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From "Abhishek Kumar Singh (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-11741) Offline training mode for schema guessing
Date Sat, 05 May 2018 16:32:00 GMT

    [ https://issues.apache.org/jira/browse/SOLR-11741?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16464829#comment-16464829
] 

Abhishek Kumar Singh commented on SOLR-11741:
---------------------------------------------

Uploading the updated patch, with following features:-

 A new URP LearnSchemaUpdateRequestProcessorFactory: It simply learns from the incoming data
to check what the current  data type looks like. Based on, it updates the metadata about
each field. 

 

APIs: 

> Offline training mode for schema guessing
> -----------------------------------------
>
>                 Key: SOLR-11741
>                 URL: https://issues.apache.org/jira/browse/SOLR-11741
>             Project: Solr
>          Issue Type: Improvement
>      Security Level: Public(Default Security Level. Issues are Public) 
>            Reporter: Ishan Chattopadhyaya
>            Priority: Major
>         Attachments: RuleForMostAccomodatingField.png, SOLR-11741-temp.patch, SOLR-11741.patch,
screenshot-1.png, screenshot-3.png
>
>
> Our data driven schema guessing doesn't work under many situations. For example, if the
first document has a field with value "0", it is guessed as Long and subsequent fields with
"0.0" are rejected. Similarly, if the same field had alphanumeric contents for a latter document,
those documents are rejected. Also, single vs. multi valued field guessing is not ideal.
> Proposing an offline training mode where Solr accepts bunch of documents and returns
a guessed schema (without indexing). This schema can then be used for actual indexing. I think
the original idea is from Hoss.
> I think initial implementation can be based on an UpdateRequestProcessor. We can hash
out the API soon, as we go along.



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