Hi Joe:
Ok this just makes me more confused. If I understand what you have said, you have moved the datasets to be local, and are now testing locally. However, the threads you show say you are running or accessing something on AWS (our main ERDDAP which runs all sort software stuff does not show those threads, but we do not use AWS).
So, stepping back, can you give a more complete description of your setup.
Second, as I have stated before, Java now uses a lot more memory than the heap. Just looking at the heap usage will not necessarily tell you a lot. For example, on our main ERDDAP, heap is at 20GB, but Java memory usage is at 45GB. So you have to track memory usage., not just heap usage, and memory usage can be much larger than heap size.
Also, you need to be checking if swap space is being used, If ERDDAP starts swapping, it will eventually grind to a halt, And you need to check if you have the time to do a major reload at a large enough value to be certain it is finishing before it starts again. This is set in setup.xml.
Finally our experience is that memory usage increases for awhile as usage increases until it plateaus, so for example the ERDDAP mentioned above pretty much now stays around 45GB, but right after startup it is at 26GB, and takes a while to grow.
Also what I use to track things are the commands ’sar’ and ‘pidstat’, you can install these by installing ’systat’, though top, htop or top should do the trick.
What I would suggest is to set up a purely local ERDDAP with no AWS involved, and just a couple of datasets to start so that you can feel pretty certain that you can both stay within your memory limits and that the reloads finish, and track memory usage. If that seems to e working, add some more datasets. Also I believe EDDTableFromMultidimNcFiles require a good bit more work than other datatypes. So as you test this local only version, look at the ERDDAP logs to see if there are any issues in loading the datasets.
So the short version is first remove anything to do with AWS, in case that is where the problem is arising. (It may well be), and track you java memory usage, not just heap usage, track swap usage, and start with a much smaller number of datasets and see if you see the same behavior.
HTH,
-Roy
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