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Build / Spark Connect Python-only (master-server, 35-client, Python 3.11) #91

Build / Spark Connect Python-only (master-server, 35-client, Python 3.11)

Build / Spark Connect Python-only (master-server, 35-client, Python 3.11) #91

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# to you under the Apache License, Version 2.0 (the
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# KIND, either express or implied. See the License for the
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name: Build / Spark Connect Python-only (master-server, 35-client, Python 3.11)
on:
schedule:
- cron: '0 21 * * *'
jobs:
# Build: build Spark and run the tests for specified modules using SBT
build:
name: "Build modules: pyspark-connect"
runs-on: ubuntu-latest
timeout-minutes: 100
if: github.repository == 'apache/spark'
steps:
- name: Checkout Spark repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cache SBT and Maven
uses: actions/cache@v4
with:
path: |
build/apache-maven-*
build/*.jar
~/.sbt
key: build-spark-connect-python-only-${{ hashFiles('**/pom.xml', 'project/build.properties', 'build/mvn', 'build/sbt', 'build/sbt-launch-lib.bash', 'build/spark-build-info') }}
restore-keys: |
build-spark-connect-python-only-
- name: Cache Coursier local repository
uses: actions/cache@v4
with:
path: ~/.cache/coursier
key: coursier-build-spark-connect-python-only-${{ hashFiles('**/pom.xml') }}
restore-keys: |
coursier-build-spark-connect-python-only-
- name: Install Java 17
uses: actions/setup-java@v4
with:
distribution: zulu
java-version: 17
- name: Install Python 3.11
uses: actions/setup-python@v5
with:
python-version: '3.11'
architecture: x64
- name: Build Spark
run: |
./build/sbt -Phive Test/package
- name: Install Python dependencies
run: |
pip install 'numpy==1.25.1' 'pyarrow==12.0.1' 'pandas<=2.0.3' scipy unittest-xml-reporting plotly>=4.8 'mlflow>=2.3.1' coverage 'matplotlib==3.7.2' openpyxl 'memory-profiler==0.60.0' 'scikit-learn==1.1.*'
# Add Python deps for Spark Connect.
pip install 'grpcio>=1.48,<1.57' 'grpcio-status>=1.48,<1.57' 'protobuf==3.20.3' 'googleapis-common-protos==1.56.4' 'graphviz==0.20.3'
# Add torch as a testing dependency for TorchDistributor
pip install 'torch==2.0.1' 'torchvision==0.15.2' torcheval
- name: Run tests
env:
SPARK_TESTING: 1
SPARK_SKIP_CONNECT_COMPAT_TESTS: 1
SPARK_CONNECT_TESTING_REMOTE: sc://localhost
run: |
# Make less noisy
cp conf/log4j2.properties.template conf/log4j2.properties
sed -i 's/rootLogger.level = info/rootLogger.level = warn/g' conf/log4j2.properties
# Start a Spark Connect server for local
PYTHONPATH="python/lib/pyspark.zip:python/lib/py4j-0.10.9.7-src.zip:$PYTHONPATH" ./sbin/start-connect-server.sh \
--driver-java-options "-Dlog4j.configurationFile=file:$GITHUB_WORKSPACE/conf/log4j2.properties" \
--jars "`find connector/protobuf/target -name spark-protobuf-*SNAPSHOT.jar`,`find connector/avro/target -name spark-avro*SNAPSHOT.jar`"
# Checkout to branch-3.5 to use the tests in branch-3.5.
cd ..
git clone --single-branch --branch branch-3.5 $GITHUB_SERVER_URL/$GITHUB_REPOSITORY spark-3.5
cd spark-3.5
# Several tests related to catalog requires to run them sequencially, e.g., writing a table in a listener.
# Run branch-3.5 tests
./python/run-tests --parallelism=1 --python-executables=python3 --modules pyspark-connect
# None of tests are dependent on each other in Pandas API on Spark so run them in parallel
./python/run-tests --parallelism=4 --python-executables=python3 --modules pyspark-pandas-connect,pyspark-pandas-slow-connect
- name: Upload test results to report
if: always()
uses: actions/upload-artifact@v4
with:
name: test-results-spark-connect-python-only
path: "**/target/test-reports/*.xml"
- name: Upload Spark Connect server log file
if: ${{ !success() }}
uses: actions/upload-artifact@v4
with:
name: unit-tests-log-spark-connect-python-only
path: logs/*.out