Fixed Issues in Spark

Review the list of Spark issues that are resolved in Cloudera Runtime 7.2.2.

CDPD-1138: Spark Atlas Connector tracks column-level lineage

This issue is now resolved.

CDPD-14906: Spark reads or writes TIMESTAMP data for values before the start of the Gregorian calendar. This happens when Spark is:

• Using dynamic partition inserts.
• Reading or writing from an ORC table when spark.sql.hive.convertMetastoreOrc=false (the default is true).
• Reading or writing from an Orc table when spark.sql.hive.convertMetastoreOrc=true but spark.sql.orc.impl=hive (the default is native).
• Reading or writing from a Parquet table when spark.sql.hive.convertMetastoreParquet=false (the default is true).

This issue is now resolved.

CDPD-15385: Currently, delegation token support for Spark DStreams is not available.

Added Kafka delegation token support for DStreams in the Spark 2.4.5. This issue is now resolved.

CDPD-15735: Oozie Spark actions are failing because Spark and Kafka are using different Scala versions.

This issue is now resolved.

CDPD-10532: Update log4j to address CVE-2019-17571

Replaced log4j with an internal version to fix CVE-2019-17571.

CDPD-10515: Incorrect version of jackson-mapper-asl

Use an internal version of jackson-mapper-asl to address CVE-2017-7525.

CDPD-7882: If an insert statement specifies partitions both statically and dynamically, there is a potential for data loss

To prevent data loss, this fix throws an exception if partitions are specified both statically and dynamically. You can follow the workarounds provided in the error message.

CDPD-15773: In the previous versions, applications that share a Spark Session across multiple threads was experiencing a deadlock accessing the HMS.

This issue is now resolved.