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From "Grant Ingersoll (Resolved) (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SOLR-2218) Performance of start= and rows= parameters are exponentially slow with large data sets
Date Thu, 06 Oct 2011 13:56:29 GMT

     [ https://issues.apache.org/jira/browse/SOLR-2218?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Grant Ingersoll resolved SOLR-2218.
-----------------------------------

    Resolution: Duplicate

Dup of SOLR-1726
                
> 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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