Splice Machine Touts Hybrid Big Data Workloads in Update

Splice Machine Inc. touted new hybrid workload capabilities in announcing version 2.5 of its Big Data platform, an open source SQL-based relational database solution powered by Apache Hadoop and Apache Spark.

Specifically, the company highlighted the ability of its flagship RDBMS to concurrently process large-scale workloads that are transactional in nature -- as in online transaction processing (OLTP) -- along with online analytical processing (OLAP) workloads. This is often called Hybrid Transactional and Analytical Processing (HTAP). HTAP offerings from other companies include SAP HANA, Oracle Exadata, MemSQL, VoltDB, NuoDB and several more.

Splice Machine open sourced its database in July when it came out with a community 2.0 edition in addition to its commercial edition, while also incorporating the dual-engine approach leveraging both Hadoop and Spark.

Spark was put to use for the OLAP side of things, while transactional processing was handled by other open source components of the Hadoop ecosystem: Apache HBase and the Hadoop Distributed File System (HDFS).

Company co-founder and CEO Monte Zweben summed up those and other hybrid capabilities in one statement announcing the new update.

"The new capabilities further emphasize the benefits of Splice Machine's hybrid architecture," Zweben said. "For modern applications that need to combine fast data ingestion, Web-scale transactional and analytical workloads, and continuous machine learning, one storage model does not fit all. The Splice Machine SQL RDBMS tightly integrates multiple compute engines, with in-memory and persistent storage in both row-based and columnar formats. The cost-based optimizer uses new advanced statistics to find the optimal execution strategy across all these resources for OLTP and OLAP workloads."

The addition of columnar external tables and in-memory caching are new in version 2.5, along with an improved cost-based optimizer that uses Yahoo's sketching library for better optimizer statistics, and cost-optimized storage for developers using the Amazon Web Services Inc. (AWS) cloud.

Interested developers can check out pricing and an FAQ for further information.

About the Author

David Ramel is an editor and writer for Converge360.