couchbase-sqlpp-tuning

To tune slow Couchbase SQL++ queries, read the EXPLAIN plan to identify bottlenecks like PrimaryScan or Fetch operations. This skill diagnoses these execution plan issues and recommends matching covering or partial indexes to eliminate unnecessary data scans.

The best index design for Couchbase N1QL pagination problems uses covering or composite indexes that match query predicates and sort keys. This eliminates expensive Fetch operations and offset processing, drastically reducing latency for deep pagination requests.

A Couchbase query execution plan shows a PrimaryScan when missing or ineffective indexes force a full bucket scan. Fix it by creating targeted composite, partial, or functional indexes that match your query predicates to enable proper index span pushdown.

Yes, you can optimize Couchbase join performance and repeated prepared statements by analyzing query profiles and completed request diagnostics. This skill identifies inefficient join shapes and verifies runtime improvements through profile-driven validation of index strategies.

You should use array or functional indexes in Couchbase SQL++ when queries filter on array elements or computed expressions. This skill recommends these specialized index patterns to match complex query predicates and avoid costly post-fetch filtering.