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https://issues.apache.org/jira/browse/SOLR-11741?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16464830#comment-16464830
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Abhishek Kumar Singh commented on SOLR-11741:
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[^SOLR-11741.patch]
> 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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