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Status

Current state: 

Discussion thread: 

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

Released: <Flink Version>

Motivation

Scalar Python UDF (FLIP-58) has already been supported in release 1.10 and Python UDTF will be supported in the coming release of 1.11. In release 1.10, we focused on supporting UDF features and did not make many optimizations in terms of performance. Although we have made a lot of optimizations in master, cython can further greatly improve the performance of Python UDF.

...

This example (from cython official web) is about the integration of the function f (x) in [a, b].

# Pure Python Code

def f(x):

    return x ** 2 - x

def integrate_f(a, b, N):

    s = 0

    dx = (b - a) / N

    for i in range(N):

        s += f(a + i * dx)

    return s * dx








# Cython Code

cdef double f(double x):

    return x ** 2 - x

cpdef integrate_f(double a, double b, int N):

    cdef int i

    cdef double s, dx

    s = 0

    dx = (b - a) / N

    for i in range(N):

        s += f(a + i * dx)

    return s * dx



Check out the table below which shows how much speed Cython gave us for different number function calls.  We got over 150X speedup with Cython!

nCalls

Pure code time(s)

Cython Code time(s)

Speedup

10000

2.165466

0.012378

174x

50000

10.803928

0.061041

176x

100000

21.277053

0.114681

185x

200000

41.897756

0.219027

191x

500000

105.50863

0.544354

193x

1000000

218.101658

1.07485

202x


As we can see, although the syntax of cython is similar to python, cython can bring huge performance improvements.

...

Next, let's take a look at the test code we used and compare it with the detailed test performance data of release-1.10, master and code optimized with cython.

Test Code



@udf(input_types=[DataTypes.INT(False)], result_type=DataTypes.INT(False))
def inc(x):
   return x + 1


t_env.register_function("inc", inc)
num_rows = 100000000
num_columns = 10

select_list = ["inc(c%s)" % i for i in range(num_columns)]
t_env.register_table_sink(
   "sink",
   PrintTableSink(
       ["c%s" % i for i in range(num_columns)],
       [DataTypes.INT(False)] * num_columns))

t_env.from_table_source(MultiRowColumnTableSource(num_rows, num_columns)) \
   .select(','.join(select_list)) \
   .insert_into("sink")

beg_time = time.time()
t_env.execute("perf_test")
print("consume time: " + str(time.time() - beg_time))

End To End Performance comparison

Check out the table below which shows how much speed Cython gave us for different rows and columns test data. When there is only one column of data, the inc func is called once for each row of data, so all the overhead lies in the framework. When there are ten columns of data, the inc func will be called ten times for each row of data, Therefore, compared with the case of 10 rows and one column, the more time is spent in calculation, so the end-to-end promotion multiple is not as large as that of 10 rows and one column.

rows, columns

PyFlink 1.10

master

master-cython

cython speedup

(release-1.10)

cython seedup

(master)

10kw, 1

2154s

441s

70s

30X

6x

10kw, 10

5697s

1221s

254s

22X

5X


From the data in the table and diagram, we can find that using cython can greatly improve our performance.

...

We need to add two steps at the beginning to install the dependencies of cython and apache-beam after introducing cython optimization in the progress of building sdist and wheel packages in building from source code doc page[1].

# 1. pip install apache-beam

pip install apache-beam==2.19.0

# 2. pip install cython 

pip install cython==0.28.1


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


NOTE: Although we have added two front steps to build the sdist and wheel packages from source, for users, they still directly pip install apache-flink

...

We need to add a step to download wheels from github actions and upload corresponding wheels to PyPI[2].

#1. Downloads wheels in github actions workflow

#2. Unzip the packages, e.g.

unzip apche_flink-1.11.dev0-cp35-cp35m-linux_x86_64.zip

# you will get apche_flink-1.11.dev0-cp35-cp35m-linux_x86_64.whl

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

twine upload --repository-url https://upload.pypi.org/legacy/ apche_flink-1.11.dev0-cp35-cp35m-linux_x86_64.whl


Solutions For Packages Building

...

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



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 3

2. We need to create another repository

Solution 2

We can directly add building wheel packages logic to current Travis or Azure Devops CI


Solution 3

1. It won’t affect current CI logic

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)

3. It is very convenient to download the built wheel packages

It is very young


Solution 3 is prefered now as it won’t affect current CI logic and we have learned that many projects have introduced github actions such as spark, arrow and beam.

I have configured on my test repo that every push on Master branch and Release created will trigger the workflow of building wheel packages. For saving resources, we can also configure a daily built in Master branch.

on:

  push:

    branches: [ master ]

  release:

    types: [ created ]


We can download the corresponding wheel package in the workflow page of the github action once the reconstruction is successful.The wheel packages will automatically expire after 90 days.

...

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

Workflow of Building Wheel Packages

We need to add a workflow to build python wheel packages

# This workflow will build PyFlink wheel packages.

name: Build Python Wheel Package

on:

  push:

    branches: [ master ]

  release:

    types: [ created ]


env:

  VERSION: 1.11-SNAPSHOT

jobs:

  build:

    runs-on: ubuntu-latest

    strategy:

      matrix:

        java: [ '1.8' ]

    name: Build Flink - JDK - ${{ matrix.java }}

    steps:

    - uses: actions/checkout@v2

    - name: Set up JDK ${{ matrix.java }}

      uses: actions/setup-java@v1

      with:

        java-version: ${{ matrix.java }}

    - name: Build with Maven

      run: |

        export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=1g"

        export MAVEN_CLI_OPTS="--no-transfer-progress"

        mkdir -p ~/.m2

        mvn $MAVEN_CLI_OPTS clean install -Dmaven.javadoc.skip=true -DskipTests -Dorg.slf4j.simpleLogger.defaultLogLevel=WARN

        rm -rf ~/.m2/repository/org/apache/flink

    - uses: actions/upload-artifact@v1

      with:

        name: maven-result

        path: flink-dist/target/flink-${{ env.VERSION }}-bin/flink-${{ env.VERSION }}

  build-python:

    runs-on: ${{ matrix.os }}

    needs: build

    strategy:

      matrix:

        os: [ubuntu-latest, macos-latest]

        python-version: [3.5, 3.6, 3.7]

    name: Build Python - Python${{ matrix.os }}/${{ matrix.python-version }}

    steps:

    - uses: actions/checkout@v2

    - uses: actions/download-artifact@v1

      with:

        name: maven-result

        path: flink-dist/target/flink-${{ env.VERSION }}-bin/flink-${{ env.VERSION }}

    - name: Set up Python ${{ matrix.python-version }}

      uses: actions/setup-python@v1

      with:

        python-version: ${{ matrix.python-version }}

    - name: Install dependencies

      run: |

        cd flink-python

        python -m pip install --upgrade pip setuptools

        pip install wheel

        pip install apache-beam==2.19.0

        pip install cython==0.28.1

    - name: build bdist wheel

      run: |

        cd flink-python

        python setup.py bdist_wheel

    - id: getwheelname

      name: get wheel name

      run: |

        cd flink-python/dist

        echo "::set-output name=file::$(ls *.whl)"

    - uses: actions/upload-artifact@v1

      with:

        name: ${{ steps.getwheelname.outputs.file }}

        path: flink-python/dist/${{ steps.getwheelname.outputs.file }}

Compatibility, Deprecation, and Migration Plan

...