Developing with Couchbase: App Development with Indexes and Queries
Deep Dive into N1QL with Global Secondary Indexes – Couchbase Live New York 2015
Transcript of Deep Dive into N1QL with Global Secondary Indexes – Couchbase Live New York 2015
Deep Dive into N1QL & Indexing
Cihan Biyikoglu | Dir. Product Management, Couchbase
©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
©2015 Couchbase Inc. 3
Agenda Part I - Architectural Overview
– Architecture with Couchbase Server 4.0– Query Processing & Indexing
Part II - Optimizing Queries– Execution Plans and Operators
QA
Architecture OverviewPart I
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Couchbase Server Cluster Architecture
STORAGE
Couchbase Server 1
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Managed CacheStorage
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
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 4
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 5
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 6
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
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Couchbase Server Cluster Architecture
STORAGE
Couchbase Server 1
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Managed CacheStorage
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
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 4
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 5
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
STORAGE
Couchbase Server 6
SHARD7
SHARD9
SHARD5
SHARDSHARDSHARD
Managed Cache
Cluster ManagerCluster Manager
Data Service
Index Service
Query Service
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
Managed CacheStorage
Query Processing Overview
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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…
Execution Flow
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Query Service - Capacity Management Scaling the Query Service
– Pro: Load Balance Queries across all nodes– Con: Compete with Index and Data Workloads
Index Service
Couchbase Cluster
Query ServiceData Service
node1 node8
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Query Service - Capacity Management Scaling the Query Service
– Added CPU: higher concurrent query execution and 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
Indexing Overview
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Indexing in Couchbase Server 4.0Multiple 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/Reduce Views – Data Service– Powerful programmable indexer for complex reporting and indexing logic. – Full partition alignment and paired scalability with Data Service.
Spatial View – Data Service– Incremental R-tree indexing for powerful bounding-box queries– Full partition alignment and paired scalability with Data Service
New
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Query and Index TodayOnce upon a time in a User Profile System…. Q1: Find the top 10 most “active” customer by #logins in
AUG 2015
{…“customer_name” : ”Cihan”,“total_logins”: {…
“aug_2015”:100,…}
“type” : “customer_profile”…}
…
Q1Active @ Jan
2015
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Query and Index TodayINDEX ON Customer_bucket(customer_name, total_logins.jan_2015)WHERE type=“customer_profile”;
SELECT customer_name, total_logins.jan_2015 FROM customer_bucketWHERE type=“customer_profile”ORDER BY total_logins.aug_2015 DESC LIMIT 10;
…
Q1Active @ Jan
2015
Q1: Execution Plan on N nodes• Scatter: Execute Q1 on N nodes• Gather: gather N results• Finalize: Execute Q1 on
governor node
1
2 2 2 2 2
3
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Query and Index with GSIINDEX ON Customer_bucket(customer_name, total_logins.jan_2015)WHERE type=“customer_profile”;
SELECT customer_name, total_logins.jan_2015 FROM customer_bucketWHERE type=“customer_profile”ORDER BY total_logins.aug_2015 DESC LIMIT 10;
…
Q1Active @ Jan
2015
Q1: Execution Plan on N nodes• Execute Q1 on N1QL Service
node• Scan index on Index Service node
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Introducing Global Secondary IndexesWhat are Global Secondary Indexes? High performance indexes for low latency queries with powerful caching, storage and independent placement.
Power of GSI– Fully integrated into N1QL Query Optimization and
Execution– Independent Index Distribution for Limiting scatter-gather– Independent Scalability with Index Service – more on this
later– Powerful caching and storage with ForestDB
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Which to choose – GSI vs Views
Workloads New GSI in v4.0
Map/Reduce Views
Complex Reporting
Just In Time 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
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Which to choose – GSI vs Views
Capabilities New GSI in v4.0
Map/Reduce Views
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 with Views API
Caching Managed Not Managed
Storage ForestDB Couchstore
Availability Multiple Identical Indexes load balanced
Replica Based
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Index Service - Capacity Management Scaling the Index Service
– Pro: Load balance scans across all nodes– Con: Compete with Query and Data Workloads
Index Service
Couchbase Cluster
Query ServiceData Service
node1 node8
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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
Data ServiceQuery Service
Index Service
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Data Service
Projector & Router
Indexing Service
Query ServiceIndex Service
SupervisorIndex maintenance &
Scan coordinator
Index#2
Index#1
Query Processorcbq-engine
Bucket#1
Bucket#2
DCP Stream Index#4Index#3
...Bucket#2
Bucket#1
Projector and Router: 1 Projector and Router per node1 stream of changes per buckets per supervisor
ForestDBStorage Engine Supervisor
1 Supervisor per nodeMany indexes per Supervisor
Optimizing QueriesPart II
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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 ParallelPrimary
Scan
InitialProjectFetch
InitialProjectFetch
InitialProjectFetch
…
Limit
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OperatorsMain 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
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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
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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.
DEMO
Execution Plans
Common Techniques for Tuning Queries
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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 and WHERE 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;
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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;
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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.
Optimizing Applications
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FUTURE - 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: Scan Consistency can be set to any logical timestamp
– Indicate stale=false to stale=ok and everything in between
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FUTURE - Flexible Consistency SettingsTime
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”
Recap
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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
Soon - Use powerful new Consistency Dials for best efficiency
Get Started with Couchbase Server 4.0 - Couchbase.com/Downloads
Q&ACihan Biyikoglu | [email protected] |
@cihangirb