commit | 1f7add92916c37b05be270d9c75a9042134ec506 | [log] [tgz] |
---|---|---|
author | Mathieu <wxhjsxz@126.com> | Fri Oct 02 05:25:29 2020 +0800 |
committer | GitHub <noreply@github.com> | Thu Oct 01 14:25:29 2020 -0700 |
tree | ec9412baa804cc1dc77671bb6d1848026f7c063e | |
parent | 5aaaf8bff17fcff595aa3927b45f4928e0d400e8 [diff] |
[HUDI-1089] Refactor hudi-client to support multi-engine (#1827) - This change breaks `hudi-client` into `hudi-client-common` and `hudi-spark-client` modules - Simple usages of Spark using jsc.parallelize() has been redone using EngineContext#map, EngineContext#flatMap etc - Code changes in the PR, break classes into `BaseXYZ` parent classes with no spark dependencies living in `hudi-client-common` - Classes on `hudi-spark-client` are named `SparkXYZ` extending the parent classes with all the Spark dependencies - To simplify/cleanup, HoodieIndex#fetchRecordLocation has been removed and its usages in tests replaced with alternatives Co-authored-by: Vinoth Chandar <vinoth@apache.org>
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).
Hudi supports three types of queries:
Learn more about Hudi at https://hudi.apache.org
Prerequisites for building Apache Hudi:
# 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
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
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'
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
Please visit https://hudi.apache.org/docs/quick-start-guide.html to quickly explore Hudi's capabilities using spark-shell.