<h3><u>#general</u></h3><br><strong>@mailtobuchi: </strong>Quick question about
Pinot query.
If this was the Pinot query result plan, does this mean `numSegmentsProcessed`
segments were mem mapped?
```{
"resultTable": {
"dataSchema": {
"columnDataTypes": ["BYTES"],
"columnNames": ["id"]
},
"rows": [
["6a254bd3c853e950"]
]
},
"exceptions": [],
"numServersQueried": 4,
"numServersResponded": 4,
"numSegmentsQueried": 1237,
"numSegmentsProcessed": 1229,
"numSegmentsMatched": 4,
"numConsumingSegmentsQueried": 8,
"numDocsScanned": 4,
"numEntriesScannedInFilter": 4,
"numEntriesScannedPostFilter": 4,
"numGroupsLimitReached": false,
"totalDocs": 265510367,
"timeUsedMs": 32,
"segmentStatistics": [],
"traceInfo": {},
"minConsumingFreshnessTimeMs": 1595297570989
}```
<br><strong>@mayanks: </strong>No, this is the number of segments the query had
to process.<br><strong>@hiboss1: </strong>@hiboss1 has joined the
channel<br><strong>@dlavoie: </strong>Wouldn't Pinot make an incredible
datasource for Grafana?<br><strong>@pradeepgv42: </strong>@steotia Thanks a lot
for enabling the TEXT_MATCH feature on dictionary encoded columns.
on a smaller table with ~25M rows, simple regexp_like query takes 178ms vs
TEXT_MATCH takes ~30ms
This is pretty cool.<br><strong>@g.kishore: </strong>Amazing video by
@kennybastani on Deploying Pinot on Kubernetes
<https://u17000708.ct.sendgrid.net/ls/click?upn=1BiFF0-2FtVRazUn1cLzaiMc92bR9g-2BkGUUQX5IM7P9-2BAhHRVzOXTS92je0dEky-2B6erluPv4yRe5Qxf8-2BPzQrpHg-3D-3DJHPT_vGLQYiKGfBLXsUt3KGBrxeq6BCTMpPOLROqAvDqBeTybC1-2B-2FcUzX2RLE0WEiXpW8-2FoU2Y6JwxpYBGsUTMOfMOQsqm51Fyzpb3bLaYfh1TSyYryVJawHiPEsIw2FwM9lfYIO-2FfnyBg2faUY-2FGTgbUWpBy2hTlI03MzGgGB5mzTur8qzcpXO4hA0qczzwAyzOolfDvN60lMyZoH2Uda0Bgz2qIA-2BXBWWA94G1dOwatLvc-3D><br><strong>@rahulvinaykumar.chhap:
</strong>Thanks for sharing :slightly_smiling_face:<br><strong>@sanjay:
</strong>@sanjay has joined the
channel<br><h3><u>#random</u></h3><br><strong>@hiboss1: </strong>@hiboss1 has
joined the channel<br><strong>@sanjay: </strong>@sanjay has joined the
channel<br><h3><u>#troubleshooting</u></h3><br><strong>@ankit.raj.singh:
</strong>@ankit.raj.singh has joined the channel<br><strong>@elon.azoulay:
</strong>We are about to upgrade to pinot-0.4.0 - do you recommend going to
head or just cutting it at the 0.4.0 release commit?<br><strong>@elon.azoulay:
</strong>Any notable config changes, or k8s changes we should be aware of?
We're on pinot-0.3.0 now<br><strong>@damianoporta: </strong>Nooooo I have just
upgraded my custom aggregation function :smile: did you change the
API?<br><strong>@damianoporta: </strong>:joy:<br><strong>@g.kishore:
</strong>@elon.azoulay I would go with 0.4.0 unless you need any feature in
master<br><strong>@quietgolfer: </strong>Sorry, I think I've asked before (I
lost my slack history). Is there an easy way to have Pinot take the realtime
inputs and automatically run data ingestion jobs to populate the offline
tables? Mostly checking to see if I can shortcut some work for a v1
deliverable. I assume there is probably a simple setup to output the kafka
topic for 1 day, split the data and run batch ingestion
jobs.<br><strong>@g.kishore: </strong>Yes, it’s doable but there is no such
tool <br><strong>@g.kishore: </strong>You can download the real-time segments
use Pinot segment reader to read multiple segments to generate a new offline
segment and push it<br>
