The Path to ScyllaDB 5.2
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ScyllaDB co-founder and CTO Avi Kivity will cover 2022 accomplishments and deliveries. Avi will focus on ScyllaDB core database in this talk. This year we enabled full repair-based-node-operations, brought distributed aggregation into Scylla as default, focused on goodput, at the face of overload and many other changes.
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The Path to ScyllaDB 5.2
- 1. ScyllaDB 5.2 and Beyond Fresh from the ScyllaDB Oven Avi Kivity, CTO and Co-Founder
- 2. Agenda ■ Increasing Streaming Robustness ■ Autoparallel Queries ■ WebAssembly User Defined Functions ■ Per-partition Throttling ■ Alternator Updates ■ Consistent Schema and Topology ■ New SSD Disk Modeling ■ Taming Corner Cases ■ What’s Cooking Now
- 3. Repair-Based Node Operations ■ Resumable bootstrap/decommission ■ Stream from primary replica ■ Or a quorum if primary is missing ■ Increases resilience and improves correctness
- 4. Autoparallel Queries ■ Aggregations previously done via Spark or custom code ■ Instead, recognize certain CQL patterns ■ Dispatch to all nodes, all vcpus within nodes Node 5 Node 1 Node 2 Node 4 Node 3 SELECT COUNT(*) FROM t
- 5. WebAssembly UDF/UDA ■ Push compute into database ■ Use any language* ■ Computations run in a WASM sandbox ■ Use case: analytics *as long as it’s Rust
- 6. Per Partition Rate Limit ■ New CQL table attribute to limit access rate to partition ■ Works for reads and writes ■ Prevent bot accounts from spamming database ■ “Hot partition”
- 7. Alternator Updates ■ Time-to-live Expiration ■ Improved performance
- 8. ■ Eliminate classes of operator errors ■ Concurrent schema changes ■ Concurrent topology operations ■ Lay groundwork for more advanced features ■ Concurrent node bootstrap/decommission ■ Tablets ■ Strong consistency Consistent Schema and Topology
- 9. ScyllaDB knows more about the disk operating envelope New SSD Disk Modeling
- 11. Reverse Queries ■ 4.5 and older slow for large partitions ■ 4.6 fast, but skipped cache ■ 5.0+ fast, supports cache ■ Works well with paging SELECT * FROM tab WHERE … ORDER BY clustering_key DESC
- 12. ■ Queries that encounter large consecutive tombstone runs are now well supported ■ Partitions with many range tombstones work well Better Handling of Tombstones
- 13. ■ Escalating countermeasures as memory usage increases ■ Prevent new queries from starting ■ Allow only one query to make progress ■ Kill all but one query Improved Out-of-Memory Handling
- 14. Repair-Based Tombstone Garbage Collection ■ Eliminate gc_grace_seconds ■ Tie tombstone garbage collection to last repair ■ Improves performance for clusters that have frequent repair ■ Improves correctness for clusters that missed repair
- 15. Cooking Now
- 16. Nudging the CQL Grammar Towards SQL ■ Relaxing constraints ■ Reconciling semantic oddities ■ Increasing the scope of autoparallel queries
- 17. ■ A spectrum of cost/performance tradeoffs ■ RAM: Extremely fast (100ns), very expensive ■ NVMe: Very fast, (100µs), expensive ■ HDD: Slow (10ms), cheap ■ Cloud Object storage (S3 and similar) ■ Slow (40ms), cheap ■ Infinitely expandable ■ Easy to manipulate ■ Shared access Object Storage
- 18. ■ Very dense databases ■ Where latency is not critical ■ Tiered storage ■ Mix service levels and cost ■ Optimize both cost and latency Use Cases for Object Storage
- 19. Thank You Stay in Touch Avi Kivity avi@scylladb.com @AviKivity @avikivity