Hive on Spark: Getting Started
Spark Installation
Follow instructions to install latest spark: https://spark.apache.org/docs/latest/spark-standalone.html. In particular:
- Install spark (either download pre-built spark, or build assembly from source).
- Download the correct version. To find out what version of Spark that your particular Hive build was built/tested on, check your Hive's root pom.xml.
- Note: Each version of Spark in turn has several distributions, corresponding with different versions of Hadoop. Choose the one corresponding to Hadoop installation.
- Once spark is installed, find and keep note of the spark-assembly-*.jar location.
- Start Spark cluster (Master and workers).
- Keep note of the Spark master URL. This can be found in Spark master WebUI.
Configuring Hive
- As Hive on Spark is still in development, only a Hive assembly built from hive/spark development branch works against spark: https://github.com/apache/hive/tree/spark. Build hive assembly from this branch as described in https://cwiki.apache.org/confluence/display/Hive/HiveDeveloperFAQ.
Start hive and add the spark-assembly.jar to the hive auxpath.
hive --auxpath /location/to/spark-assembly-spark_version-hadoop_version.jar
Configure hive execution engine to run on spark:
hive> set hive.execution.engine=spark;
Configure required properties for spark-conf. See: http://spark.apache.org/docs/latest/configuration.html. This can be done either by adding a file "spark-defaults.conf" to the hive classpath, or configured as normal properties from hive.
hive> set spark.master=<spark master URL> hive> set spark.eventLog.enabled=true; hive> set spark.executor.memory=512m; hive> set spark.serializer=org.apache.spark.serializer.KryoSerializer;
Common Issues
Issue | Cause | Resolution |
---|---|---|
java.lang.NoSuchMethodError: com.google.common.hash.HashFunction.hashInt (I)Lcom/google/common/hash/HashCode | Guava library version conflict between Spark and Hadoop. See HIVE-7387 and SPARK-2420 for details. | Alternatives until this is fixed:
|
org.apache.spark.SparkException: Job aborted due to stage failure: Task 5.0:0 had a not serializable result: java.io.NotSerializableException: org.apache.hadoop.io.BytesWritable | Spark serializer not set to Kryo | Set spark.serializer to be org.apache.spark.serializer.KryoSerializer as described above |