About DataStax Enterprise

New features  

DataStax Enterprise 4.5 introduces Apache Spark 0.9.1 and Shark 0.9.1.1 integration for running performant analytical queries independent of Hadoop. Spark is a distributed, parallel, batch data processing engine based on the Resilient Distributed Datasets (RDD) concept instead of the MapReduce concept upon which Hadoop is based. Spark is typically faster than Hadoop.

Shark, a SQL-like, Hive-compatible language is built on top of Spark. The transition for users of Hive, which is the SQL-like language built on top of Hadoop, is painless.

A bring your own Hadoop (BYOH) model integrates Hadoop data warehouse implementations Cloudera and Hortonworks. This model can provide better performance through custom, better-tuned Hadoop than previous DataStax Enterprise versions.

DataStax Enterprise 4.5 improves integration of Apache Sqoop for importing RDBMS data and exporting Cassandra CQL data.

The About the Performance Service automatically collects and organizes performance diagnostic information into a set of data dictionary tables that can be queried with CQL. Use the information gathered in the diagnostic tables to examine your database metrics and improve the function of your clusters, queries, and nodes.

DataStax Enterprise 4.5 also introduces a number of DSE Hadoop, Hive, and Pig features discussed later in this document and the following Solr features.

• For performance, you can now configure DSE Search/Solr to parallelize row reads.
• DSE Search uses the faster doc values-based join system under certain circumstances.
• DataStax Enterprise 4.5 and later moves the DSE per-segment filter cache off-heap by using native memory, hence reducing on-heap memory consumption and garbage collection overhead.
• The new off-heap filter cache is enabled by default, but can be disabled at startup time.