Cheng Chen on Mooncake

Mooncake is joining Databricks!

Wed, 01 Oct 2025 00:00:00 +0000

Thirteen months ago, we started Mooncake to bring the data intelligence stack that powers giants like OpenAI to every vibe-coded app and developer. From first prompt to IPO.

Today, we’re joining forces with Databricks. The mission continues: make Lakebase and the Lakehouse feel like one.

For a decade, we thought the final form of a database was one that could do it all. Fast updates, transactions, analytics. For database people, this was HTAP. It never quite landed. Zhou and Cheng built it. Pranav tried to sell it. It was hard.

Mooncake: a real-time storage engine for Apache Iceberg

Mon, 11 Aug 2025 00:00:00 +0000

This technical overview assumes familiarity with Apache Iceberg, an open table format for analytical datasets. Iceberg is part of the general “Lakehouse” movement, where users:

Store large datasets using open file formats (Parquet + Metadata) in Object Storage
Query them directly by engines like Spark, Trino, Flink, and DuckDB using SQL
Get the benefits of ‘database features’ like schema evolution & time-travel
Scale practically infinitely, and aren’t locked into a vendor

Iceberg was built for slow-changing data + batch analytics & processing. That world is pretty clear and only improving with engines getting better at reading open formats.

The easiest way to Analytics in Postgres: pg_mooncake v0.1

Sat, 11 Jan 2025 00:00:00 +0000

We announced pg_mooncake to the world on Day 60.

Today, Day 131, pg_mooncake comes out of beta.

pg_mooncake wins on ease and performance. So, we doubled down for v0.1. That’s it.

The Easiest Way to Fast Analytics in Postgres

With a native columnstore table in Postgres, pg_mooncake brings familiar table semantics to the way you run analytics. Columnstore tables support transactional operations like INSERT, SELECT, UPDATE, DELETE, and COPY, alongside joins with regular Postgres heap tables.

pg_mooncake: Fast Analytics in Postgres with Columnstore Tables and DuckDB

Wed, 06 Nov 2024 00:00:00 +0000

pg_mooncake brings columnstore tables with DuckDB execution to Postgres for fast analytics.

It supports transactional inserts, updates, and deletes, just like regular Postgres tables. Data is written with Delta Lake (and Apache Iceberg in v0.2+) table metadata, allowing it to be queried outside of Postgres.

Why another Postgres analytics extension?

Postgres is the most popular database these days, but it’s bad at analytics. Numerous attempts have been made to address this, but with limited success so far. Fast analytics require a distinct set of techniques, such as columnar storage, vectorized execution, and late materialization, etc. Early projects like Citus & Timescale added columnar storage, but there was no good vectorized execution to leverage the format.

Mooncake: next-gen search and analytic systems

Wed, 30 Oct 2024 00:00:00 +0000

We are Zhou, Cheng, and Pranav. And we’ve spent the last decade building databases at MemSQL (now SingleStore).

We love complex systems, deep-tech engineering, and naturally – physics 🙂 Data systems, much like physics, are built on certain principles. The principles need to be unanimous and durable.

Building systems that are simply ’technically’ better is futile. The success of a data system depends on the principles it embodies, and changing them along the way is extremely difficult. Closed-source companies can’t go to open source and expect adoption. Databases can’t just abandon their storage format and be effective stateless engines.