Apache Cassandra: NoSQL in the enterprise
- 1. Apache Cassandra: NoSQL in the Enterprise, today Jonathan Ellis CTO @spyced
- 2. Cassandra Job Trends (indeed.com)
- 4. Why Big Data Matters Research done by McKinsey & Company shows the eye-opening, 10-year category growth rate differences between businesses that smartly use their big data and those that do not.
- 5. Big data Analytics Realtime ? (Hadoop) (“NoSQL”)
- 6. Some users ✤ Financial ✤ Social Media ✤ Advertising ✤ Entertainment ✤ Energy ✤ E-tail ✤ Health care ✤ Government
- 7. Common use cases ✤ Time series data ✤ Messaging ✤ Ad tracking ✤ Data mining ✤ User activity streams ✤ User sessions ✤ Anything requiring: Scalable + performant + highly available
- 8. Why Cassandra? ✤ Fully distributed, no SPOF ✤ Multi-master, multi-DC ✤ Linearly scalable ✤ Larger-than-memory datasets ✤ Best-in-class performance (not just writes!) ✤ Fully durable ✤ Integrated caching ✤ Tuneable consistency
- 9. Classing partitioning with SPOF partition 1 partition 2 partition 3 partition 4 slave slave master request router
- 10. Fully distributed, no SPOF client p3 p6 p1 p1 p1
- 14. “With Cassandra, we get better business agility, and we don’t have to plan capacity in advance, we don’t need to ask permission of other people to build things for us, and we don’t worry about running out of space or power.” Adrian Cockcroft, Cloud Architect
- 15. Netflix on Cassandra ✤ Could not build datacenters fast enough ✤ Made decision to go to cloud (AWS) ✤ Applications include Netflix’s subscriber system, AB testing, and viewing history service ✤ Over a year in, Netflix finds Cassandra to be ✤ Fast ✤ Cost-effective ✤ Scalable ✤ Flexible ✤ Reliable: no SPOF
- 16. “Without Cassandra, our engineers would’ve had to create something that could scale to our needs, that would’ve prevented us from focusing on building product and solving problems for Backupify’s users, which are far more important tasks.” Matt Conway, VP Engineering
- 17. Backupify on Cassandra ✤ Cloud-based utility that enables businesses and consumers to backup, search and restore the content of popular online applications such as Google Apps, Gmail, Facebook, Twitter, and Blogger ✤ Cassandra findings: ✤ Solved scaling, allowing engineers to focus on their business ✤ DataStax OpsCenter made it easy to monitor the health and performance of their cluster ✤ Reliable, redundant and scalable data storage helped eliminate down-time ✤ Ability to offer both backup and storage, but also analysis
- 18. “You can seamlessly add new nodes and expand your total capacity without deteriorating the performance of the data store. Cassandra has allowed us to scale very effectively.” Harry Robertson, Tech Lead
- 19. Ooyala on Cassandra ✤ Ooyala provides a suite of technologies and services that support content owners in managing, analyzing and monetizing the digital video they publish online ✤ Cassandra findings: ✤ Classic “Big Data” problem did not require re-architecting ✤ Delivered ability to respond to increasingly sophisticated analytic needs of customers ✤ Developers spend time building application features, not figuring out how to scale
- 20. “Cassandra has allowed us to build bigger features faster and more reliably, while using less money and without needing to expand our staff.” Kyle Ambroff, Sr. Engineer
- 21. Formspring on Cassandra ✤ Users of Formspring engage with and learn more about each other by asking and responding to questions. Close to 4B responses in the system and 30M unique users ✤ Cassandra experience ✤ No sharding needed – just add nodes to scale ✤ Performance – the popular users with many followers saw no speed reduction. No more memcached! ✤ Flexibility of a schema-optional architecture is very developer friendly
- 22. Big data Analytics Realtime ? (Hadoop) (“NoSQL”)
- 23. The evolution of Analytics Analytics + Realtime
- 24. The evolution of Analytics replication Analytics Realtime
- 25. The evolution of Analytics ETL
- 26. Big data Analytics Datastax Realtime (Hadoop) Enterprise (“NoSQL”)
- 27. DataStax Enterprise re-unifies realtime and analytics
- 29. Portfolio Demo dataflow Portfolios Portfolios Historical Prices Live Prices for today Intermediate Results Largest loss Largest loss
- 30. Operations ✤ “Vanilla” Hadoop ✤ 8+ services to setup, monitor, backup, and recover (NameNode, SecondaryNameNode, DataNode, JobTracker, TaskTracker, Zookeeper, Region Server,...) ✤ Single points of failure ✤ Can't separate online and offline processing ✤ DataStax Enterprise ✤ Single, simplified component ✤ Self-organizes based on workload ✤ Peer to peer ✤ JobTracker failover
- 31. Managing & Monitoring Big Data ✤ DataStax OpsCenter manages and monitors all Cassandra and Hadoop operations
- 32. Questions?