Postgres for everything hits a wall the moment dashboards get concurrent
I used to say, "Just use Postgres." After that, our dashboards became concurrent, and the read replica we quickly added to offload some of the pressure from the main database began overheating instead. The meme that traps you There is a reassuring notion that Postgres can handle all tasks. OLTP, queues, search, analytics, everything in a single package. But it shouldn't be the ultimate goal.…
Postgres, the versatile database solution, encountered limitations when faced with concurrent dashboard queries. Despite being praised for its ability to handle OLTP, queues, search, and analytics in a single package, the reality proved to be different. MotherDuck, in July 2026, highlighted that once real analytics are introduced, the "Postgres for Everything" approach becomes a structural inconsistency.
The issue lies in Postgres' row-oriented storage, which stores data in 8 KB pages with a fixed tuple header of 23 bytes. This design forces full-row scans when only a fraction of the data is needed, leading to inefficiencies in analytics queries. As the number of concurrent queries increases, CPU and memory pressure rise, resulting in slow performance. A September 2026 incident demonstrated this, where hundreds of aggregations simultaneously caused CPU and memory pressure spikes, making fast queries painfully slow.
Moreover, mixing queue-like workloads with normal OLTP workloads in Postgres leads to MVCC bloat, index fragmentation, and WAL pile-up. This not only slows down the system but also accumulates "rot," causing costly hardware over-provisioning. The expensive solutions such as increasing the size of AWS r8gd.4xlarge instances provide only temporary relief, as the underlying storage format mismatch remains unresolved.
Postgres remains a reliable choice for transactional data storage, but attempting to use it for analytics workloads is a mismatch. Column-oriented serving layers like ClickHouse, designed for aggregations and optimized for compressing data and accessing only required columns, provide a more suitable solution. The row store's limitations should not be forced to behave like a column store.
The choice to rely solely on Postgres for all data storage needs may seem romantic, but it comes with significant costs. So, the question remains: at what point did you realize that Postgres alone couldn't handle your analytics workloads?
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
