Spark and Hadoop Integration
This section describes how to write to various Hadoop ecosystem components from Spark.
Writing to HBase from Spark
You can use Spark to process data that is destined for HBase. See Importing Data Into HBase Using Spark.
You can also use Spark in conjunction with Apache Kafka to stream data from Spark to HBase. See Importing Data Into HBase Using Spark and Kafka.
- Manually provide the location of HBase configurations and classes to the driver and executors. You do so by passing the locations to both classpaths when you run spark-submit, spark-shell, or pyspark:
- parcel installation
--driver-class-path /etc/hbase/conf:/opt/cloudera/parcels/CDH/lib/hbase/lib/* --conf "spark.executor.extraClassPath=/etc/hbase/conf/:/opt/cloudera/parcels/CDH/lib/hbase/lib/*"
- package installation
--driver-class-path /etc/hbase/conf:/usr/lib/hbase/lib/hbase/lib/* --conf "spark.executor.extraClassPath=/etc/hbase/conf/:/usr/lib/hbase/lib/*"
- parcel installation
- Add an HBase gateway role to all YARN worker hosts and the edge host where you run spark-submit, spark-shell, or pyspark and deploy HBase client configurations.
Limitations in Kerberized Environments
- The application must be restarted every seven days.
- If the cluster also has HA enabled, you must specify the keytab and principal parameters in your command line (as opposed
to using kinit). For example:
spark-shell --jars MySparkHbaseApp.jar --principal ME@DOMAIN.COM --keytab /path/to/local/keytab ...
spark-submit --class com.example.SparkHbaseApp --principal ME@DOMAIN.COM --keytab /path/to/local/keytab SparkHBaseApp.jar [ application parameters....]"For further information, see Spark Authentication.
Accessing Hive from Spark
When a Spark job accesses a Hive view, Spark must have privileges to read the data files in the underlying Hive tables. Currently, Spark cannot use fine-grained privileges based on the columns or the WHERE clause in the view definition. If Spark does not the required privileges on the underlying data files, a SparkSQL query against the view returns an empty result set, rather than an error.
Running Spark Jobs from Oozie
For CDH 5.4 and higher you can invoke Spark jobs from Oozie using the Spark action. For information on the Spark action, see Oozie Spark Action Extension.
In CDH 5.4, to enable dynamic allocation when running the action, specify the following in the Oozie workflow:
<spark-opts>--conf spark.dynamicAllocation.enabled=true --conf spark.shuffle.service.enabled=true --conf spark.dynamicAllocation.minExecutors=1 </spark-opts>
If you have enabled the shuffle service in Cloudera Manager, you do not need to specify spark.shuffle.service.enabled.