How We Used Cassandra/Solr to Build Real-Time Analytics Platform
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The document outlines the process of building real-time analytics using DSE Enterprise, focusing on the choice of technologies like Cassandra/Solr for scalability and performance. It highlights the development of a micro-services architecture that enables near real-time data processing and analytics. Key challenges included technology selection and achieving real-time processing while managing latencies across clusters.
How We Used Cassandra/Solr to Build Real-Time Analytics Platform
- 1. BUILDING REAL-TIME ANALYTICS With DSE Enterprise. jKoolCloud.com 1
- 2. Objectives • Store everything, analyze everything… • Combined real-time & historical analytics • Fast response, flexible query capabilities • Target - for business user • Insulate us from underlying software • Hide complexity • Scale for ingesting data-in-motion • Scale for storing data-at-rest • Elasticity & Operational efficiency • Ease of monitoring & management 2
- 3. Technologies we considered? • SQL (Oracle, MySQL, etc.) • No scale. We have had a lot of experience our customer’s issues with this at our parent company Nastel… • RAM was “the” bottleneck. Commits take too long and while that is happening everything else stops • NoSQL • Cassandra/Solr (DSE) • Hadoop/MapReduce • MongoDB • Clustered Computing Platforms • STORM • MapReduce • Spark (we learned about this while building jKool) 3
- 4. Why we chose Cassandra/Solr? • Pros: • Simple to setup & scale for clustered deployments • Scalable, resilient, fault-tolerant (easy replication) • Ability to have data automatically expire (TTL – necessary for our pricing model) • Configurable replication strategy • Great for heavy write workloads • Write performance was better than Hadoop. • Insert rate was of paramount importance for us – get data in as fast as possible was our goal • Java driver balances the load amongst the nodes in a cluster for us (master-slave would never have worked for us) • Solr provides a way to index all incoming data - essential • DSE provides a nice integration between Cassandra and Solr • Cons: • Susceptible to GC pauses (memory management) • The more memory the more GC pauses • Less memory and more nodes seems a better approach than one big “honking” server (we see 6-8GB optimal, so far) • Data compaction tasks may hang 4
- 5. Why not Hadoop MapReduce? • MapReduce too slow for real-time workloads • Ok for batch, not so great for real-time • Need to be paired with other technologies for query (Hive/Pig) • Complex to setup, run and operate • Our goals were simplicity first… • Opted for STORM/SPARK wrapped with our own micro services platform FatPipes instead of the Map Reduce functionality 5
- 6. Why we chose Cassandra/Solr vs. Mongo? • Why not Mongo? • Global write-lock performance concerns… • Cassandra/Solr • Java based (our project was in Java) • Easy to scale, replicate data, • Flexible write & write consistency levels (ALL, QUORUM, ANY, etc.) • Did we say Java? Yes.(we like Java…) • Flexible choice of platform coverage • Great for time-series data streams (market focus for jKool) • Inherent query limitations in Cassandra solved via Solr integration (provided with DSE – as mentioned earlier) 6
- 7. How we achieved near real-time analytics? • Created our own micro-services architecture (FatPipes) which runs on top of: • STORM/JMS/Kafka • FatPipes can be embedded or distributed • Real-time Grid • Feeds tracking data and real-time queries to CEP and back • User interacts with Real-time via JKQL (jKool Query Language) • English like query language for analyzing data in motion and at rest. • “Subscribe” verb for real-time updates Real-time (Real-time.png) 7
- 8. Why clustered computing platforms? • STORM paired with Kafka/JMS and CEP • Clustered way to process incoming real-time streams • STORM handles clustering/distribution • Kafka/JMS for a messaging between grids • Split streaming workload across the cluster • Achieve linear scalability for incoming real-time streams • Apache Spark (alternative to MapReduce) • For distributing queries and trend analysis • Micro batching for historical analytics • Loading large dataset into memory (across different nodes) • Running queries against large data-sets 8
- 9. Key to Real-time Analytics • Process streams as they come while at the same time avoiding IO • Streams are split into real-time queue and persistence queue with eventual consistency (eventually… both real-time and historical must reconcile) • Both have to be processed in parallel • Writing to persistence layer and then analyzing will not achieve near-real time processing 9
- 11. Deeper View Web Application Server Web Application Server Web Application Server jKool Web Grid Cassandra Cassandra Cassandra Cassandra Storage Grid Solr Solr Solr Solr Search Grid Digest, Index Real-time Grid JKQL FatPipes Micro Services (INGEST) Compute Grid FatPipes Micro Services (REAL-TIME) (STORM/CEP) Distributed Messaging (JMS or Kafka) 11
- 12. Challenges we ran into? • So many technology options (…so little time…) • Deciding on the right combination is key early on • Cassandra/Solr deployment – (it was a learning experience for us) • Lots of configuration, memory management, replication options • Monitoring, managing clusters • Cassandra/Solr, STORM, Zookeeper, Messaging • +Leverage parent company’s AutoPilot Technology • Achieving near real-time analytics proved extremely challenging – but we did it! • Keeping track of latencies across cluster • Estimating computational capacity required to crunch incoming streams 12
- 13. Business Analyst User Interface It's easy to “visualize your data” 13
- 14. jKOOL IN REAL-TIME Real-time Demonstration of jKool’s usage of DSE