Postgres Just Cracked the Top Fastest Databases for Analytics

I read the title a couple of times and I'm still not sure it isn't misleading. The benchmarks are not just for Postgres but for Postgres with the Mooncake extension. There are also other results for Postgres with different extensions. While it does rank among the top fastest databases, it is not the fastest and not even within the top 10.

How does mooncake work with std oltp workloads? Can I use Postgres with OLTP , add mooncake and expect duckdb-level of performance for OLAP queries? I know that SAP HANA has some sort of several layers of storage and automatic movement of data between them to allow for such performant OLTP/OLAP hybrid, and I think this is the holy grail for cheap/open source db. Users need OLTP first but don’t want to add Kafka + clickhouse or similar pipelines when OLAP is needed.

Just to be clear, standard SQL databases are not great for large-scale analytics. I know from first hand experience and a lot of pain.

We tried using Postgres with large analytics at my previous company https://threekit.com but it is an absolute pain. Basically we started to collected detailed analytics and thus had a rapidly growing table of around 2B records of user events during their sessions. As it grew past a 500 million records it turned out to be impossible to query this table in any thing close to real-time - it was basically untouchable because it was so slow.

I know I could have used some type of daily aggregation combined with a weekly aggregation, etc to roll up the data incrementally. A dev tried this and yeah, it hide the slow queries but then it became inflexible in terms of reporting. And writing and maintaining these cronjobs is a lot of work.

But then I tried using BigQuery on my https://web3dsurvey.com side project and I just recorded raw data and then wrote queries to do real-time aggregation (with a caching layer) in a large variety of ways. And it was near instant and easy and also very cheap.

So then I deployed this strategy over at Threekit.com and it also was easy and fast and cheap. Even more flexible queries than Web3DSurvey at basically no developer cost or infrastructure costs. No more need for aggregating cron-jobs or trying to decide ahead of time how users want to slice and dice the data. Real time, flexible queries on the fly via BigQuery is the way.

Also BigQuery bill for https://web3dsurvey.com is like $0.25 month and it is dealing with millions of records in its 3 month window of stored data. Where as just running the cheapest Postgres SQL server on Google Cloud is like >$25/month and it is a slow one.

I would never go back to traditional SQL for analytics - it was hard, slow, expensive and inflexible. Worst of all worlds.

What's the business model? It's a extension that's MIT licensed, yet it has a company and a VC behind it. Sounds like a rug pull waiting to happen

Reading the linked blog it seems the speed up comes from offloading some lookups to Postgres tables.

That doesn’t seem like something that makes sense to in DuckDB.

> pg_mooncake leverages Postgres to store table metadata, eliminating the need to query an external catalog (as DuckDB would require when scanning external Iceberg or Delta Lake tables). Instead, it directly queries the Postgres table to identify which Parquet files to scan, reducing overhead.

> Additionally, pg_mooncake stores detailed Parquet metadata for each file in Postgres, including column statistics for every row group. These statistics, such as MIN/MAX values for each column, are aggregated to optimize scans. When querying a columnstore table, pg_mooncake uses these statistics to filter out data files and row groups that cannot produce matches.