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[Hive]To avoid excessive HMS memory usage, when executing AlterTable for a Paimon table containing a large number of fields #301

[Hive]To avoid excessive HMS memory usage, when executing AlterTable for a Paimon table containing a large number of fields

[Hive]To avoid excessive HMS memory usage, when executing AlterTable for a Paimon table containing a large number of fields #301

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name: UTCase and ITCase Spark 4.x
on:
push:
pull_request:
paths-ignore:
- 'docs/**'
- '**/*.md'
env:
JDK_VERSION: 17
concurrency:
group: ${{ github.workflow }}-${{ github.event_name }}-${{ github.event.number || github.run_id }}
cancel-in-progress: true
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up JDK ${{ env.JDK_VERSION }}
uses: actions/setup-java@v2
with:
java-version: ${{ env.JDK_VERSION }}
distribution: 'adopt'
- name: Build Spark
run: mvn -T 1C -B clean install -DskipTests -Pspark4
- name: Test Spark
timeout-minutes: 60
run: |
# run tests with random timezone to find out timezone related bugs
. .github/workflows/utils.sh
jvm_timezone=$(random_timezone)
echo "JVM timezone is set to $jvm_timezone"
test_modules=""
for suffix in common_2.13 4.0; do
test_modules+="org.apache.paimon:paimon-spark-${suffix},"
done
test_modules="${test_modules%,}"
mvn -T 1C -B test -pl "${test_modules}" -Duser.timezone=$jvm_timezone -Pspark4
env:
MAVEN_OPTS: -Xmx4096m