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This is the #step 2 of RFC-24: https://cwiki.apache.org/confluence/display/HUDI/RFC+-+24%3A+Hoodie+Flink+Writer+Proposal This PR introduce a BucketAssigner that assigns bucket ID (partition path & fileID) for each stream record. There is no need to look up index and partition the records anymore in the following pipeline for these records, we actually decide the write target location before the write and each record computes its location when the BucketAssigner receives it, thus, the indexing is with streaming style. Computing locations for a batch of records all at a time is resource consuming so a pressure to the engine, we should avoid that in streaming system.
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).
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
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.