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/

• 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

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-3.2.3-bin-hadoop3.2/bin/spark-shell \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark3.2-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

The default Spark 2.x version supported is 2.4.4. The default Spark 3.x version, corresponding to spark3 profile is 3.4.0. The default Scala version is 2.12. Refer to the table below for building with different Spark and Scala versions.

For example,

# Build against Spark 3.2.x
mvn clean package -DskipTests

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

# Build against Spark 2.4.4 and Scala 2.11
mvn clean package -DskipTests -Dspark2.4 -Dscala-2.11

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

The default Flink version supported is 1.17. The default Flink 1.17.x version, corresponding to flink1.17 profile is 1.17.0. 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.

For example,

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

# Build against Flink 1.14.x and Scala 2.11
mvn clean package -DskipTests -Dflink1.14 -Dscala-2.11

# Build against Flink 1.13.x and Scala 2.12
mvn clean package -DskipTests -Dflink1.13

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

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

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