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High Performance Data Storage in a Microservices Environment

Red Hat Developers
Red Hat Developers

This document discusses using microservices and in-memory data grids for high performance data storage and analytics. It shows how Apache Spark can be used for real-time analytics on data stored in an in-memory data grid. Examples are provided of SQL queries run on Spark to analyze user data and posts from a social network. The results are collected and written back to the data grid.

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HIGH PERFORMANCE DATA STORAGE IN A MICROSERVICES ENVIRONMENT
MICROSERVICES & DATA STORAGE 2
3
4
5 BUSINESS DOMAIN SERVICES
JBOSS DATAGRID 6
JBOSS DATA GRID IN-MEMORY DATA STORE ACCELERATE BIG DATA ANALYTICS 7 In-memory data platform BRMS Spark EAPHadoop Spring Boot etc. Cassandra / Red Hat Storage Data OverïŹ‚ow Mgmt. to Persistence Store (Hydration & Dehydration) RDD/DStream Map-Reduce Java Stream In- memory Analytics and Compute Grid Application Frameworks & APIs
UNLOCK YOUR DATA 8
GET VALUE OUT OF YOUR DATA 9 APACHE SPARK MICROSERVICES BUSINESS REPORTING
DEMONSTRATION 10
11INSERT DESIGNATOR, IF NEEDED
String query = "SELECT displayName, reputation FROM users ORDER BY reputation DESC LIMIT 10"; List<Row> rows = sqlContext.sql(query).collectAsList(); InfinispanJavaRDD.write(rows,properties);
String query = "SELECT u.location as `Location`,count(p.id) as `Posts` " + "FROM users u " + "INNER JOIN posts p ON u.id=p.ownerUserId " + "WHERE p.postTypeId=1 " + "GROUP BY u.location ” + "ORDER BY `Posts` DESC LIMIT 10"; List<Row> rows = sqlContext.sql(query).collectAsList();
highestReputation keyword KeywordAnalytics HighestRankAnalytics DEMO DATA PostStore UserStore owner = 2, dist_sync
AT A SCALE
plus.google.com/+RedHat linkedin.com/company/red-hat youtube.com/user/RedHatVideos facebook.com/redhatinc twitter.com/RedHatNews THANK YOU

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High Performance Data Storage in a Microservices Environment