Skip to content

Latest commit

 

History

History
149 lines (112 loc) · 7.6 KB

README.md

File metadata and controls

149 lines (112 loc) · 7.6 KB
Raw

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).

Hudi logo

https://hudi.apache.org/

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

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-2.4.4-bin-hadoop2.7/bin/spark-shell \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-*.*.*-SNAPSHOT.jar` \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

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

Maven build options Expected Spark bundle jar name Notes
(empty) hudi-spark-bundle_2.11 (legacy bundle name) For Spark 2.4.4 and Scala 2.11 (default options)
-Dspark2.4 hudi-spark2.4-bundle_2.11 For Spark 2.4.4 and Scala 2.11 (same as default)
-Dspark2.4 -Dscala-2.12 hudi-spark2.4-bundle_2.12 For Spark 2.4.4 and Scala 2.12
-Dspark3.1 -Dscala-2.12 hudi-spark3.1-bundle_2.12 For Spark 3.1.x and Scala 2.12
-Dspark3.2 -Dscala-2.12 hudi-spark3.2-bundle_2.12 For Spark 3.2.x and Scala 2.12
-Dspark3.3 -Dscala-2.12 hudi-spark3.3-bundle_2.12 For Spark 3.3.x and Scala 2.12
-Dspark3 hudi-spark3-bundle_2.12 (legacy bundle name) For Spark 3.3.0 and Scala 2.12
-Dscala-2.12 hudi-spark-bundle_2.12 (legacy bundle name) For Spark 2.4.4 and Scala 2.12

For example,

# Build against Spark 3.2.x
mvn clean package -DskipTests -Dspark3.2 -Dscala-2.12

# Build against Spark 3.1.x
mvn clean package -DskipTests -Dspark3.1 -Dscala-2.12

# Build against Spark 2.4.4 and Scala 2.12
mvn clean package -DskipTests -Dspark2.4 -Dscala-2.12

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

Maven build options Expected Flink bundle jar name Notes
(empty) hudi-flink1.14-bundle_2.11 For Flink 1.14 and Scala 2.11 (default options)
-Dflink1.14 hudi-flink1.14-bundle_2.11 For Flink 1.14 and Scala 2.11 (same as default)
-Dflink1.14 -Dscala-2.12 hudi-flink1.14-bundle_2.12 For Flink 1.14 and Scala 2.12
-Dflink1.13 hudi-flink1.13-bundle_2.11 For Flink 1.13 and Scala 2.11
-Dflink1.13 -Dscala-2.12 hudi-flink1.13-bundle_2.12 For Flink 1.13 and Scala 2.12

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

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.