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- Add a new action called INDEX, whose state transition is described in the RFC. - Changes in timeline to support the new action. - Add an index planner in ScheduleIndexActionExecutor. - Add index plan executor in RunIndexActionExecutor. - Add 3 APIs in HoodieTableMetadataWriter; a) scheduleIndex: will generate an index plan based on latest completed instant, initialize file groups and add a requested INDEX instant, b) index: executes the index plan and also takes care of writes that happened after indexing was requested, c) dropIndex: will drop index by removing the given metadata partition. - Add 2 new table configs to serve as the source of truth for inflight and completed indexes. - Support upgrade/downgrade taking care of the newly added configs. - Add tool to trigger indexing in HoodieIndexer. - Handle corner cases related to partial failures. - Abort gracefully after deleting partition and instant. - Handle other actions in timeline to consider before catching up
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
- 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 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 version supported is 2.4.4. To build for different Spark versions and Scala 2.12, use the corresponding profile
| Label | Artifact Name for Spark Bundle | Maven Profile Option | Notes |
|---|---|---|---|
| Spark 2.4, Scala 2.11 | hudi-spark2.4-bundle_2.11 | -Pspark2.4 |
For Spark 2.4.4, which is the same as the default |
| Spark 2.4, Scala 2.12 | hudi-spark2.4-bundle_2.12 | -Pspark2.4,scala-2.12 |
For Spark 2.4.4, which is the same as the default and Scala 2.12 |
| Spark 3.1, Scala 2.12 | hudi-spark3.1-bundle_2.12 | -Pspark3.1 |
For Spark 3.1.x |
| Spark 3.2, Scala 2.12 | hudi-spark3.2-bundle_2.12 | -Pspark3.2 |
For Spark 3.2.x |
| Spark 3, Scala 2.12 | hudi-spark3-bundle_2.12 | -Pspark3 |
This is the same as Spark 3.2, Scala 2.12 |
| Spark, Scala 2.11 | hudi-spark-bundle_2.11 | Default | The default profile, supporting Spark 2.4.4 |
| Spark, Scala 2.12 | hudi-spark-bundle_2.12 | -Pscala-2.12 |
The default profile (for Spark 2.4.4) with Scala 2.12 |
For example,
# Build against Spark 3.2.x (the default build shipped with the public Spark 3 bundle)
mvn clean package -DskipTests -Pspark3.2
# Build against Spark 3.1.x
mvn clean package -DskipTests -Pspark3.1
# Build against Spark 2.4.4 and Scala 2.12
mvn clean package -DskipTests -Pspark2.4,scala-2.12
What about "spark-avro" module?
Starting from versions 0.11, Hudi no longer requires spark-avro to be specified using --packages
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.