significant advances MemSQL, a real-time database provider for transactions and analysis, announced for pipelines real-time data for Apache Spark, as well as support for the Python language and access of non-uniform memory.
MemSQL can now execute SQL queries inside Spark of MemSQL database, provide in- browser Python programming, and automatically optimize NUMA deployments . These features generate quick results and faster scans for data scientists.
"The new version of Ops MemSQL reinforces our commitment to the Spark community to provide even faster access to real-time data and analysis," said Eric Frenkiel, co-founder and CEO MemSQL . "Our mission is to provide technology that integrates the progress through the open source ecosystem that appeals to the general programming community."
As a transitional treatment setting, Spark is well suited for Data analysis and development model , but it is purpose built for high performance SQL . To this end, Spark MemSQL now allows SQL queries to run within the MemSQL database, which would be able to improve performance by up to 50x on many workloads. Combining MemSQL with Spark, data scientists can exploit a permanent transactional database to feed the latest business data in their models for real-time analysis.
The combination of Spark and other MemSQL unifies the processing in memory with memory storage for "lightning fast" results. Users have access to a familiar SQL interface, which would provide the performance and persistence to perform real-time data pipelines successfully. Spark data transformation capabilities can be fully utilized when paired with distributed memory stores like MemSQL compared to stores using traditional drives as HDFS.
NUMA Environment
The latest version of MemSQL Ops also features in the browser Programming Python , which opens the vast Python library packages of analysis such as Numpy, Scipy and Pandas to users running MemSQL.
for MemSQL users in a NUMA environment, MemSQL Ops now offers point-and-click installation. MemSQL Ops can intelligently map the MemSQL instances CPUs that share the local memory. The increased efficiency on large server deployments can speed up queries up to 40%. The execution of ultrafast motions for efficient storage of corporate data, MemSQL allow users to operate with maximum efficiency in the production fast paced environments.
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