vs

DuckDB vs pg_duckdb

DuckDB: In-process columnar SQL database: the SQLite of analytics, running inside your app or notebook. pg_duckdb: PostgreSQL extension that embeds DuckDB's vectorized engine for fast analytics and data-lake queries.

key differences

  • DuckDB is primarily a columnar / OLAP database; pg_duckdb is relational.
  • Only DuckDB has full-text search, vector search, geospatial, in-memory, encryption at rest.
  • DuckDB can run embedded in your application process.

choose duckdb for

  • +Local data analysis in notebooks
  • +ETL and data-engineering pipelines on files
  • +Embedded analytics inside applications
  • +Querying data lakes on S3 without a cluster

best at · OLAP, Embedded, Lakehouse

choose pg-duckdb for

  • +Dashboards and reports directly on Postgres data
  • +Joining Postgres tables with Parquet/Iceberg data in S3
  • +Exporting query results to Parquet for data lakes
  • +Ad-hoc analytics without standing up a warehouse

best at · OLAP, Real-time analytics, Lakehouse

overview
Primary model
Columnar / OLAP
Relational
Also supports
Relational, Document, Spatial, Vector
Columnar / OLAP
Type
Database engine
Extension
Status
Active
Active
Developer
DuckDB Foundation / DuckDB Labs
DuckDB Labs, MotherDuck and Hydra
First release
2019
2024
Latest version
2.0.0 (Oct 21, 2026)
1.1.1 (Dec 18, 2025)
License
Open source (permissive)MIT License
Open source (permissive)MIT License
Free tier
✓
✓
popularity
WhichDB Score
63.7
14.9
Rank
#13
#226
GitHub stars
42.6K
3.3K
Stack Overflow questions
501
–
scale & guarantees
Comfortable data size
Medium (≤ 10 TB)
Medium (≤ 10 TB)
Horizontal scaling
None
None
Multi-region
None
None
ACID
Full
Full
Consistency
Strong
Strong
Schema
Strict
Strict
Architecture
Embedded, Single node, In memory
Single node, Embedded
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
—
Serverless
—
—
Embedded
✓
—
Edge
—
—
Clouds
–
–
capabilities
Transactions
✓
✓
Joins
✓
✓
Secondary indexes
✓
✓
Full-text search
✓
—
Vector search
✓
—
Geospatial
✓
—
JSON
✓
✓
Change data capture
—
—
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
—
Pub/Sub
—
—
Columnar storage
✓
✓
In-memory
✓
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
✓
—
Role-based access
—
—
Time travel
—
—
Branching
—
—
Online schema changes
—
—
Streaming ingest
—
—
Offline sync
—
—
Runs in WebAssembly
✓
—
interfaces
Query languages
SQL (PostgreSQL-like dialect)
SQL
Client languages
Python, R, Java, JavaScript, Go, Rust, C, Swift
C, Java, Python, JavaScript, Go, Rust
Best for
OLAP, Embedded, Lakehouse, Warehouse
OLAP, Real-time analytics, Lakehouse, HTAP

DuckDB vs pg_duckdb: FAQ

What is the difference between DuckDB and pg_duckdb?

DuckDB: In-process columnar SQL database: the SQLite of analytics, running inside your app or notebook. pg_duckdb: PostgreSQL extension that embeds DuckDB's vectorized engine for fast analytics and data-lake queries. DuckDB is primarily a columnar / OLAP database; pg_duckdb is relational. Only DuckDB has full-text search, vector search, geospatial, in-memory, encryption at rest. DuckDB can run embedded in your application process.

When should I choose DuckDB over pg_duckdb?

Choose DuckDB for local data analysis in notebooks; ETL and data-engineering pipelines on files; and embedded analytics inside applications. Choose pg_duckdb for dashboards and reports directly on Postgres data; joining Postgres tables with Parquet/Iceberg data in S3; and exporting query results to Parquet for data lakes.

Is DuckDB or pg_duckdb more popular?

DuckDB ranks #13 on the WhichDB Score (63.7/100) and pg_duckdb ranks #226 (14.9/100). Popularity is not fit: check the requirements that matter for your project.

Are DuckDB and pg_duckdb open source?

Yes. DuckDB is open source. License: MIT License. Yes. pg_duckdb is open source. License: MIT License.