analytics on Mooncake

What does Analytics in Postgres really mean?

Sun, 23 Mar 2025 00:00:00 +0000

Few areas in databases are hotter right now than extending Postgres to support analytics queries. This isn’t a particularly novel area either –– there have been plenty of attempts in the last decade:

Here is what it means to our customers –– and why the problem remains largely unsolved, despite our first attempt with pg_mooncake.

Level 0: I just run analytics queries on my Postgres tables

You’ll be surprised how far this can go with proper indexes, statistics, query tuning, and materialized views—people really overthink this.

pg_mooncake 🤝 drizzle: typed SDKs for your analytic workloads

Wed, 08 Jan 2025 00:00:00 +0000

A thing that surprised us in the pg_mooncake launch was how much developers cared about ORMs for their analytical workloads. We saw two typical requests:

“I have a bunch of Parquet files. I want full ORM support over them.”
“I use Drizzle/Prisma for my transactional database. I also want to use it for my analytics and dashboards.”

We’ve always believed that columnar storage in Postgres must look and feel like a regular heap table. It’s where we innovated with pg_mooncake columnstore tables; you can run transactions, updates, deletes, and joins with heap tables.

pg_mooncake + Neon: serverless analytics in Postgres

Tue, 07 Jan 2025 00:00:00 +0000

pg_mooncake launched in beta two months ago. It adds columnar storage and a vectorized engine to Postgres.

Over the last two months, with our customers, we’ve realized we accidentally built the best serverless database for analytics. Here’s the story:

1. pg_mooncake 🤝 Neon storage engine

Neon innovated significantly on its storage engine.

There’s a great thread by Nikita on this. This storage engine is how Neon can achieve true separation of storage & compute. This leads to an exceptional DX around branching, scale-to-zero, read replicas, instant provisioning… All of these are goodies of a true serverless Postgres.