[v11.0.x] postgres: improved tests (#86181)
postgres: improved tests (#84677)
* postgres: tests: better test aligning time-series results
* postgres: add snapshot test for a backward-compat code
(cherry picked from commit c835022861)
Co-authored-by: Gábor Farkas <gabor.farkas@gmail.com>
This commit is contained in:
grafana-delivery-bot[bot]
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parent
6204c035df
commit
bce6d445e9
+65
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// 🌟 This was machine generated. Do not edit. 🌟
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//
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// Frame[0] {
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// "typeVersion": [
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// 0,
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// 0
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// ],
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// "executedQueryString": "SELECT floor(extract(epoch from \"t\")/300)*300 AS \"time\",avg(v) FROM tbl GROUP BY 1"
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// }
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// Name:
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// Dimensions: 2 Fields by 1 Rows
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// +-------------------------------+------------------+
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// | Name: time | Name: avg |
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// | Labels: | Labels: |
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// | Type: []*time.Time | Type: []*float64 |
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// +-------------------------------+------------------+
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// | 2023-12-24 14:30:00 +0000 UTC | 42 |
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// +-------------------------------+------------------+
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//
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//
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// 🌟 This was machine generated. Do not edit. 🌟
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"executedQueryString": "SELECT floor(extract(epoch from \"t\")/300)*300 AS \"time\",avg(v) FROM tbl GROUP BY 1"
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},
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"fields": [
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{
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"name": "time",
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"type": "time",
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"typeInfo": {
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"frame": "time.Time",
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"nullable": true
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}
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},
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{
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"name": "avg",
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"type": "number",
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"typeInfo": {
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"frame": "float64",
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"nullable": true
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"data": {
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"values": [
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[
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1703428200000
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],
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[
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42
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]
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}
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}
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]
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}
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+10
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-- SELECT $__timeGroup("t",5m,NULL),avg(v) FROM tbl GROUP BY 1
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-- we change behavior based on what follows the timegroup, see:
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-- https://github.com/grafana/grafana/blob/0b4c81158ea32a671e7788b888b086082343407c/pkg/tsdb/grafana-postgresql-datasource/macros.go#L36
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CREATE TEMPORARY TABLE tbl (
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t timestamp with time zone,
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v double precision
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);
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INSERT INTO tbl (t, v) VALUES
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('2023-12-24 14:30:03 UTC', 42);
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+65
@@ -0,0 +1,65 @@
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// 🌟 This was machine generated. Do not edit. 🌟
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//
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// Frame[0] {
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// "typeVersion": [
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// 0,
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// 0
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// ],
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// "executedQueryString": "SELECT floor(extract(epoch from \"t\")/300)*300 ,avg(v) FROM tbl GROUP BY 1"
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// }
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// Name:
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// Dimensions: 2 Fields by 1 Rows
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// +------------------+------------------+
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// | Name: ?column? | Name: avg |
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// | Labels: | Labels: |
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// | Type: []*float64 | Type: []*float64 |
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// +------------------+------------------+
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// | 1.7034282e+09 | 42 |
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// +------------------+------------------+
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//
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//
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// 🌟 This was machine generated. Do not edit. 🌟
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"executedQueryString": "SELECT floor(extract(epoch from \"t\")/300)*300 ,avg(v) FROM tbl GROUP BY 1"
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"fields": [
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"name": "?column?",
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"nullable": true
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{
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"name": "avg",
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"type": "number",
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"frame": "float64",
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"nullable": true
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1703428200
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}
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+10
@@ -0,0 +1,10 @@
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-- SELECT $__timeGroup("t",5m,NULL) ,avg(v) FROM tbl GROUP BY 1
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-- we change behavior based on what follows the timegroup, see:
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-- https://github.com/grafana/grafana/blob/0b4c81158ea32a671e7788b888b086082343407c/pkg/tsdb/grafana-postgresql-datasource/macros.go#L36
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CREATE TEMPORARY TABLE tbl (
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t timestamp with time zone,
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v double precision
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);
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INSERT INTO tbl (t, v) VALUES
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('2023-12-24 14:30:03 UTC', 42);
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+100
@@ -0,0 +1,100 @@
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// 🌟 This was machine generated. Do not edit. 🌟
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//
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// Frame[0] {
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// "typeVersion": [
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// 0,
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// 0
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// ],
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// "executedQueryString": "SELECT floor(extract(epoch from \"time\")/300)*300 AS \"time\",avg(v1) AS \"v1\", avg(v2) AS \"v2\" FROM tbl GROUP BY 1 ORDER BY 1"
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// }
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// Name:
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// Dimensions: 3 Fields by 7 Rows
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// +-------------------------------+------------------+------------------+
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// | Name: Time | Name: v1 | Name: v2 |
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// | Labels: | Labels: | Labels: |
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// | Type: []*time.Time | Type: []*float64 | Type: []*float64 |
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// +-------------------------------+------------------+------------------+
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// | 2023-12-24 14:15:00 +0000 UTC | 27 | 27 |
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// | 2023-12-24 14:20:00 +0000 UTC | 15 | 115 |
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// | 2023-12-24 14:25:00 +0000 UTC | 27 | 27 |
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// | 2023-12-24 14:30:00 +0000 UTC | 27 | 27 |
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// | 2023-12-24 14:35:00 +0000 UTC | 50 | 150 |
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// | 2023-12-24 14:40:00 +0000 UTC | 27 | 27 |
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// | 2023-12-24 14:45:00 +0000 UTC | 27 | 27 |
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// +-------------------------------+------------------+------------------+
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//
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//
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// 🌟 This was machine generated. Do not edit. 🌟
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{
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"status": 200,
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"frames": [
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{
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"schema": {
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"meta": {
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"typeVersion": [
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0,
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0
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],
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"executedQueryString": "SELECT floor(extract(epoch from \"time\")/300)*300 AS \"time\",avg(v1) AS \"v1\", avg(v2) AS \"v2\" FROM tbl GROUP BY 1 ORDER BY 1"
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},
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"fields": [
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{
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"name": "Time",
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"type": "time",
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"typeInfo": {
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"frame": "time.Time",
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"nullable": true
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}
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},
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{
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"name": "v1",
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"type": "number",
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"typeInfo": {
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"frame": "float64",
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"nullable": true
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}
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},
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{
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"name": "v2",
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"type": "number",
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"typeInfo": {
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"frame": "float64",
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"nullable": true
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}
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}
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]
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},
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"data": {
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"values": [
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[
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1703427300000,
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1703427600000,
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1703427900000,
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1703428200000,
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1703428500000,
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1703428800000,
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1703429100000
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],
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[
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27,
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15,
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27,
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27,
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50,
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27,
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27
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],
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[
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27,
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115,
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27,
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27,
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150,
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27,
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27
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]
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]
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}
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}
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]
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}
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+12
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-- SELECT $__timeGroup("time",5m,27),avg(v1) AS "v1", avg(v2) AS "v2" FROM tbl GROUP BY 1 ORDER BY 1
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-- tests fill-mode=value
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CREATE TEMPORARY TABLE tbl (
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"time" timestamp with time zone,
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v1 double precision,
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v2 double precision
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);
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INSERT INTO tbl ("time", v1, v2) VALUES
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('2023-12-24 14:21:03 UTC', 10, 110),
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('2023-12-24 14:23:03 UTC', 20, 120),
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('2023-12-24 14:39:03 UTC', 50, 150);
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