Hi everyone. I'm hoping to get some collective wisdom on a persistent issue we're hitting in our CI pipeline.
We're running SonarScanner for a large, monolithic Go codebase (several million lines). Our analysis consistently fails during the scanning phase with out-of-memory errors, even after we've allocated what we thought were generous resources. The scanner process is getting killed when it spikes past 8GB RAM, which is causing failed builds and a lot of frustration for the team.
We're using the official SonarScanner CLI (not the Maven/Gradle plugins) in a Kubernetes pod. We've tried tuning the standard `-Xmx` settings for the JVM, and we've set `SONAR_SCANNER_OPTS` with increased heap space. While that helps a bit, the memory consumption during the Go analysis itself seems to balloon unpredictably. We're not seeing this with our smaller services, only this large project.
Has anyone successfully scaled SonarQube analysis for a massive Go project? I'm particularly interested in:
* Any scanner configuration flags specific to Go that reduce memory footprint.
* Whether splitting the analysis or using a different scanner approach helped.
* If there are known issues with certain versions of the Go plugin or scanner.
I'd also welcome any general strategies for managing scanner resources in memory-constrained environments. We're committed to the quality gates, but the infrastructure overhead is becoming a real blocker.
Thanks in advance for any insights you can share.
~ Amy
We've run into a similar scaling problem with a large Go monorepo. The memory spike often correlates with the number of import statements and the size of the dependency graph being analyzed in a single scanner run.
Two things worked for us. First, we configured the scanner to exclude vendor directories and test files explicitly, which reduced the initial load. Second, we split the analysis by using separate sonar-project.properties files for distinct, loosely-coupled service layers within the monoropo, invoking the scanner sequentially. This is a manual process, but it kept each run's memory footprint under 4GB.
Also, check if you're using `sonar.exclusions` and `sonar.go.coverage.reportPaths` properly. A misconfigured coverage report path pointing to a huge, aggregated file can cause the scanner to try and process it all at once.
Data is the only truth.
That ballooning memory with a huge Go codebase is such a pain, I feel you. The first thing I'd double-check is the `sonar.go.tests.reportPaths` setting, not just exclusions. If you're feeding it a combined test report for the whole monolith, that single XML file can be enormous and the scanner tries to hold it all in memory. I've seen splitting that report generation per service module help more than just splitting the properties files.
Also, have you tried the `-Dsonar.go.file.suffixes=.go` trick? I know it sounds redundant, but explicitly limiting the file suffixes can sometimes prevent the scanner from getting distracted by other non-Go files in your massive tree. It's a long shot, but cheap to try.
What version of the SonarGo plugin are you on? There were some memory improvements around import analysis in the later 1.x releases.
ship it
Are you tracking memory costs for this in your CI budget? I'm curious if scaling up the pod specs is even viable long-term or if the price jumps make it a nonstarter.
What version of the SonarGo plugin are you using? Someone mentioned improvements around version 2.x, but I haven't seen hard numbers on the memory impact.
I've seen this exact pattern with other SAST tools applied to large Go monoliths. Throwing more heap at the JVM often just delays the inevitable crash, because the memory spike usually happens in the native scanner process during AST parsing, not in the JVM.
Before you start carving up the codebase or reports, check the vendor's own documentation for known scaling limits. You'd be surprised how often the "enterprise-ready" marketing copy doesn't match the reality of parsing several million lines of a single language. There's a point where the architectural approach just doesn't scale linearly.
What's the actual failure mode? Does it die during the "sensor" phase or during report processing? That tells you if the problem is the Go plugin itself or the scanner's reporting engine. I've found the latter is more common with huge projects, and splitting the property files does nothing to fix it.
Question everything