If you want full-text search without building your own search engine, the usual options are:

1) Use a hosted search service

Best if you want something quick and reliable.

Popular options:

• Algolia — very fast, great developer experience, strong typo tolerance
• Meilisearch — simple, open-source, easy to run or use hosted
• Typesense — fast, typo-tolerant, easy setup
• Elastic Cloud / OpenSearch — more powerful, more complex
• Azure Cognitive Search / Vertex AI Search / AWS OpenSearch — good if you’re already on those clouds

How it usually works

1. Your app stores data in its normal database.
2. You sync records to the search service.
3. Users type queries.
4. Your app sends the query to the search service.
5. The service returns ranked results, filters, highlights, facets, etc.

Why this is good

• No need to design indexing/ranking yourself
• Typo handling, relevance tuning, facets, autocomplete are often built in
• Scales well

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2) Use your database’s built-in full-text search

Best if your needs are modest and you want fewer moving parts.

Examples:

• PostgreSQL: full-text search with tsvector, tsquery, GIN indexes
• MySQL/MariaDB: FULLTEXT indexes
• SQLite: FTS5 extension

Pros

• Simple architecture
• No extra service
• Good for small to medium search needs

Cons

• Less powerful than dedicated search engines
• Relevance tuning and typo tolerance are usually weaker

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3) Use a search library/framework plugin

If your framework already supports it, this can save time:

• Laravel Scout with Algolia/Meilisearch
• Django Haystack or integrations with Elasticsearch/OpenSearch
• Rails gems for search integrations
• Prisma + external search sync patterns

This is often just a convenience layer over option 1 or 2.

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A practical recommendation

If you want the fastest path:

• Start with Meilisearch or Algolia if search is a core feature
• Start with PostgreSQL FTS if search is simple and your data is already in Postgres

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Minimal implementation pattern

No matter what tool you choose, the pattern is usually:

• Source of truth: your primary DB
• Search index: separate search engine/index
• Sync process:
  • on create/update/delete, update the search index
  • use background jobs or events if possible
• Query path:
  • search service returns IDs + ranking
  • fetch full records from your DB if needed

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Things to plan for

• Indexing latency: search may lag a few seconds behind the DB
• Synced deletions/updates
• Ranking/relevance
• Filters/facets
• Permissions/security: make sure users only search what they’re allowed to see

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If you want the shortest path by stack

Tell me your:

• backend language/framework
• database
• expected data size
• whether you need typo tolerance/autocomplete/facets

…and I can recommend the best option and show a concrete setup.