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https://issues.apache.org/jira/browse/SOLR-8963?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joel Bernstein reopened SOLR-8963:
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> 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: 6.2, master (7.0)
>
>
> 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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