The document provides guidance on tuning the Java driver for Apache Cassandra, highlighting the architecture, data modeling, and specific use cases for high availability and eventual consistency. It discusses tuning options including load balancing, pooling, speculative executions, timeouts, and retry policies tailored to different scenarios like click stream data and mission-critical information. Key takeaways emphasize the importance of understanding use cases and proper monitoring to optimize database performance.

3. Tuning Java Driver for
Apache Cassandra
November 2017
Nenad Bozic
@NenadBozicNs
nenad.bozic@smartcat.
SmartCat
www.smartcat.
io

4. When people start with Apache Cassandra

5. When people call us for help

6. Agenda
• intro to Apache Cassandra
• tuning options in driver
• use cases
• takeaways and Q&A

7. Apache Cassandra

8. Cassandra Overview
• partitioned data with tunable consistency
• replication factor - how many replicas
• masterless architecture
• native multi-datacenter support

9. Architecture
Client contact

10. Architecture
Client request
Consistency level 1
Replication factor 3

11. Architecture
Client request
response
Consistency level 1
Replication factor 3

12. Architecture
DC1 DC2
Cluster

13. Data Modeling
• query based modeling
• data is denormalized
• data is duplicated

14. Use Cases
• when high availability is crucial, and eventual consistency is tolerable
• event sourcing
• logging continuous streams of data
• deep visitor analytics
• early prototyping with significant query changes
• referential integrity required
• dynamic access patterns on data

15. Tuning options in driver

16. Drivers for Apache Cassandra

17. Load balancing
https://www.slideshare.net/planetcassandra/apache-cassandra-and-drivers

18. Data Center Aware Load Balancing
https://www.slideshare.net/planetcassandra/apache-cassandra-and-drivers

19. Toke Aware Load Balancing
https://www.slideshare.net/planetcassandra/apache-cassandra-and-drivers

20. Latency Aware Load Balancing

21. Pooling options
• driver communicates with cluster with pool of connections
• changed between V2 and V3 version of protocol (core lowered to 1)
• going for more requests on connection can put more load to cluster
• add monitoring of in flight queries on driver side and tune for your use case

22. Pooling options

23. Speculative executions
• spawn additional queries to other nodes after configured time
http://docs.datastax.com/en/developer/java-driver/3.1/manual/speculative_execution/

24. Speculative executions
• constant speculative execution policy
• percentile speculative execution policy

25. Timeouts
• driver read timeout vs server read timeout
• driver settings for all queries or per query settings
• setReadTimeoutMillis and setConnectionTimeoutMillis

26. Retry policies
• fail early and retry
• add retry policy or speculative execution
• downgrading retry policy if inconsistent data is more important than no data

27. Use cases

28. Click stream and IoT measurements
• visualize measurements from many devices
• fast access with tolerable inconsistencies
• DC aware and token aware policy to land on local node with data
• lower consistency level (ONE) or use downgrading retry policy
• use speculative executions to query more nodes if cluster can manage load

29. Mission critical data with tolerable performance
• stock data in warehouse used to compare with ERP system
• high consistency (read + write > replication factor)
• retry and reconnect policy is a must
• choose lower requests per connection numbers not to overload cluster
• set lower read timeout to fail early and retry

30. Write heavy low latency read use case
• ad serving (store user analytics and serve ads fast)
• separate read and write for different tuning options
• latency aware policy on reads to choose always fast performing nodes
• lower down read timeout on driver and server to fail early
• increase maximum requests per connection

31. Conclusion

32. Conclusion and take aways
• know your use case and know your database
• each tuning options requires good monitoring
TEST
ADJUST MEASURE

33. Links
• SmartCat Blog post - Tuning Java driver for Apache Cassandra - part 1
• SmartCat Blog post - Tuning Java driver for Apache Cassandra - part 2
• Use case example - Tuning for heavy write and low latency read scenario

34. Q&A

35. Thank you
Nenad Bozic
@NenadBozic
Ns
SmartCat
www.smartcat.i
o