Tuning Java Driver for Apache Cassandra by Nenad Bozic at Big Data Spain 2017
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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.
Tuning Java Driver for Apache Cassandra by Nenad Bozic at Big Data Spain 2017
- 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
- 8. Cassandra Overview • partitioned data with tunable consistency • replication factor - how many replicas • masterless architecture • native multi-datacenter support
- 10. Architecture Client request Consistency level 1 Replication factor 3
- 11. Architecture Client request response Consistency level 1 Replication factor 3
- 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
- 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