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Breaking Out of the Proprietary Cage - Real-time Data Warehouses in Open Source
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- 1 Intro
- 2 Presenter Bio
- 3 What makes analytic applications special?
- 4 SQL data warehouses run analytic queries
- 5 What ClickHouse is not
- 6 Merge Tree is the workhorse table engine
- 7 Merge Tree data layout
- 8 Detailed storage layout within a single part /var/lib/clickhouse/data/airline/ontime
- 9 Adding CPUs boosts parallelized execution
- 10 I/O drives ClickHouse performance
- 11 Compression and codecs reduce I/O
- 12 Effect on storage is dramatic
- 13 Materialized views restructure/reduce data
- 14 Pattern: TTLs + downsampled views
- 15 Alternative pattern: Tiered storage
- 16 More table engines for clustering!
- 17 How do distributed queries work?
- 18 Pattern: Kafka-based ingestion pipelines
- 19 Alternative ingest pattern: Kafka engine
- 20 Pattern: Grafana visualization
- 21 Pattern: Operation on Kubernetes
- 22 Wrap-up . ClickHouse meets/beats proprietary SQL data warehouses in head-to-head comparisons
