Thanks!
On Mon, Mar 17, 2014 at 3:47 PM, Steve Rowe <sar...@gmail.com> wrote: > 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 > >> > >> > >
