8eed440694
* [HUDI-2119] Ensure the rolled-back instance was previously synced to the Metadata Table when syncing a Rollback Instant. If the rolled-back instant was synced to the Metadata Table, a corresponding deltacommit with the same timestamp should have been created on the Metadata Table timeline. To ensure we can always perfomr this check, the Metadata Table instants should not be archived until their corresponding instants are present in the dataset timeline. But ensuring this requires a large number of instants to be kept on the metadata table. In this change, the metadata table will keep atleast the number of instants that the main dataset is keeping. If the instant being rolled back was before the metadata table timeline, the code will throw an exception and the metadata table will have to be re-bootstrapped. This should be a very rare occurance and should occur only when the dataset is being repaired by rolling back multiple commits or restoring to an much older time. * Fixed checkstyle * Improvements from review comments. Fixed checkstyle Replaced explicit null check with Option.ofNullable Removed redundant function getSynedInstantTime * Renamed getSyncedInstantTime and getSyncedInstantTimeForReader. Sync is confusing so renamed to getUpdateTime() and getReaderTime(). * Removed getReaderTime which is only for testing as the same method can be accessed during testing differently without making it part of the public interface. * Fix compilation error * Reverting changes to HoodieMetadataFileSystemView 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
- 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
# 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.