Elephantshark is like Wireshark for Postgres. It surfaces traffic and workloads, letting operators see where time is spent. [1]
Traffic patterns meet caching debates Using Elephantshark to observe real traffic helps you spot hot paths and tail latencies that shape caching decisions. [1] In the Redis vs Postgres caching conversation, a post argues that Redis is fast enough and you can cache in Postgres. [2]
Pipelining and performance choices • pgx supports query pipelining in Postgres [2] • rueidis brings auto-pipelining to Redis workflows [2] • Redka offers a Redis-like API backed by SQLite or Postgres [2]
Takeaway The takeaway isn’t “one cache fits all.” As one writer puts it, there’s “a good enough” level of performance, and once you need that speed, there are ways to push it further. [2] A key point: raw latency isn’t everything—traffic insight from Elephantshark helps decide what to cache and where the gains actually live. [1]
Closing thought Pairing traffic visibility with caching strategy lets teams tune whether Redis, PostgreSQL-as-cache, or a hybrid approach makes sense for their workload. [1]
References
Elephantshark: like Wireshark, but specifically for Postgres
Wireshark-like PostgreSQL traffic tool; Elephantshark analyzes PostgreSQL traffic and queries; author announces AMA with README emphasis for PostgreSQL developers everywhere.
View sourceRedis is fast – I'll cache in Postgres
Debate comparing Redis and PostgreSQL as cache backends; discusses pipelining, TTLs, unlogged tables, and performance tradeoffs across real-world workloads.
View source