ff53e8f0b6
- This pull request adds upgrade/downgrade infra for smooth transition from list based rollback to marker based rollback* - A new property called hoodie.table.version is added to hoodie.properties file as part of this. Whenever hoodie is launched with newer table version i.e 1(or moving from pre 0.6.0 to 0.6.0), an upgrade step will be executed automatically to adhere to marker based rollback.* - This automatic upgrade step will happen just once per dataset as the hoodie.table.version will be updated in property file after upgrade is completed once* - Similarly, a command line tool for Downgrading is added if incase some user wants to downgrade hoodie from table version 1 to 0 or move from hoodie 0.6.0 to pre 0.6.0* - *Added UpgradeDowngrade to assist in upgrading or downgrading hoodie table* - *Added Interfaces for upgrade and downgrade and concrete implementations for upgrading from 0 to 1 and downgrading from 1 to 0.* - *Made some changes to ListingBasedRollbackHelper to expose just rollback stats w/o performing actual rollback, which will be consumed by Upgrade infra* - Reworking failure handling for upgrade/downgrade - Changed tests accordingly, added one test around left over cleanup - New tables now write table version into hoodie.properties - Clean up code naming, abstractions. Co-authored-by: Vinoth Chandar <vinoth@apache.org>
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 -DskipITs
# 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 -DskipITs -Dscala-2.12
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 -DskipITs -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
All tests can be run with maven
mvn 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 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.