Mooncake LabsOriginal upstream engineering team behind pg_mooncake, duckdb_mooncake, and moonlink (@Mooncake-Labs) Zhou SunCo-founder & CEO of Mooncake Labs · Co-creator of pg_mooncake and Moonlink (@zhousun) Cheng ChenCo-founder & CTO of Mooncake Labs · Original creator of pg_mooncake, duckdb_mooncake, and Moonlink (@dpxcc) Pranav AuroraCo-founder of Mooncake Labs · Co-creator of pg_mooncake and Moonlink (@paurora17)

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.

In v0.1, we’ve made it even easier to load data into columnstore tables:

1. From Parquet, CSV, and JSON files:

SELECT * FROM mooncake.read_parquet('<file_path>');

2. From Hugging Face datasets:

COPY reddit_comments FROM 'hf://datasets/fddemarco/pushshift-reddit-comments/data/RC_2012-01.parquet';

3. From existing Iceberg and Delta Lake tables:

SELECT * FROM mooncake.iceberg_scan('s3://bucket/table');

Better performance for time-series workloads

pg_mooncake is the only Postgres extension that adds columnar storage and vectorized execution.

In v0.1, we added file statistics and skipping. Customers run time-series and analytics workloads without traditional sharding.

Our ClickBench results land next week. (Hint: top 10 🙂). Perhaps Postgres is indeed a Great Real-Time Analytics Database.

Looking Ahead to v0.2

We will double down on two areas: DevEx and time-series workloads. Planned improvements include:

1. Enhanced support for small writes, updates, and deletes
2. Syncing rowstore tables to columnstore tables
3. Indexes (secondary indexes and sort keys)
4. Time-series functions

To our community, thank you

It takes a village. Thank you to our community, customers, contributors, and Postgres partners like Neon and Supabase. Your support has been invaluable.

Our favorite metric is the number of contributors to pg_mooncake growing.

The momentum behind pg_mooncake is clear. Analytics in Postgres is indeed a reality 🥮