marina432/hudi

Upserts, Deletes And Incremental Processing on Big Data.

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/

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

# 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 the Javadoc for all Java and Scala classes:

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

Build with Scala 2.12

The default Scala version supported is 2.11. To build for Scala 2.12 version, build using scala-2.12 profile

mvn clean package -DskipTests -Dscala-2.12

Build with Spark 3.0.0

The default Spark version supported is 2.4.4. To build for Spark 3.0.0 version, build using spark3 profile

mvn clean package -DskipTests -Dspark3

Build without spark-avro module

The default hudi-jar bundles spark-avro module. To build without spark-avro module, build using spark-shade-unbundle-avro profile

# Checkout code and build
git clone https://github.com/apache/hudi.git && cd hudi
mvn clean package -DskipTests -Pspark-shade-unbundle-avro

# Start command
spark-2.4.4-bin-hadoop2.7/bin/spark-shell \
  --packages org.apache.spark:spark-avro_2.11:2.4.4 \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-*.*.*-SNAPSHOT.jar` \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

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.