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https://issues.apache.org/jira/browse/SOLR-13494?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joel Bernstein updated SOLR-13494:
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Attachment: Screen Shot 2019-05-28 at 4.50.54 PM.png
> Add DeepRandomStream implementation
> -----------------------------------
>
> Key: SOLR-13494
> URL: https://issues.apache.org/jira/browse/SOLR-13494
> Project: Solr
> Issue Type: Improvement
> Security Level: Public(Default Security Level. Issues are Public)
> Components: streaming expressions
> Reporter: Joel Bernstein
> Assignee: Joel Bernstein
> Priority: Major
> Attachments: SOLR-13494.patch, SOLR-13494.patch, Screen Shot
> 2019-05-28 at 4.50.54 PM.png
>
>
> Currently the *random* Streaming Expression performs a conventional
> distributed search. This involves retrieving the top N docs from each shard
> and then selecting the top N from all the shards in the aggregator node. This
> technique eventually bogs down as the number of shards goes up and/or N goes
> up.
> Selecting distributed random samples does not actually require this behavior.
> Instead you can select N/numShards from each shard and simply return all
> results. This technique will actually get faster as more shards are added
> instead of slowing down.
> This ticket will allow the random Streaming Expression to use the strategy
> above when N reaches a certain threshold (ie 10000).
> The *DeepRandomStream* class will implement the deep random sampling behavior.
> The random Streaming Expression will switch between the RandomStream and
> DeepRandomStream depending on N.
> Local testing shows astounding performance on random sampling with the new
> technique.
> Selecting a random sample of 250,000 documents with two numeric fields and
> running a regression analysis on the sample set takes under a second.
> Attached is a screen shot with the math expression code.
>
>
>
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