# ParadeDB — Elasticsearch-Quality Search Inside Postgres

> ParadeDB is an open-source PostgreSQL extension that brings full-text search, hybrid search, and analytics capabilities directly into Postgres, replacing the need for a separate Elasticsearch cluster.

## Install

Save as a script file and run:

# ParadeDB — Elasticsearch-Quality Search Inside Postgres

## Quick Use
```bash
# Run ParadeDB (Postgres with extensions pre-installed)
docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=secret paradedb/paradedb:latest
# Create a BM25 search index
psql -h localhost -U postgres -c "
  CREATE INDEX ON docs USING bm25 (title, body);
  SELECT * FROM docs WHERE docs @@@ 'database search';
"
```

## Introduction
ParadeDB extends PostgreSQL with Elasticsearch-grade full-text search and analytics capabilities. Instead of syncing data to a separate search engine, you run BM25 search, sparse vector queries, and columnar analytics directly inside your Postgres database.

## What ParadeDB Does
- Adds BM25 full-text search to Postgres via the pg_search extension
- Supports hybrid search combining BM25 text scoring with vector similarity
- Provides columnar storage and vectorized analytics via the pg_analytics extension
- Integrates with standard Postgres tooling (psql, ORMs, connection poolers)
- Eliminates the need for a separate Elasticsearch or OpenSearch deployment

## Architecture Overview
ParadeDB ships two core Postgres extensions. pg_search implements an inverted index using the Tantivy search library (Rust-based Lucene equivalent) and exposes BM25 scoring through a custom Postgres operator. pg_analytics adds a columnar table access method built on Apache Arrow and DataFusion for analytical queries. Both extensions run inside the Postgres process and use standard Postgres transactions.

## Self-Hosting & Configuration
- Run the ParadeDB Docker image, which is standard Postgres with extensions pre-installed
- Alternatively, install pg_search and pg_analytics as extensions on an existing Postgres instance
- Create BM25 indexes with standard CREATE INDEX syntax using the bm25 access method
- Configure tokenizers, analyzers, and search options per index
- Compatible with managed Postgres providers that support custom extensions

## Key Features
- BM25 full-text search with relevance scoring, phrase matching, and faceted search
- Hybrid search combining keyword (BM25) and semantic (vector) results in one query
- Sparse vector search for learned sparse retrieval models like SPLADE
- Columnar analytics with vectorized execution for OLAP-style queries
- Standard SQL interface with no proprietary query language to learn

## Comparison with Similar Tools
- **Elasticsearch** — Purpose-built search engine with its own query DSL and operational complexity; ParadeDB runs inside Postgres
- **OpenSearch** — AWS fork of Elasticsearch with similar architecture; still requires a separate cluster
- **Meilisearch** — Fast typo-tolerant search API, but standalone service and not SQL-based
- **Typesense** — Developer-friendly search engine, but requires data sync from your primary database
- **pgvector** — Postgres extension for vector similarity only; ParadeDB adds BM25 text search and hybrid scoring

## FAQ
**Q: Can I use ParadeDB with my existing PostgreSQL database?**
A: Yes. pg_search and pg_analytics are standard Postgres extensions that can be installed on any Postgres 14+ instance.

**Q: Does ParadeDB replace Elasticsearch for all use cases?**
A: ParadeDB covers full-text search, hybrid search, and basic analytics. For specialized use cases like log aggregation at massive scale, a dedicated search engine may still be appropriate.

**Q: How does search performance compare to Elasticsearch?**
A: ParadeDB uses Tantivy, a Rust-based search library that benchmarks competitively with Lucene. For most application search workloads, performance is comparable.

**Q: What is the license?**
A: ParadeDB is licensed under AGPL-3.0.

## Sources
- https://github.com/paradedb/paradedb
- https://docs.paradedb.com

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Source: https://tokrepo.com/en/workflows/074a845b-3c72-11f1-9bc6-00163e2b0d79
Author: Script Depot