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https://issues.apache.org/jira/browse/SOLR-1316?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12879799#action_12879799
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Stefan Seidel commented on SOLR-1316:
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Hi. First of all, thanks for making this effort for a usable auto-suggest
feature.
I have a question which is (hopefully) relevant to all this: we tried using the
facet method for autosuggest as presented in David's book about solr. It works
well and usually fast, however, there are two issues: (a) initializing the
faceting takes time (>300 sec. for 8.3 million docs here), which would be ok if
not (b) after any commit (even without new documents added) the faceting needs
exactly this time again. On a heavy load server with commits every 60 seconds,
this doesn't work at all. Also, it makes solr use >1G of RAM, and I can get it
into HeapSpace errors when multiple queries are running and the faceting hasn't
been initialized.
Question: has this scenario (frequent commits) been considered a use case when
implementing these new components? Will the tree get rebuilt at every commit as
well? (Judging from the previous numbers this should take about 10 sec. in our
scenario?) I think this is a crucial question for the whole feature.
> Create autosuggest component
> ----------------------------
>
> Key: SOLR-1316
> URL: https://issues.apache.org/jira/browse/SOLR-1316
> Project: Solr
> Issue Type: New Feature
> Components: search
> Affects Versions: 1.4
> Reporter: Jason Rutherglen
> Assignee: Shalin Shekhar Mangar
> Priority: Minor
> Fix For: Next
>
> Attachments: SOLR-1316.patch, SOLR-1316.patch, SOLR-1316.patch,
> SOLR-1316.patch, SOLR-1316.patch, SOLR-1316.patch, suggest.patch,
> suggest.patch, suggest.patch, TST.zip
>
> Original Estimate: 96h
> Remaining Estimate: 96h
>
> Autosuggest is a common search function that can be integrated
> into Solr as a SearchComponent. Our first implementation will
> use the TernaryTree found in Lucene contrib.
> * Enable creation of the dictionary from the index or via Solr's
> RPC mechanism
> * What types of parameters and settings are desirable?
> * Hopefully in the future we can include user click through
> rates to boost those terms/phrases higher
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