Mike, Days. I plan on making a 4.7.1 release candidate a week from today, and assuming nobody finds any problems with the RC, it will be released roughly four days thereafter (three days for voting + one day for release propogation to the Apache mirrors): i.e., next Friday-ish.
Steve On Mar 17, 2014, at 4:40 PM, Mike Hugo <m...@piragua.com> wrote: > Thanks Steve, > > That certainly looks like it could be the culprit. Any word on a release > date for 4.7.1? Days? Weeks? Months? > > Mike > > > On Mon, Mar 17, 2014 at 3:31 PM, Steve Rowe <sar...@gmail.com> wrote: > >> Hi Mike, >> >> The OOM you're seeing is likely a result of the bug described in (and >> fixed by a commit under) SOLR-5875: < >> https://issues.apache.org/jira/browse/SOLR-5875>. >> >> If you can build from source, it would be great if you could confirm the >> fix addresses the issue you're facing. >> >> This fix will be part of a to-be-released Solr 4.7.1. >> >> Steve >> >> On Mar 17, 2014, at 4:14 PM, Mike Hugo <m...@piragua.com> wrote: >> >>> Hello, >>> >>> We recently upgraded to Solr Cloud 4.7 (went from a single node Solr 4.0 >>> instance to 3 node Solr 4.7 cluster). >>> >>> Part of out application does an automated traversal of all documents that >>> match a specific query. It does this by iterating through results by >>> setting the start and rows parameters, starting with start=0 and >> rows=1000, >>> then start=1000, rows=1000, start = 2000, rows=1000, etc etc. >>> >>> We do this in parallel fashion with multiple workers on multiple nodes. >>> It's easy to chunk up the work to be done by figuring out how many total >>> results there are and then creating 'chunks' (0-1000, 1000-2000, >> 2000-3000) >>> and sending each chunk to a worker in a pool of multi-threaded workers. >>> >>> This worked well for us with a single server. However upon upgrading to >>> solr cloud, we've found that this quickly (within the first 4 or 5 >>> requests) causes an OutOfMemory error on the coordinating node that >>> receives the query. I don't fully understand what's going on here, but >> it >>> looks like the coordinating node receives the query and sends it to the >>> shard requested. For example, given: >>> >>> shards=shard3&sort=id+asc&start=4000&q=*:*&rows=1000 >>> >>> The coordinating node sends this query to shard3: >>> >>> NOW=1395086719189&shard.url= >>> >> http://shard3_url_goes_here:8080/solr/collection1/&fl=id&sort=id+asc&start=0&q=*:*&distrib=false&wt=javabin&isShard=true&fsv=true&version=2&rows=5000 >>> >>> Notice the rows parameter is 5000 (start + rows). If the coordinator >> node >>> is able to process the result set (which works for the first few pages, >>> after that it will quickly run out of memory), it eventually issues this >>> request back to shard3: >>> >>> NOW=1395086719189&shard.url= >>> >> http://10.128.215.226:8080/extera-search/gemindex/&start=4000&ids=a..bunch...(1000)..of..doc..ids..go..here&q=*:*&distrib=false&wt=javabin&isShard=true&version=2&rows=1000 >>> >>> and then finally returns the response to the client. >>> >>> One possible workaround: We've found that if we issue non-distributed >>> requests to specific shards, that we get performance along the same lines >>> that we did before. E.g. issue a query with shards=shard3&distrib=false >>> directly to the url of the shard3 instance, rather than going through the >>> cloud solr server solrj API. >>> >>> The other workaround is to adapt to use the new new cursorMark >>> functionality. I've manually tried a few requests and it is pretty >>> efficient, and doesn't result in the OOM errors on the coordinating node. >>> However, i've only done this in single threaded manner. I'm wondering if >>> there would be a way to get cursor marks for an entire result set at a >>> given page interval, so that they could then be fed to the pool of >> parallel >>> workers to get the results in parallel rather than single threaded. Is >>> there a way to do this so we could process the results in parallel? >>> >>> Any other possible solutions? Thanks in advance. >>> >>> Mike >> >>
