Interesting. In my original post, the memory growth (during restart) occurs after the tlog is done replaying, but during the merge.
-Greg On Mon, Dec 23, 2013 at 2:06 PM, Joel Bernstein <joels...@gmail.com> wrote: > Greg, > > There is a memory component to the tlog, which supports realtime gets. This > memory component grows until there is a commit, so it will appear like a > leak. I suspect that replaying a tlog that was big enough to possibly cause > OOM is also problematic. > > One thing you might want to try is going to 15 second commits, and then > kill the Solr instance between the commits. Then watch the memory as the > replaying occurs with the smaller tlog. > > Joel > > > > > Joel Bernstein > Search Engineer at Heliosearch > > > On Mon, Dec 23, 2013 at 4:17 PM, Greg Preston > <gpres...@marinsoftware.com>wrote: > >> Hi Joel, >> >> Thanks for the suggestion. I could see how decreasing autoCommit time >> would reduce tlog size, and how that could possibly be related to the >> original OOM error. I'm not seeing how that would make any difference >> once a tlog exists, though? >> >> I have a saved off copy of my data dir that has the 13G index and 2.5G >> tlog. So I can reproduce the replay -> merge -> memory usage issue >> very quickly. Changing the autoCommit to possibly avoid the initial >> OOM will take a good bit longer to try to reproduce. I may try that >> later in the week. >> >> -Greg >> >> >> On Mon, Dec 23, 2013 at 12:20 PM, Joel Bernstein <joels...@gmail.com> >> wrote: >> > Hi Greg, >> > >> > I have a suspicion that the problem might be related or exacerbated be >> > overly large tlogs. Can you adjust your autoCommits to 15 seconds. Leave >> > openSearcher = false. I would remove the maxDoc as well. If you try >> > rerunning under those commit setting it's possible the OOM errors will >> stop >> > occurring. >> > >> > Joel >> > >> > Joel Bernstein >> > Search Engineer at Heliosearch >> > >> > >> > On Mon, Dec 23, 2013 at 3:07 PM, Greg Preston < >> gpres...@marinsoftware.com>wrote: >> > >> >> Hello, >> >> >> >> I'm loading up our solr cloud with data (from a solrj client) and >> >> running into a weird memory issue. I can reliably reproduce the >> >> problem. >> >> >> >> - Using Solr Cloud 4.4.0 (also replicated with 4.6.0) >> >> - 24 solr nodes (one shard each), spread across 3 physical hosts, each >> >> host has 256G of memory >> >> - index and tlogs on ssd >> >> - Xmx=7G, G1GC >> >> - Java 1.7.0_25 >> >> - schema and solrconfig.xml attached >> >> >> >> I'm using composite routing to route documents with the same clientId >> >> to the same shard. After several hours of indexing, I occasionally >> >> see an IndexWriter go OOM. I think that's a symptom. When that >> >> happens, indexing continues, and that node's tlog starts to grow. >> >> When I notice this, I stop indexing, and bounce the problem node. >> >> That's where it gets interesting. >> >> >> >> Upon bouncing, the tlog replays, and then segments merge. Once the >> >> merging is complete, the heap is fairly full, and forced full GC only >> >> helps a little. But if I then bounce the node again, the heap usage >> >> goes way down, and stays low until the next segment merge. I believe >> >> segment merges are also what causes the original OOM. >> >> >> >> More details: >> >> >> >> Index on disk for this node is ~13G, tlog is ~2.5G. >> >> See attached mem1.png. This is a jconsole view of the heap during the >> >> following: >> >> >> >> (Solr cloud node started at the left edge of this graph) >> >> >> >> A) One CPU core pegged at 100%. Thread dump shows: >> >> "Lucene Merge Thread #0" daemon prio=10 tid=0x00007f5a3c064800 >> >> nid=0x7a74 runnable [0x00007f5a41c5f000] >> >> java.lang.Thread.State: RUNNABLE >> >> at org.apache.lucene.util.fst.Builder.add(Builder.java:397) >> >> at >> >> >> org.apache.lucene.codecs.BlockTreeTermsWriter$TermsWriter.finishTerm(BlockTreeTermsWriter.java:1000) >> >> at >> >> org.apache.lucene.codecs.TermsConsumer.merge(TermsConsumer.java:112) >> >> at >> >> org.apache.lucene.codecs.FieldsConsumer.merge(FieldsConsumer.java:72) >> >> at >> >> org.apache.lucene.index.SegmentMerger.mergeTerms(SegmentMerger.java:365) >> >> at >> >> org.apache.lucene.index.SegmentMerger.merge(SegmentMerger.java:98) >> >> at >> >> org.apache.lucene.index.IndexWriter.mergeMiddle(IndexWriter.java:3772) >> >> at >> org.apache.lucene.index.IndexWriter.merge(IndexWriter.java:3376) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler.doMerge(ConcurrentMergeScheduler.java:405) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler$MergeThread.run(ConcurrentMergeScheduler.java:482) >> >> >> >> B) One CPU core pegged at 100%. Manually triggered GC. Lots of >> >> memory freed. Thread dump shows: >> >> "Lucene Merge Thread #0" daemon prio=10 tid=0x00007f5a3c064800 >> >> nid=0x7a74 runnable [0x00007f5a41c5f000] >> >> java.lang.Thread.State: RUNNABLE >> >> at >> >> >> org.apache.lucene.codecs.DocValuesConsumer$1$1.hasNext(DocValuesConsumer.java:127) >> >> at >> >> >> org.apache.lucene.codecs.lucene42.Lucene42DocValuesConsumer.addNumericField(Lucene42DocValuesConsumer.java:144) >> >> at >> >> >> org.apache.lucene.codecs.lucene42.Lucene42DocValuesConsumer.addNumericField(Lucene42DocValuesConsumer.java:92) >> >> at >> >> >> org.apache.lucene.codecs.DocValuesConsumer.mergeNumericField(DocValuesConsumer.java:112) >> >> at >> >> org.apache.lucene.index.SegmentMerger.mergeNorms(SegmentMerger.java:221) >> >> at >> >> org.apache.lucene.index.SegmentMerger.merge(SegmentMerger.java:119) >> >> at >> >> org.apache.lucene.index.IndexWriter.mergeMiddle(IndexWriter.java:3772) >> >> at >> org.apache.lucene.index.IndexWriter.merge(IndexWriter.java:3376) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler.doMerge(ConcurrentMergeScheduler.java:405) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler$MergeThread.run(ConcurrentMergeScheduler.java:482) >> >> >> >> C) One CPU core pegged at 100%. Manually triggered GC. No memory >> >> freed. Thread dump shows: >> >> "Lucene Merge Thread #0" daemon prio=10 tid=0x00007f5a3c064800 >> >> nid=0x7a74 runnable [0x00007f5a41c5f000] >> >> java.lang.Thread.State: RUNNABLE >> >> at >> >> >> org.apache.lucene.codecs.DocValuesConsumer$1$1.hasNext(DocValuesConsumer.java:127) >> >> at >> >> >> org.apache.lucene.codecs.lucene42.Lucene42DocValuesConsumer.addNumericField(Lucene42DocValuesConsumer.java:108) >> >> at >> >> >> org.apache.lucene.codecs.lucene42.Lucene42DocValuesConsumer.addNumericField(Lucene42DocValuesConsumer.java:92) >> >> at >> >> >> org.apache.lucene.codecs.DocValuesConsumer.mergeNumericField(DocValuesConsumer.java:112) >> >> at >> >> org.apache.lucene.index.SegmentMerger.mergeNorms(SegmentMerger.java:221) >> >> at >> >> org.apache.lucene.index.SegmentMerger.merge(SegmentMerger.java:119) >> >> at >> >> org.apache.lucene.index.IndexWriter.mergeMiddle(IndexWriter.java:3772) >> >> at >> org.apache.lucene.index.IndexWriter.merge(IndexWriter.java:3376) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler.doMerge(ConcurrentMergeScheduler.java:405) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler$MergeThread.run(ConcurrentMergeScheduler.java:482) >> >> >> >> D) One CPU core pegged at 100%. Thread dump shows: >> >> "Lucene Merge Thread #0" daemon prio=10 tid=0x00007f5a3c064800 >> >> nid=0x7a74 runnable [0x00007f5a41c5f000] >> >> java.lang.Thread.State: RUNNABLE >> >> at >> >> >> org.apache.lucene.codecs.compressing.CompressingTermVectorsReader.get(CompressingTermVectorsReader.java:322) >> >> at >> >> >> org.apache.lucene.index.SegmentReader.getTermVectors(SegmentReader.java:169) >> >> at >> >> >> org.apache.lucene.codecs.compressing.CompressingTermVectorsWriter.merge(CompressingTermVectorsWriter.java:789) >> >> at >> >> >> org.apache.lucene.index.SegmentMerger.mergeVectors(SegmentMerger.java:312) >> >> at >> >> org.apache.lucene.index.SegmentMerger.merge(SegmentMerger.java:130) >> >> at >> >> org.apache.lucene.index.IndexWriter.mergeMiddle(IndexWriter.java:3772) >> >> at >> org.apache.lucene.index.IndexWriter.merge(IndexWriter.java:3376) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler.doMerge(ConcurrentMergeScheduler.java:405) >> >> at >> >> >> org.apache.lucene.index.ConcurrentMergeScheduler$MergeThread.run(ConcurrentMergeScheduler.java:482) >> >> >> >> E) CPU usage drops to nominal levels. Thread dump shows no Lucene Merge >> >> Thread. >> >> >> >> F) Manually triggered full GC. Some memory freed, but much remains. >> >> >> >> G) Restarted solr. Very little memory used. >> >> >> >> >> >> Throughout all of this, there was no indexing or querying happening. >> >> In ordered to try to determine what's using up the memory, I took a >> >> heap dump at point (F) and analyzed it in Eclipse MAT (see attached >> >> screenshot). This shows 311 instances of Lucene42DocValuesProducer$3, >> >> each holding a large byte[]. By attaching a remote debugger and >> >> re-running, it looks like there is one of these byte[] for each field >> >> in the schema (we have several of the "dim_*" dynamic fields). And as >> >> far as I know, I'm not using DocValues at all. >> >> >> >> >> >> Any hints as to what might be going on here would be greatly >> >> appreciated. It takes me about 10 minutes to reproduce this, so I'm >> >> willing to try things. I don't know enough about the internals of >> >> solr's memory usage to proceed much further on my own. >> >> >> >> Thank you. >> >> >> >> -Greg >> >> >>
