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Posted to dev@lucene.apache.org by "Joel Bernstein (JIRA)" <ji...@apache.org> on 2016/04/09 21:16:25 UTC
[jira] [Comment Edited] (SOLR-8963) And new TimeStream to support
fine grain time series operations
[ https://issues.apache.org/jira/browse/SOLR-8963?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15233687#comment-15233687 ]
Joel Bernstein edited comment on SOLR-8963 at 4/9/16 7:15 PM:
--------------------------------------------------------------
Need to do some research into the best practices around timezones. I suspect lot's of dev-ops people have all servers syncing to GMT, but I could be wrong.
was (Author: joel.bernstein):
Need to do some research into the best practices around this timezones. I suspect lot's of dev-ops people have all servers syncing to GMT, but I could be wrong.
> 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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