ParadeDB: A Transactional Elasticsearch Alternative on Postgres

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Introduction

ParadeDB is a modern Elasticsearch alternative built on Postgres, designed for real-time, update-heavy workloads. It provides powerful capabilities for full-text search, analytics, and hybrid search, leveraging the reliability and familiarity of PostgreSQL. Developed in Rust, ParadeDB extends PostgreSQL to deliver high-performance search and analytical functionalities, making it a compelling choice for applications requiring both transactional integrity and advanced search features. It is licensed under the AGPL-3.0.

Installation

ParadeDB can be easily deployed using Docker or Kubernetes Helm charts. For a quick start, you can pull and run the official Docker image. For Kubernetes environments, the Helm chart is available via Artifact Hub or the ParadeDB Helm Chart GitHub repository. Prebuilt binaries for ParadeDB Postgres extensions are also available for various operating systems and PostgreSQL versions (14+).

Examples

To quickly get a ParadeDB instance up and running with Docker, use the following command:

docker run --name paradedb -e POSTGRES_PASSWORD=password paradedb/paradedb

You can then connect to the database using psql:

docker exec -it paradedb psql -U postgres

For a more persistent setup with custom credentials and data volumes, consider:

docker run \
  --name paradedb \
  -e POSTGRES_USER=<user> \
  -e POSTGRES_PASSWORD=<password> \
  -e POSTGRES_DB=<dbname> \
  -v paradedb_data:/var/lib/postgresql/ \
  -p 5432:5432 \
  -d \
  paradedb/paradedb:latest

Why Use ParadeDB?

Leverages PostgreSQL: Combines the robust transactional capabilities of a relational database with advanced search and analytics features.
Real-time and Update-Heavy Workloads: Optimized for scenarios requiring frequent data updates and immediate search results.
Elasticsearch Alternative: Offers a transactional approach to search and analytics, providing a strong alternative to Elasticsearch without the operational complexity of a separate search stack.
Hybrid Search: Supports various search paradigms including full-text (BM25), similarity, and sparse vector search.
Open Source: Licensed under AGPL-3.0, fostering community contributions and transparency.