Hi, I was trying to run a feature model based application with slightly less memory that usual and noticed that it failed outright when I tried to launch it with 256MiB of RAM. The heap dump was very very small, so when trying to trace back the problem I noticed that the feature launcher uses a 256MiB byte array at [1] for reading feature archive files. It's a local variable so it does not leak, but it does impose a hard limit on the memory size.
Is this something that is done intentionally? I think that for more light-weight applications that don't consume a lot of heap at runtime this allocation is too aggresive. If we want to keep this size ( although maybe we wanted 256 KiB? ) we can at least allocate it on- demand the first time when reading a feature archive. As a data point, I can run the Starter just fine on my laptop with a 280MiB heap. Thoughts? Thanks, Robert [1]: https://github.com/apache/sling-org-apache-sling-feature-launcher/blob/36b7fe229780b06f81db0a97f2e8e86726a3158c/src/main/java/org/apache/sling/feature/launcher/impl/FeatureProcessor.java#L111
