Implement an efficient search algorithm for large data sets in Android.

Official answer

Define the search contract before choosing an index: exact identifier, prefix, token/phrase or semantic retrieval; searchable fields, language rules, access scope and freshness. An indexed equality/prefix lookup and full-text retrieval solve different problems.

For local structured data, use appropriate SQLite indexes and bounded queries. Room supports FTS entities, such as FTS4 for a compatible setup, but FTS is token-based rather than an arbitrary substring or typo-correction engine. Keep the search index synchronized with its source. Bind SQL parameters; also treat the MATCH value as a separate query language, because SQL parameterization does not remove its operators or malformed syntax. Decide whether to accept advanced syntax or construct a validated literal-token/phrase query.

For remote search, enforce authorization on the server and return a bounded page plus stable continuation information. Do not download an entire corpus to search it on the UI thread. Cache by account/access scope, query and relevant version/filter parameters and invalidate appropriately.

Debounce or otherwise limit rapid input, cancel obsolete work where possible and tag results with their query/generation so a late old response cannot replace the newest result. Cancellation alone may not stop work already accepted remotely. Provide empty-query, no-results, offline and retry states.

Test expected results, ranking and access with a labeled query set, including accents, punctuation, duplicates and denied records. Measure latency, memory, request count and relevance separately. Click-through reflects position and presentation as well as relevance, so it is not an accuracy metric by itself.

Room full-text entities, SQLite FTS query syntax, Coroutine execution