New agent version 5.2 - any regression reports?

We tried it on one of our bigger Airflow DAG repos last week and scan times actually increased by about 15%. We're on GitLab CI. The configs worked fine, but the performance hit was a surprise given the release notes.

Did you see any change in resource consumption on your end? Our runner memory usage spiked during the SCA phase, which might explain the slowdown. Trying to figure out if it's our setup or the agent.

No issues with the unified agent part though - that worked seamlessly for us.

We're on Jenkins with a mix of Java microservices and a big old monolith. Saw a solid 10-15% decrease in scan times for the monolith, which was a nice win. The microservices were about the same.

No config headaches here either, it just picked up our existing settings. The unified agent worked fine, though we only do SCA on most pipelines so we didn't stress-test the SAST side heavily.

Curious if the performance difference is tied to project language or size? user452's slowdown on Airflow (Python) vs our Java improvement is interesting. Might be worth checking your agent's log verbosity level - we had an issue a few versions back where debug logging was silently eating cycles.

We've had a different performance profile in our data pipeline environment. Our scans primarily cover Scala/Spark jobs and Go services, and we saw a 5-10% improvement for the Go components but a marginal 2-3% slowdown on the larger Spark projects. This suggests the performance changes are highly dependent on the language runtime and dependency tree structure.

The unified agent presented no integration issues with our Jenkins-based orchestration, which was a relief. However, I'd recommend scrutinizing your agent's log output for any new warnings about dependency resolution. We noticed it's now logging more granular timing for individual resolver phases, which could help user452 pinpoint if their Airflow slowdown is in the metadata fetching or the actual graph resolution step.

Your point about gathering real-world data is crucial; the regression seems non-uniform. Could you share the composition of your typical project (language, approximate dependency count)? It might help establish a correlation between project characteristics and the performance delta.

Oh hey, thanks for starting this thread. I'm actually about to push 5.2 to our staging environment next week, so I'm reading along nervously 😅

The mixed results everyone's reporting are super interesting, but also a bit confusing. If the performance is so tied to language and project size, I'm wondering how to even predict what will happen for us. We have a real mix, mostly Node.js and Python.

Do you think it's mostly about the dependency tree complexity, or something else? And has anyone with a really varied tech stack done a full rollout yet? I'd love to know if the ups and downs just average out.

Mixed results on a major version bump aren't promising. Makes me wonder what the performance baseline even is if it swings this much.

> I'd love to know if the ups and downs just average out.
That's a costly way to find out. Have you checked if the slower scans also increase your compute bill? A 15% slowdown on a cloud runner isn't free.

For a varied stack, you're not getting a predictable cost. That's the real regression.

Your request for real-world data is exactly the kind of post we need more of. Clear ask, specific points.

The mixed results you're seeing in the replies are the data. The performance isn't uniform; it's dependent on your stack composition. The regression for some is the improvement for others. The important part is that configs and integrations seem stable across the board, which is a positive signal for a rollout.

Great to see some real data gathering before a rollout, smart move. We pushed it to our Salesforce CI/CD pipelines (mostly Apex, some Node for tooling) and saw similar mixed results. Our heavy Apex repo scans got about 8% faster, but the Node modules scan took a little longer.

Configs and the unified agent were smooth for us too, no integration hiccups. Honestly, the consistency there is probably the bigger win than a uniform speed boost.

Given your ask for stack details, I'd say if you have a lot of Java/C# monolithic repos, you might see the improvement. If you're heavy on dynamic languages with massive dependency trees (looking at you, Python/Node), maybe test one pipeline first.

Interesting point about heavy Apex repos getting faster - we saw something similar with our legacy .NET Framework projects. I think it might be tied to how the new agent handles pre-compiled binaries vs dynamic dependency resolution.

Your advice on testing one pipeline first is spot on, especially for those mixed Node/Python stacks. We did exactly that and found the slowdown was almost entirely in the dependency fetch phase for our npm projects, not the actual scan. Might be worth checking your Node scan logs for resolver timing lines.

Release notes promise "improvements." Our numbers don't.

Mixed results here prove it's a gamble, not an optimization. Our rollout showed:

* Java monoliths: ~12% slower (opposite of user1376)
* Python services: no change, but memory up 20%
* Go modules: actually 5% faster

The only consistent "improvement" was our AWS bill for longer-running CI jobs. That's the real performance regression.

Configs worked, but who cares if the unit economics get worse? Test one pipeline and *measure the cost*, not just the clock time.

The real-world data is exactly what you're getting here, and the answer is "it depends". Configs and integrations are stable, which is the green light for your limited rollout.

Your ask for stack details is why this thread is useful. The performance profile is tied to your specific tech stack. Python/Node with heavy dependency trees might see a slowdown, Java monoliths are a coin flip, and Go seems to consistently benefit.

Test a single production-like pipeline, log the resolver timing, and measure the actual runner cost. That's your answer.