The newest versions of SQLFire and GemFire XD are one and the same: Pivotal GemFire XD version 1.3. What were previously two separate products are now merged, so current licensees of either product are entitled to upgrade to the new version.
GemFire XD was previously spawned from SQLFire. As you can see below, users of prior versions of GemFire XD will see a relatively small delta from the Pivotal HD-integrated solution they are currently running. SQLFire customers will find a significant improvement with some major new capabilities. Customers using either of the previously separate products will enjoy the following updates and enhancements:
What’s New for GemFire XD Customers
For GemFire XD customers, the version 1.3 enhancements are incremental improvements upon existing features:
- Scale and performance: Optimization of concurrent writes to HDFS.
- Consistent Database Operations: Improved resilience of transactions over distributed Apache Hadoop® nodes.
- High availability and resilience: Data extractor utility allows for disaster recovery.
- Developer features: Including a .NET entity framework that generates object models from a data schema, and/or a relational data schema from an object model, an implementation of Language Integrated Query (LINQ), indexing improvements, and the ability to cancel queries programmatically.
- Simplified administration: Automated setup of Kerberos security, and GemFire XD service roles within a Pivotal HD cluster using the cluster install wizard.
What’s New for SQLFire Customers
SQLFire customers gain the ability to leverage Apache Hadoop® as a persistence layer, enabling a number of new capabilities:
- Pivotal HD Persistence: Store and archive very large data stores. (A cluster of Apache Hadoop® is required to enable this capability.)
- Add consistent read-write capability to Apache Hadoop®: The Apache Hadoop® file system (HDFS) is “append-only”. GemFire XD will allow users to effectively treat Apache Hadoop® as a read-write store by managing the compaction of files. Data will have database consistency, and users can scale up write capacity to handle analytic processing and ingest of high volume streaming data.
- Enable advanced analytics: Take advantage of the ability to easily analyze application data, transactional history, and system logging through the ability to leverage Apache Hadoop® tools, and through an integration to Pivotal HAWQ, Pivotal’s SQL query engine for Apache Hadoop®. An extension of the MapReduce API allows Apache Hadoop® MR jobs to read and write persisted GemFire data without having to actually access a GemFire distributed system.
These capabilities essentially allow users to create a scale-out transactional application with built in advanced analytics and BI capacity. Not only will they be able to see what’s going on in the business processes powered by an application, but users can utilize the analysis to continuously optimize the application’s behavior.
In addition all the previously mentioned new capabilities and improvements, SQLFire customers will enjoy the following enhancements:
- Scale and performance: Off-heap table storage allows more efficient use of higher-memory systems as nodes, and improves CPU utilization by avoiding Java garbage collection processes.
- High availability, resilience, and global scale: Resilient self-healing of clusters, and detection of WAN conflicts with error handlers, enabling automated management.
- Developer features: An Open Database Connectivity (ODBC) driver is now supported.
- Simplified administration: Includes integration with the GemFire online monitoring tool.
For more details on these features, please refer to the release notes:
Developer install options:
Maven Repository/ Distribution:
Editor’s Note: Apache, Apache Hadoop, Hadoop, and the yellow elephant logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
About the Author