444ff496a4
* [HUDI-2560] introduce id_based schema to support full schema evolution. * add test for FileBasedInternalSchemaStorageManger and rebase code * add support for change column type and fix some test case * fix some bugs encountered in the production env and delete useless code * fix test error * rebase code * fixed some nested schema change bugs * [HUDI-2429][Stacked On HUDI-2560]Support full schema evolution for spark * [use dummyInternalSchema instead of null] * add support for spark3.1.x * remove support for spark3.1.x , sicne some compile fail * support spark3.1.x * rebase and prepare solve all comments * address all comments * rebase code * fixed the count(*) bug * try to get internalSchema by parser commit file/history file directly, not use metaclient which is time cost address some comments * fixed all comments * fix new comments * rebase code,fix UT failed * fixed mistake * rebase code ,fixed new comments * rebase code , and prepare for address new comments * address commits * address new comments * fix new issues * control fallback original write logical
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.