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From "Joel Bernstein (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-8963) And new TimeStream to support fine grain time series operations
Date Sat, 09 Apr 2016 19:20:25 GMT

    [ https://issues.apache.org/jira/browse/SOLR-8963?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15233689#comment-15233689
] 

Joel Bernstein commented on SOLR-8963:
--------------------------------------

We should also add a *skew* parameter to skew the time X seconds to support cross-correlation.

> And new TimeStream to support fine grain time series operations
> ---------------------------------------------------------------
>
>                 Key: SOLR-8963
>                 URL: https://issues.apache.org/jira/browse/SOLR-8963
>             Project: Solr
>          Issue Type: New Feature
>            Reporter: Joel Bernstein
>             Fix For: 6.1
>
>
> The TimeStream will read Tuples from an underlying stream and expand a unix timestamp
into the individual fields: year, month, day, hour, week, minute, second, milli-second).
> This will allow rollups to made on any time grain. This should be very useful for time
series log analysis.
> Sample syntax:
> {code}
> rollup(
>             time(search(...,sort="timestamp asc", fl="timestamp,..."), field="timestamp")
>             over="year, month,day,hour,minute,second,millis",
>             sum(a_i),
>             sum(a_f),
>             min(a_i),
>             min(a_f),
>             max(a_i),
>            max(a_f),
>            avg(a_i),
>            avg(a_f),
>            count(*))
> {code}
> Example broken down by customer:
> {code}
> rollup(
>             time(search(...,sort="customer asc, timestamp asc", fl="timestamp,..."),
field="timestamp")
>             over="customer, year, month,day,hour,minute,second,millis",
>             sum(a_i),
>             sum(a_f),
>             min(a_i),
>             min(a_f),
>             max(a_i),
>            max(a_f),
>            avg(a_i),
>            avg(a_f),
>            count(*))
> {code}
> To do parallel time series rollups just wrap in a parallel stream and add the partitionKeys
to the search.
> {code}
> paralllel(..., (rollup(
>             time(search(...,sort="customer asc, timestamp asc", fl="timestamp,...", partitionKeys="customer"),
field="timestamp")
>             over="customer, year, month,day,hour,minute,second,millis",
>             sum(a_i),
>             sum(a_f),
>             min(a_i),
>             min(a_f),
>             max(a_i),
>            max(a_f),
>            avg(a_i),
>            avg(a_f),
>            count(*)))
> {code}



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