Tuning N1QL Query Performance with Couchbase Server 4.0
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The document discusses optimizing N1QL query performance and scaling in Couchbase Server 4.0, emphasizing architectural overview, query processing, and indexing features. It outlines techniques for tuning queries, including execution plans, optimizing joins, and new consistency settings. Key advancements include improvements in indexing architecture and workload isolation, leading to better performance and scalability.
Tuning N1QL Query Performance with Couchbase Server 4.0
- 1. TUNING N1QL QUERY PERFORMANCE & SCALE IN COUCHBASE SERVER 4.0 Cihan Biyikoglu Dir. Product Management 1
- 2. ©2015 Couchbase Inc. 2 Goals Deeper look at query performance and scale Look at Query and Index Service Scale Characteristics Understand Query Execution Flow Understand Index Usage Tune queries with a few techniques
- 3. ©2015 Couchbase Inc. 3 Agenda Part I - Architectural Overview New Cluster Architecture with Couchbase Server 4.0 Query Processing & Indexing Part II - Optimizing Queries Execution Plans and Operators Optimizing Queries - Filtering, Index Selection and Joins OptimizingApps - Consistency Dials QA Demos & More Demos…
- 4. ©2015 Couchbase Inc. 4 Disclaimer Couchbase Server 4.0 and ForestDB are still in development and the final version of the products may not be identical in details discussed on this session.
- 6. ©2015 Couchbase Inc. 6 Couchbase Server Cluster Architecture STORAGE Couchbase Server 1 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Managed Cache Storage Data Service Index Service Query Service STORAGE Couchbase Server 2 Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 3 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 4 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 5 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 6 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service Managed Cache Storage Managed Cache Storage Managed Cache Storage Managed Cache Storage Managed Cache Storage
- 7. ©2014 Couchbase Inc. Couchbase Server Cluster Architecture STORAGE Couchbase Server 1 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Managed Cache Storage Data Service Index Service Query Service STORAGE Couchbase Server 2 Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 3 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 4 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 5 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service STORAGE Couchbase Server 6 SHARD 7 SHARD 9 SHARD 5 SHARDSHARDSHARD Managed Cache Cluster ManagerCluster Manager Data Service Index Service Query Service Managed Cache Storage Managed Cache Storage Managed Cache Storage Managed Cache Storage Managed Cache Storage
- 9. ©2015 Couchbase Inc. 9 Query Execution Submitting Queries in N1QL Stateless Connectivity through REST Load-Balance across Query Service nodes Prepared vs Ad-hoc Query Execution Consistency Dials – more on this later…
- 10. ©2015 Couchbase Inc. 10 Query Execution Parallelization factor is #cores on Query Service Node Execution Flow
- 11. ©2015 Couchbase Inc. 11 Query Service - Capacity Management Scaling the Query Service Pro: Load Balance Queries across all nodes Con: Compete with Index and DataWorkloads Index Service Couchbase Cluster Query Service Data Service node1 node8
- 12. ©2015 Couchbase Inc. 12 Query Service - Capacity Management Scaling the Query Service Added CPU: higher intra-query parallelization Added RAM: improved caching with larger result sets Added Node: better availability and load balancing Couchbase Cluster node1 node8 Data ServiceIndex Service Query Service
- 14. ©2015 Couchbase Inc. 14 Indexing in Couchbase Server 4.0 Multiple Indexers GSI – Index Service New indexing for N1QL for low latency queries without compromising on mutation performance (insert/update/delete) Independently partitioned and independently scalable indexes in Indexing Service Map/ReduceViews – Data Service Powerful programmable indexer for complex reporting and indexing logic. Full partition alignment and paired scalability with Data Service. SpatialView – Data Service Incremental R-tree indexing for powerful bounding-box queries Full partition alignment and paired scalability with Data Service New Index Scan
- 15. ©2015 Couchbase Inc. 15 Which to choose – GSI vsViews Workloads New GSI in v4.0 Map/ReduceViews Complex Reporting Just InTime Pre-aggregated Workload Optimization Optimized for Scan Latency & Throughput Optimized for Insertion Flexible Index Logic N1QL Functions Javascript Secondary Lookups Single Node Lookup Scatter-Gather Tunable Consistency Staleness false or ok or everything in between Staleness false or ok
- 16. ©2015 Couchbase Inc. 16 Which to choose – GSI vsViews Capabilities New GSI in v4.0 Map/ReduceViews Partitioning Model Independent – Indexing Service Aligned to Data – Data Service Scale Model Independently Scale Index Service Scale with Data Service Fetch with Index Key Single Node Scatter-Gather Range Scan Single Node Scatter-Gather Grouping,Aggregates With N1QL Built-in withViews API Caching Managed Not Managed Storage ForestDB Couchstore Availability Multiple Identical Indexes load balanced Replica Based
- 17. ©2015 Couchbase Inc. 17 Query Service - Capacity Management Scaling the Index Service Pro: Load balance scans across all nodes Con: Compete with Query and DataWorkloads Index Service Couchbase Cluster Query Service Data Service node1 node8
- 18. ©2015 Couchbase Inc. 18 Index Service Capacity Management Scaling the Index Service Added RAM: better caching of indexes Added CPU: faster index maintenance & parallelized index scans Add Faster IO Path: faster index persistence Added Node: better availability and load balancing Couchbase Cluster node1 node8 Data Service Index Service Query Service
- 20. ©2015 Couchbase Inc. 20 Execution Plans & Explain EXPLAIN query Plan is assembled into an execution flow expressed through the operators Operators stream results up and down the stream Sequence Parallel Primary Scan Initial Project Fetch Initial ProjectFetch Initial ProjectFetch … Limit
- 21. ©2015 Couchbase Inc. 21 Operators Main Operations Scans PrimaryScan: Scan of the Primary Index based on document keys IndexScan: Scan of the Secondary Index based on a predicate Fetch Fetch: Reach into the Data service with a document key Projection Operations InitialProject: reducing the stream size to the fields involved in query. FinalProject: final shaping of the result to the requested JSON shape
- 22. ©2015 Couchbase Inc. 22 Operators cont. Operator Assembly Parallel: execute all child operations in parallel Sequence: execute child items in a sequence Filtering Operators Filter:Apply a filter expression (ex.WHERE field = “value”) Limit: limit the number of items returned to N Offset: start returning items from a specified item count
- 23. ©2015 Couchbase Inc. 23 Operators cont. Join Operators Join: Join left and right keyspaces on attributes and document key Unnest: Join operation between a parent and a child with a nested array where parent is repeated for each child array item. Nest:Grouping operation between a parent and a child array where child array is embedded into the parent.
- 26. ©2015 Couchbase Inc. 26 Minimize Items Scanned Primary Index Scan vs. Index Scan Primary Index can only filter on document keys thus typically means “full-scan” of the bucket Secondary Index is typically done with predicates and are smaller in size thus better to scan Index Selection: Based on matching expressions matching in Index andWHERE clause DEMO #2 SELECT name,updated FROM `beer-sample` WHERE type="beer" AND abv>0 ORDER BY name LIMIT 10; Vs. CREATE INDEX i_type on `beer-sample`(type) USING GSI; SELECT name,updated FROM `beer-sample` WHERE type="beer" AND abv>0 ORDER BY name LIMIT 10;
- 27. ©2015 Couchbase Inc. 27 Minimize Items Scanned HINT index usage to queries There can be multiple indexes with to choose from and you can hint index choice to us. SELECT name,updated FROM `beer-sample` USE INDEX(i_type using gsi) WHERE type="beer" AND abv>0 ORDER BY name LIMIT 10;
- 28. ©2015 Couchbase Inc. 28 Minimize Items Scanned Limit & Filters help eliminate rows early in the execution plan With Limit, Upstream operators are signaled to stop by limit when enough rows accumulate Ex: Remember to Filter on Document type with buckets that contain multiple types. DEMO #3 SELECT b1.name as beer_name, b2.name as brewery_name, b2.country FROM `beer-sample` AS b1 JOIN `beer-sample` AS b2 on KEYS b1.brewery_id WHERE abv>0; vs SELECT b1.name as beer_name, b2.name as brewery_name, b2.country FROM `beer-sample` AS b1 JOIN `beer-sample` AS b2 on KEYS b1.brewery_id WHERE b1.type="beer” and abv>0;
- 29. ©2015 Couchbase Inc. 29 Joins Joins are efficient by nature Left hand value is joined to the right hand document key with nested loop. Query: Get brewery location for each beer: SELECT … FROM `beer-sample` AS b1 JOIN `beer-sample` AS b2 on KEYS b1.brewery_id WHERE b1.type="beer”; For each document with type=“beer” take b1.brewery_id and look for and equal document key in b2.
- 31. ©2015 Couchbase Inc. 31 New Consistency Settings! View Stale-ness Ok: unbounded – query what’s available in the index/view now False: query after all changes up to the request timestamp (and maybe more) has been indexed for a given index or view. New Indexes with Couchbase Server 4.0 Improves granularity of the consistency logical-timestamp. New: ScanConsistency can be set to any logical timestamp Indicate stale=false to stale=ok and everything in between
- 32. ©2015 Couchbase Inc. 32 Flexible Consistency Settings Time t1 insert (k1, v1) … t2 do other business logic computation … t3 issue query/read on (k1,v1) with t3 vs t1 Catch up all the indexes to t3 and then issue query Identical to “stale=false” Catch up all the indexes to t1 and then issue query Improved efficiency over “stale=false”
- 33. Recap
- 34. ©2015 Couchbase Inc. 34 Recap New Unique Query and Indexing Architecture Workload isolation with MDS gives you a great performance and scale advancement. Familiar Concepts from your past life will help tune queries Understand Execution Plans Understand Indexes and Index Selection Filter & Limit aggressively Understand JOINs Use powerful new Consistency Dials for best efficiency
- 37. Thank you.
Editor's Notes
- #10: Application has single logical connection to cluster (client object) Data is automatically sharded resulting in even document data distribution across cluster Each vbucket replicated 1, 2 or 3 times (“peer-to-peer” replication) Docs are automatically hashed by the client to a shard’ Cluster map provides location of which server a shard is on Every read/write/update/delete goes to same node for a given key Strongly consistent data access (“read your own writes”) A single Couchbase node can achieve 100k’s ops/sec so no need to scale reads
- #16: Flip m/r index vs gsi on the graph
- #17: Flip m/r index vs gsi on the graph
- #21: ParentScan KeyScan ValueScan DummyScan CountScan IntersectScan
- #23: Discard Stream Collect Channel