wheels.yml #  Add a job of building wheel packages 

When a new release is released, we will manually trigger a nightly build on the release branch. After that, we can download the wheel packages and push them to PyPI.

...