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.

1. HIGH PERFORMANCE DATA STORAGE IN A
MICROSERVICES ENVIRONMENT

2. MICROSERVICES & DATA STORAGE
2

3. 3

4. 4

5. 5
BUSINESS DOMAIN SERVICES

6. JBOSS DATAGRID
6

7. 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 Overflow Mgmt.
to Persistence Store
(Hydration & Dehydration)
RDD/DStream Map-Reduce
Java Stream
In-
memory
Analytics
and
Compute
Grid
Application Frameworks
& APIs

8. UNLOCK YOUR DATA
8

9. GET VALUE OUT OF YOUR DATA
9
APACHE SPARK MICROSERVICES BUSINESS REPORTING

10. DEMONSTRATION
10

11. 11INSERT DESIGNATOR, IF NEEDED

12. String query =
"SELECT displayName, reputation FROM users ORDER BY
reputation DESC LIMIT 10";
List<Row> rows = sqlContext.sql(query).collectAsList();
InfinispanJavaRDD.write(rows,properties);

13. 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();

14. highestReputation
keyword
KeywordAnalytics
HighestRankAnalytics
DEMO DATA
PostStore
UserStore
owner = 2, dist_sync

15. AT A SCALE

16. plus.google.com/+RedHat
linkedin.com/company/red-hat
youtube.com/user/RedHatVideos
facebook.com/redhatinc
twitter.com/RedHatNews
THANK YOU