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https://issues.apache.org/jira/browse/SOLR-2218?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13049020#comment-13049020
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jess canabou commented on SOLR-2218:
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Hi all
I'm a bit confused by this thread, but think I have the same or almost same
issue. I'm searching on a document with over 7000000 entries. I'm using the
start and rows parameters (querying 30000 recs at a time), and notice the query
times getting increasingly large, the further into the document I get. Unlike
Bill, I do not care about scores or relevancy, and am having difficulty
understanding whether the docid is a suitable solution to my problem. Is there
something I can simply tack onto the end of my query to help speed up these
query times? From what I understand, it's not necessary for me to be sorting
all the rows before the chunk of data I'm querying on
My query looks as below.
http://hostname/solr/select/?q=blablabla&version=2.2&start=4000000rows=30000&indent=on&fl=<bunch
of fields>
Any help would be greatly appreciated :)
> Performance of start= and rows= parameters are exponentially slow with large
> data sets
> --------------------------------------------------------------------------------------
>
> Key: SOLR-2218
> URL: https://issues.apache.org/jira/browse/SOLR-2218
> Project: Solr
> Issue Type: Improvement
> Components: Build
> Affects Versions: 1.4.1
> Reporter: Bill Bell
>
> With large data sets, > 10M rows.
> Setting start=<large number> and rows=<large numbers> is slow, and gets
> slower the farther you get from start=0 with a complex query. Random also
> makes this slower.
> Would like to somehow make this performance faster for looping through large
> data sets. It would be nice if we could pass a pointer to the result set to
> loop, or support very large rows=<number>.
> Something like:
> rows=1000
> start=0
> spointer=string_my_query_1
> Then within interval (like 5 mins) I can reference this loop:
> Something like:
> rows=1000
> start=1000
> spointer=string_my_query_1
> What do you think? Since the data is too great the cache is not helping.
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