docs/welcome/introduction.mdx
You are likely a good fit for ParadeDB if you identify with the following:
tsvector and the GIN index, but have reached a scale where you're limited by performance bottlenecks or missing features like BM25 scoring or fuzzy searchFor teams that already use Postgres, ParadeDB is the simplest way to bring Elastic-quality search to your application.
Syncing Postgres with an external search engine like Elastic can be a time-consuming, error-prone process that involves babysitting ETL pipelines and debugging data inconsistency issues. ParadeDB eliminates this class of problems because you can:
In ParadeDB, writing a search query is as simple as writing SQL. ParadeDB supports JOINs, which removes the complexity of denormalizing your existing schema.
ParadeDB supports Postgres transactions and ACID guarantees. This means that data is searchable immediately after it's written to ParadeDB, and durable thanks to Postgres write-ahead logging.
People usually compare ParadeDB to two other types of systems: OLTP databases like vanilla Postgres and search engines like Elastic.
| OLTP database | Search engine | ParadeDB | |
|---|---|---|---|
| Primary role | System of record | Search and retrieval engine | System of record and search/analytics engine |
| Examples | Postgres, MySQL | Elasticsearch, OpenSearch | |
| Search features | Basic FTS (no BM25, weak ranking) | Rich search features (BM25, fuzzy matching, faceting, hybrid search) | Rich search features (BM25, fuzzy matching, faceting, hybrid search) |
| Analytics features | Not an analytical DB (no column store, batch processing, etc.) | Column store, batch processing, parallelization via sharding | Column store, batch processing, parallelization via Postgres parallel workers |
| Lag | None in a single cluster | At least network, ETL transformation, and indexing time | None in a single cluster |
| Operational complexity | Simple (single datastore) | Complex (ETL pipelines, managing multiple systems) | Simple (single datastore) |
| Scalability | Vertical scaling in a single node, horizontal scaling through Kubernetes | Horizontal scaling through sharding | Vertical scaling in a single node, horizontal scaling through Kubernetes |
| Language | SQL | Custom DSL | Standard SQL with custom search operators |
| ACID guarantees | Full ACID compliance, read-after-write guarantees | No transactions, atomic only per-document, eventual consistency, durability not guaranteed until flush | Full ACID compliance, read-after-write guarantees |
| Update & delete support | Built for fast-changing data | Struggles with updates/deletes | Built for fast-changing data |
As a company, ParadeDB is over two years old. ParadeDB launched in the Y Combinator (YC) S23 batch and has been validated in production since December 2023.
ParadeDB Community, the open-source version of ParadeDB, has been deployed over 700,000 times. ParadeDB Enterprise, the durable and production-hardened edition of ParadeDB, powers core search and analytics use cases at enterprises ranging from Fortune 500s to fast-growing startups. A few examples include:
1. Case study coming soon.
You're now ready to jump into our guides.
<CardGroup cols={2}> <Card title="Getting Started" icon="forward-fast" href="/documentation/getting-started/install" > Get started with ParadeDB in under five minutes. </Card> <Card title="Architecture" icon="diagram-project" href="/welcome/architecture" > Learn how ParadeDB is built. </Card> <Card title="Reference" icon="magnifying-glass" href="/documentation/full-text/overview" > API reference for full text search and analytics. </Card> <Card title="Deploy" icon="server" href="/deploy/overview"> Deploy ParadeDB as a Postgres extension or standalone database. </Card> </CardGroup>