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apache / hudi / a9194edd526e733863fef8a1fc1e945898498735
commita9194edd526e733863fef8a1fc1e945898498735[log] [tgz]
authorfhan <aaron.han.1986@gmail.com>Wed Oct 23 11:54:34 2024 +0800
committerGitHub <noreply@github.com>Wed Oct 23 11:54:34 2024 +0800
treed0b6cc0f5d59bb7feac1dcc2aaa85476587606ee
parentd5070e8063d715c87b7db30e5ddc0e892fcb6c70 [diff]
[HUDI-8400] apply 'write.ignore.failed' when write data failed (#12136)

* [HUDI-8400] apply write.ignore.failed when write data failed

* add ignoreWriteFailed() in HoodieWriteHandle and call in sub-class when markFailure

* fix checkstyle

---------

Co-authored-by: fhan <yfhanfei@jd.com>
8 files changed
tree: d0b6cc0f5d59bb7feac1dcc2aaa85476587606ee
  1. .github/
  2. .idea/
  3. conf/
  4. dependencies/
  5. docker/
  6. hudi-aws/
  7. hudi-cli/
  8. hudi-client/
  9. hudi-common/
  10. hudi-examples/
  11. hudi-flink-datasource/
  12. hudi-gcp/
  13. hudi-hadoop-common/
  14. hudi-hadoop-mr/
  15. hudi-integ-test/
  16. hudi-io/
  17. hudi-kafka-connect/
  18. hudi-platform-service/
  19. hudi-spark-datasource/
  20. hudi-sync/
  21. hudi-tests-common/
  22. hudi-timeline-service/
  23. hudi-utilities/
  24. packaging/
  25. release/
  26. rfc/
  27. scripts/
  28. style/
  29. .asf.yaml
  30. .codecov.yml
  31. .gitignore
  32. azure-pipelines-20230430.yml
  33. doap_HUDI.rdf
  34. Dockerfile
  35. LICENSE
  36. NOTICE
  37. pom.xml
  38. README.md
README.md

Apache Hudi

Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage).

https://hudi.apache.org/

Build Test License Maven Central GitHub commit activity Join on Slack Twitter Follow Follow Linkedin

Features

  • Upsert support with fast, pluggable indexing
  • Atomically publish data with rollback support
  • Snapshot isolation between writer & queries
  • Savepoints for data recovery
  • Manages file sizes, layout using statistics
  • Async compaction of row & columnar data
  • Timeline metadata to track lineage
  • Optimize data lake layout with clustering

Hudi supports three types of queries:

  • Snapshot Query - Provides snapshot queries on real-time data, using a combination of columnar & row-based storage (e.g Parquet + Avro).
  • Incremental Query - Provides a change stream with records inserted or updated after a point in time.
  • Read Optimized Query - Provides excellent snapshot query performance via purely columnar storage (e.g. Parquet).

Learn more about Hudi at https://hudi.apache.org

Building Apache Hudi from source

Prerequisites for building Apache Hudi:

  • Unix-like system (like Linux, Mac OS X)
  • Java 8 (Java 9 or 10 may work)
  • Git
  • Maven (>=3.3.1)
# Checkout code and build
git clone https://github.com/apache/hudi.git && cd hudi
mvn clean package -DskipTests

# Start command
spark-3.5.0-bin-hadoop3/bin/spark-shell \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark3.5-bundle_2.12-*.*.*-SNAPSHOT.jar` \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' \
  --conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension' \
  --conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog' \
  --conf 'spark.kryo.registrator=org.apache.spark.HoodieSparkKryoRegistrar'

To build for integration tests that include hudi-integ-test-bundle, use -Dintegration-tests.

To build the Javadoc for all Java and Scala classes:

# Javadoc generated under target/site/apidocs
mvn clean javadoc:aggregate -Pjavadocs

Build with different Spark versions

The default Spark 2.x version supported is 2.4.4. The default Spark 3.x version, corresponding to spark3 profile is 3.5.0. The default Scala version is 2.12. Scala 2.13 is supported for Spark 3.5 and above.

Refer to the table below for building with different Spark and Scala versions.

Maven build optionsExpected Spark bundle jar nameNotes
(empty)hudi-spark3.5-bundle_2.12For Spark 3.5.x and Scala 2.12 (default options)
-Dspark3.3hudi-spark3.3-bundle_2.12For Spark 3.3.x and Scala 2.12
-Dspark3.4hudi-spark3.4-bundle_2.12For Spark 3.4.x and Scala 2.12
-Dspark3.5 -Dscala-2.12hudi-spark3.5-bundle_2.12For Spark 3.5.x and Scala 2.12 (same as default)
-Dspark3.5 -Dscala-2.13hudi-spark3.5-bundle_2.13For Spark 3.5.x and Scala 2.13
-Dspark3hudi-spark3-bundle_2.12 (legacy bundle name)For Spark 3.5.x and Scala 2.12

Please note that only Spark-related bundles, i.e., hudi-spark-bundle, hudi-utilities-bundle, hudi-utilities-slim-bundle, can be built using scala-2.13 profile. Hudi Flink bundle cannot be built using scala-2.13 profile. To build these bundles on Scala 2.13, use the following command:

# Build against Spark 3.5.x and Scala 2.13
mvn clean package -DskipTests -Dspark3.5 -Dscala-2.13 -pl packaging/hudi-spark-bundle,packaging/hudi-utilities-bundle,packaging/hudi-utilities-slim-bundle -am

For example,

# Build against Spark 3.5.x
mvn clean package -DskipTests

# Build against Spark 3.4.x
mvn clean package -DskipTests -Dspark3.4

What about “spark-avro” module?

Starting from versions 0.11, Hudi no longer requires spark-avro to be specified using --packages

Build with different Flink versions

The default Flink version supported is 1.18. The default Flink 1.18.x version, corresponding to flink1.18 profile is 1.18.0. Flink is Scala-free since 1.15.x, there is no need to specify the Scala version for Flink 1.15.x and above versions. Refer to the table below for building with different Flink and Scala versions.

Maven build optionsExpected Flink bundle jar nameNotes
(empty)hudi-flink1.18-bundleFor Flink 1.18 (default options)
-Dflink1.18hudi-flink1.18-bundleFor Flink 1.18 (same as default)
-Dflink1.17hudi-flink1.17-bundleFor Flink 1.17
-Dflink1.16hudi-flink1.16-bundleFor Flink 1.16
-Dflink1.15hudi-flink1.15-bundleFor Flink 1.15
-Dflink1.14hudi-flink1.14-bundleFor Flink 1.14

For example,

# Build against Flink 1.15.x
mvn clean package -DskipTests -Dflink1.15

Running Tests

Unit tests can be run with maven profile unit-tests.

mvn -Punit-tests test

Functional tests, which are tagged with @Tag("functional"), can be run with maven profile functional-tests.

mvn -Pfunctional-tests test

Integration tests can be run with maven profile integration-tests.

mvn -Pintegration-tests verify

To run tests with spark event logging enabled, define the Spark event log directory. This allows visualizing test DAG and stages using Spark History Server UI.

mvn -Punit-tests test -DSPARK_EVLOG_DIR=/path/for/spark/event/log

Quickstart

Please visit https://hudi.apache.org/docs/quick-start-guide.html to quickly explore Hudi's capabilities using spark-shell.

Contributing

Please check out our contribution guide to learn more about how to contribute. For code contributions, please refer to the developer setup.