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Just tried it out. Works fine. Love it!
I tried it with a wordpress site. It is showing hundreds of sql queries in one request (thats probably why that wordpress site is so slow lol)
What I would love to see here is:
- some kind of sorting: eg. by excecution time or order. So I can see the slowest queries.
- search/filter feature.
- faster scrolling with pgup/pgdown keys.
- maybe how often the same query was executed. I could check the code and maybe optimize the queries.
Could I put this into default docker-compose for developers, so when they work on project with micro services they can quickly inspect SQL queries if something weird is happening? How would this UI work with that scenario, feels like React frontend would better serve that purpose.
This is very neat! IMO inspecting the queries the agents run on the database is a better approach to understand how the code works, even more than reviewing the code.
I just tried and it works smoothly. For those who doesn't want to plug in the agents to their database directly, I built a similar tool https://dbfor.dev for the exact purpose, it just embeds PGLite and implements PG wire protocol to spin up quick PG databases with a traffic viewer included.
IMO transparent proxies for observability are not the best pattern. And I speak from experience, we developed the Postgres plugin for Envoy [1], [2] and we use it in StackGres [3], among others, for this very same reason, observability.
There's two main problems with said proxies:
* Latency. Yes, yes, yes, they add "microseconds" vs "milliseconds for queries", and that's true, but just part of the story. There's an extra hop. There's two extra sets of TCP layers being traversed. If the hop is local (say a sidecar, as we do in StackGres) it adds complexity in its deployment and management (something we solved by automation, but was an extra problem to solve) and consumes resources. If it's a network hop, then adds milliseconds, and not microseconds.
* Performance. It's not that hard to write a functioning PG wire proxy (it's not trivial either). But it is extremely hard to make it perform well under high load scenarios. Most of the proxies I have seen crack down under moderate to high performance.
What's the solution then? The Postgres extension model to capture the metrics (we also experimented with eBPF, but it causes too many kernel-user space context switches when you can do the same in an extension without them), and a small sidecar to push the metrics out via a standardized protocol like OTEL.
I get what you're saying about a proxy like this, latency & performance would suffer, however minor, and in production DB land this really matters.
I've just not sure it is much of a slight on such proxies.
You don't need to run this always inline in production to get amazingly useful results. Yes, there are lots of production insight solutions out there, but lots of modern stacks can be complex enough that just getting a quick handle on how the page you're debugging talks to your DBs can be incredibly useful, which is where I love the idea of a solution like this.
Sure, it is mytop / pgtop, but trying to offering it at a different layer & with a modern interface. Seems useful to me.
The sampling-vs-continuous-monitoring tradeoff is well-studied in distributed systems observability. Gregg's work on dynamic tracing vs. static instrumentation (cf. BPF Perf Tools) demonstrates that even sporadic production sampling yields diagnostic value exceeding its overhead cost. The Heisenberg effect matters less when you're isolating specific pathological queries rather than profiling baseline performance—the latter being where latency-sensitive workloads truly suffer.
Actually, Envoy's Postgres filter isn't quite a "transparent proxy" in the observability sense—it's an L7 protocol filter in an already-deployed proxy layer. The latency argument conflates two different architectures: purpose-built DB proxies like sql-tap versus repurposing service mesh components. Calling them both "transparent proxies" muddies the distinction.
What you can also do is add frontend and backend user to the proxy and then agents won't ever get the actual db user and password. You can make it throwaway too as well as just in time if you want.
Traditionally it was database activity monitoring which kind of fell out of fashion, but i think it is going to be back with advent of agents.
Everyone acts like TLS is sacred but in production you're terminating it at the load balancer anyway. Between services? Usually plaintext. The "always TLS" orthodoxy ignores that most databases sit behind private networks where the real threat is visibility into what's actually happening, not theoretical MITM attacks from your own infrastructure.
The proxy vs packet capture debate is a bit of a non-debate in practice — the moment TLS is on (and it should always be on), packet capture sees nothing useful. eBPF is interesting for observability but it works at the network/syscall level — doing actual SQL-level inspection or blocking through eBPF would mean reassembling TCP streams and parsing the Postgres wire protocol in kernel space, which is not really practical.
I've been building a Postgres wire protocol proxy in Go and the latency concern is the thing people always bring up first, but it's the wrong thing to worry about. A proxy adds microseconds, your queries take milliseconds. Nobody will ever notice. The actual hard part — the thing that will eat weeks of your life — is implementing the wire protocol correctly. Everyone starts with simple query messages and thinks they're 80% done. Then you hit the extended query protocol (Parse/Bind/Execute), prepared statements, COPY, notifications, and you realize the simple path was maybe 20% of what Postgres actually does. Once you get through that though, monitoring becomes almost a side effect. You're already parsing every query, so you can filter them, enforce policies, do tenant-level isolation, rotate credentials — things that are fundamentally impossible with any passive approach.
You don't need to access (or even have access to) the DB server itself (e.g. to read the query-log), you can do everything by just setting a different host to connect to.
Was AI used to build this? It looks a lot like the kind of scratch-an-itch projects I have been grinding out with AI lately, in size, timeline, code, and function. If not, you are a very very productive programmer.
If so, would you mind sharing which model(s) you used and what tooling?
This is particularly useful for diagnosing N+1 queries in development. I've caught dozens by watching the real-time feed during feature work - you immediately see when loading a collection spawns hundreds of individual SELECTs.
One tip: pipe the output through jq to filter by query pattern or execution time. Makes it easier to spot the actual problems.
What I would love to see here is:
- some kind of sorting: eg. by excecution time or order. So I can see the slowest queries.
- search/filter feature.
- faster scrolling with pgup/pgdown keys.
- maybe how often the same query was executed. I could check the code and maybe optimize the queries.