Yes, but not all SQL-standard insert variants . From: Debasish Das [mailto:debasish.da...@gmail.com] Sent: Wednesday, July 22, 2015 7:36 PM To: Bing Xiao (Bing) Cc: user; dev; Yan Zhou.sc Subject: Re: Package Release Annoucement: Spark SQL on HBase "Astro"
Does it also support insert operations ? On Jul 22, 2015 4:53 PM, "Bing Xiao (Bing)" <bing.x...@huawei.com<mailto:bing.x...@huawei.com>> wrote: We are happy to announce the availability of the Spark SQL on HBase 1.0.0 release. http://spark-packages.org/package/Huawei-Spark/Spark-SQL-on-HBase The main features in this package, dubbed “Astro”, include: • Systematic and powerful handling of data pruning and intelligent scan, based on partial evaluation technique • HBase pushdown capabilities like custom filters and coprocessor to support ultra low latency processing • SQL, Data Frame support • More SQL capabilities made possible (Secondary index, bloom filter, Primary Key, Bulk load, Update) • Joins with data from other sources • Python/Java/Scala support • Support latest Spark 1.4.0 release The tests by Huawei team and community contributors covered the areas: bulk load; projection pruning; partition pruning; partial evaluation; code generation; coprocessor; customer filtering; DML; complex filtering on keys and non-keys; Join/union with non-Hbase data; Data Frame; multi-column family test. We will post the test results including performance tests the middle of August. You are very welcomed to try out or deploy the package, and help improve the integration tests with various combinations of the settings, extensive Data Frame tests, complex join/union test and extensive performance tests. Please use the “Issues” “Pull Requests” links at this package homepage, if you want to report bugs, improvement or feature requests. Special thanks to project owner and technical leader Yan Zhou, Huawei global team, community contributors and Databricks. Databricks has been providing great assistance from the design to the release. “Astro”, the Spark SQL on HBase package will be useful for ultra low latency query and analytics of large scale data sets in vertical enterprises. We will continue to work with the community to develop new features and improve code base. Your comments and suggestions are greatly appreciated. Yan Zhou / Bing Xiao Huawei Big Data team
