Hydra’s pg_duckdb: Because waiting for queries is boring
A better way to handle analytics in Postgres.
The best tools solve specific, frustrating problems. Hydra’s pg_duckdb tackles one many of us face: moving data between systems just to analyze it. With pg_duckdb, you can run powerful analytics directly in Postgres. If you’ve ever struggled with moving data between systems just to get meaningful analysis, you’ll understand why this is a big deal.
The Problem: Postgres and the Scaling Wall
Postgres is a remarkable database. It’s like the Swiss Army knife of data storage: organized, reliable, and versatile. But like any tool, it has its limits. Postgres is great for storing and retrieving data. But when you start asking hard questions, like analyzing millions of rows or identifying trends in large datasets, it begins to strain. It wasn’t built for analytics at scale.
Here’s where the frustration starts: traditionally, if you wanted to analyze that kind of data, you’d export it to another tool. Maybe you’d load it into a data warehouse, then run your queries. This kind of process is slow, clunky, and worst of all, expensive.
The Solution: DuckDB and pg_duckdb
What Hydra has done with pg_duckdb is to combine the reliability of Postgres with the speed and efficiency of DuckDB, a specialized analytics engine. DuckDB does one thing exceptionally well: it crunches through large datasets faster than almost anything else. Instead of processing data row by row (like Postgres), DuckDB looks at columns, allowing it to handle huge computations with ease.
By embedding DuckDB into Postgres, pg_duckdb eliminates the need to move data at all. You can perform advanced analytics right where your data lives.
Why pg_duckdb Matters
The beauty of pg_duckdb is its simplicity. It doesn’t ask you to learn a new system or re-architect your workflow. Instead, it takes the tools you already know: Postgres and SQL, and quietly makes them better.
Here’s what makes it special:
Speed: DuckDB processes data at a scale that would overwhelm Postgres, making queries faster and more efficient.
Flexibility: With pg_duckdb, you can query data not just from Postgres but directly from external file formats like Parquet, CSV, or JSON—even if they’re stored on AWS S3 or Google Cloud.
Cost Savings: By removing the need for expensive data pipelines or separate analytics platforms, pg_duckdb lets you get more out of the tools you already use.
How It Works
pg_duckdb functions like a bridge:
When you run a query, it decides whether Postgres or DuckDB is better suited to handle it.
It supports the same data types (numbers, text, dates, JSON, etc.), so there’s no need for painful conversions.
If DuckDB can’t handle a specific query, it hands the job back to Postgres seamlessly.
This cooperative model lets you perform complex analytics with minimal friction. Need to analyze millions of rows stored in a Parquet file on S3? Use read_parquet() to query it directly, no downloads required. Want to combine that with customer data already in Postgres? That’s just a JOIN away.
The Bigger Picture
In the past, we built massive data warehouses and complex ETL pipelines because we had to. But what if your database was smart enough to handle analytics on its own? What if it could reach into object storage, process data in place, and give you answers without making you move files around?
This is the promise of pg_duckdb. It simplifies analytics, making it accessible to more people, at lower cost. For developers, it’s like suddenly having a turbocharged engine under the hood of your trusty old database.