SF ElasticSearch Meetup 2013.04.06 - Monitoring
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Using monitoring tools Zabbix for systems-level monitoring of ElasticSearch and SPM (http://sematext.com/spm/elasticsearch-performance-monitoring/index.html) for ElasticSearch-specific monitoring. Using these tools was crucial was optimizing index building performance as well as query performance. Some general tips for index building and query performance.
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SF ElasticSearch Meetup 2013.04.06 - Monitoring
- 1. Monitoring tools for ElasticSearch SF Meetup 2013.03.06 Sushant Shankar Shyam Kuttikkad
- 2. • Why and how we use ElasticSearch • Monitoring – Tools – Index Building – Query Performance
- 3. Who is asdfas • Social Sharing and Content Discovery platform – We help >600,000 publishers with content distribution, user engagement, and advertising monetization – 450 Fortune 1000 brand marketers leverage our unique social signals to deliver impactful advertising • We develop Machine Learning algorithms operating on Big Data to: – Provide content sharing insights to Publishers – Build customized audience segments for advertising campaigns – Extract actionable insights out of social and interest data www.33Across.com www.tynt.com
- 4. Data firehose of 30B monthly events, 1.25B cookies - Interaction with web content - Shares – images, copies - Searches Build, understand, analyze Real-time view ElasticSearch! Social Audiences Behavior Context Knowledge
- 5. Production ElasticSearch cluster Hardware 6 nodes, 24GB RAM 16GB for ES service 4 cores 3x 1.5TB drive Index Build index >1TB/index using MR job (replicated) and Bulk API ~300M documents ~5KB / document ~3 hours
- 6. System monitoring using Zabbix Index Build
- 7. ElasticSearch specific monitoring using SPM Scalable Performance Monitoring (http://sematext.com/spm/index.html) • Index stats – Total/Refreshed/Merged documents • Shards – Total/Active/Relocating/Initializing • Search - Request rate and latency • Cache – {Filter, field} cache {count, evictions, size} • Machine – CPU, Memory, JVM, GC, Network, Disk
- 8. Index Building Optimization using Zabbix and SPM Amount bulk indexed Time taken CPU util. Mem util. Disk I/O Network # Shards
- 9. in practice…
- 10. Debugging and Validating using SPM
- 11. Index Building: Learnings • 2 shards / CPU • 10,000 documents (users) per indexing request • Bulk API for our use case • No replicas • Refresh off (index.refresh_interval = -1)
- 12. Query Performance: Learnings • 1-2 Replicas (and for reliability) • Turn refresh on again (5s default) • Warm up effect (Index Warm up API 0.20+) • Optimize API • Simulate multiple users
- 13. QUERIES?
- 14. Sushant Shankar sushant.shankar@33across.com Shyam Kuttikkad shyam.kuttikkad@33across.com
- 15. Why we really need a search engine Batch! Good for complicated tasks (Machine Learning, Graph Algorithms, etc.) … …
- 16. Warm Up: load into memory and cache
- 17. Other cool features • Custom Scoring functions • Scripts – MVEL, Python • Facets • Exploring: • Real-time indexing • Indexing images, files, etc. • Parent-child relationships
Notes de l'éditeur
- http://www.zabbix.com/ - ‘’Enterprise class monitoring solution for everyone’
- http://www.zabbix.com/ - ‘’Enterprise class monitoring solution for everyone’
- Collect information over 1B users internationally – text copied from over 600K publisher sites, images, searches, pages visitedDifferent slices of data – now!