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Posted to commits@druid.apache.org by GitBox <gi...@apache.org> on 2019/11/22 08:07:26 UTC
[GitHub] [incubator-druid] SehanRathnayake opened a new issue #8927:
Requests with period granularity give different results with Different
Segment Granularities when the Origin time component overlaps with DST
transition Time
SehanRathnayake opened a new issue #8927: Requests with period granularity give different results with Different Segment Granularities when the Origin time component overlaps with DST transition Time
URL: https://github.com/apache/incubator-druid/issues/8927
Requests with period granularity give different results with Different Segment Granularities when origin value has time component which overlaps DST transition.
### Affected Version
0.16.0-incubating,
### Description
When querying with period granularity type with period P1D with origin value which time is 02:00:00 (which overlaps with DST transition on March 10 ) with America/Los_Angeles timezone give different results when datasource has DAY Segment Granularity vs Month Segment Granularity
**Dataset**
`
{"timestamp":1552125600000,"type":"March 09, 2019 02:00:00 (am)","amount":10}
{"timestamp":1552212000000,"type":"March 10, 2019 03:00:00 (am)","amount":10}
{"timestamp":1553500800000,"type":"March 25, 2019 01:00:00 (am)","amount":10}
{"timestamp":1554886800000,"type":"April 10, 2019 02:00:00 (am)","amount":10}
`
QueryGranularity : THIRTY_MINUTES
**Request**
```javascript
{
"queryType":"groupBy",
"granularity":{
"type":"period",
"period":"P1D",
"timeZone":"America/Los_Angeles",
"origin":"2018-01-19T02:00:00.000-08:00"
},
"dataSource":{
"type":"union",
"dataSources":[
"datasource"
]
},
"dimensions":[
"type"
],
"aggregations":[
{
"type":"doubleSum",
"name":"toatalSale",
"fieldName":"sum_amount"
}
],
"intervals":[
"2018-01-21T04:22:26.000+0000/2019-04-30T23:59:59.999+0000"
]
}
```
**case 1 : With DAY Segment Granularity**
```javascript
[
{
"version":"v1",
"timestamp":"2019-03-09T02:00:00.000-08:00",
"event":{
"toatalSale":10.0,
"type":"March 09, 2019 02:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-03-10T03:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"March 10, 2019 03:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-03-24T02:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"March 25, 2019 01:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-04-10T02:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"April 10, 2019 02:00:00 (am)"
}
}
]
```
**case 2 : Datasource with Month Segment Granularity**
```javascript
[
{
"version":"v1",
"timestamp":"2019-03-09T02:00:00.000-08:00",
"event":{
"toatalSale":10.0,
"type":"March 09, 2019 02:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-03-10T03:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"March 10, 2019 03:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-03-24T03:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"March 25, 2019 01:00:00 (am)"
}
},
{
"version":"v1",
"timestamp":"2019-04-10T02:00:00.000-07:00",
"event":{
"toatalSale":10.0,
"type":"April 10, 2019 02:00:00 (am)"
}
}
]
```
Here when Querying in the datasource with DAY segment Granularity 3rd bucket has start with 2019-03-24T02:00:00.000-07:00 where when Querying in the datasource with Month segment Granularity 3rd bucket has start with 2019-03-24T03:00:00.000-07:00. Which is inconsistent.
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