Spark 1.5.2-1512 Release Notes

The notes below relate specifically to the MapR Distribution for Apache Hadoop. You may also be interested in the open source Spark 1.5.2 Release Notes.

Spark Version 1.5.2
Release Date December 21, 2015
MapR Version Interoperability See Spark Support Matrix.
Source on GitHub
Package Names The following packages are associated with this release:
  • mapr-spark-
  • mapr-spark_1.5.2.201512161339_all.deb
  • mapr-spark-historyserver-
  • mapr-spark-historyserver_1.5.2.201512161339_all.deb
  • mapr-spark-master-
  • mapr-spark-master_1.5.2.201512161339_all.deb

New in This Release

This release of Apache Spark for MapR includes the following features:

  • Support for SparkR (R on Spark)

For details on the features available in the open source version of this component, see the Apache Spark documentation.

Hive Support

This version of Spark supports integration with Hive. However, note the following exceptions:


This release by MapR includes the following fixes on the base Apache release. For complete details, refer to the commit log for this project in GitHub.

GitHub Commit Date (YYYY-MM-DD) Comment
a7dad34 2015-11-25 MAPR-21570: The Spark Master no longer fails to start when it is configured to be highly available.
cdd328a 2015-11-19 MAPR-21525: The HBase version is now set to 0.98.12 in the /opt/mapr/spark/spark-1.5.2/mapr-util/compatibility.version file.
0d4c58e 2015-11-02 MAPR-21243: With Spark on YARN, spark.sql.hive.metastore.sharedPrefixes is now set automatically based on the mode that is used to submit the job.

Known Issues

  • MAPR-17271: On secure clusters, the MapR Control System (MCS) does not display links for Spark-Master and Spark-HistoryServer.
  • MAPR-19761: On a secure cluster, MapR does not support the Spark SQL Thrift JDBC server. When the cluster is secure, the Spark Thrift server will not start.
  • MAPR-20263: On a secure cluster, MapR does not support submitting jobs that interacts with Hive Metastore on yarn-cluster mode. When the cluster is secure, jobs will not complete successfully.
  • Spark versions up to and including 2.3.0 have the following security vulnerability: CVE-2018-1334 Apache Spark local privilege escalation vulnerability