Global Secondary Indexes
Global Secondary Indexes (GSI) support a variety of OLTP like use-cases for N1QL including basic, ad-hoc, and short-running reporting queries that require filtering. For example, if you have a WHERE clause in a N1QL statement that selects a subset of your data on which the secondary index is defined, you can see a significant speedup in the N1QL query performance by using GSI.
Global secondary indexes are deployed independently into a separate index service within the Couchbase Server cluster, away from the data service that processes key based operations. GSIs provide the following advantages:
Predictable Performance: Core key based operations maintain predictable low latency even in the presence of a large number of indexes. Index maintenance does not compete with key based operations even under heavy mutations to data.
Low Latency Queries: GSIs independently partition into the index service nodes and don’t have to follow hash partitioning of data into vBuckets. Queries using GSIs can achieve low latency response times even when the cluster scales out because GSIs don’t require a wide fan-out to all data service nodes.
Advanced Scaling Model: GSI can be placed onto independent set of nodes. Administrators can add new indexes and evolve the application performance without stealing cycles from the incoming workload.
You can define a primary or secondary index using GSIs in N1QL using the CREATE INDEX statement and the USING GSI clause. For more information on the syntax and examples, see CREATE INDEX statement.
Index Partitioning increases query performance, by dividing and spreading a large index of documents across multiple nodes. This feature is available only in Couchbase Server Enterprise Edition.
The benefits include:
The ability to scale out horizontally, as index size increases.
Transparency to queries, requiring no change to existing queries.
Reduction of query latency for large, aggregated queries; since partitions can be scanned in parallel.
Provision of a low-latency range query, while allowing indexes to be scaled out as needed.
For detailed information, see Index Partitioning.