ClickHouse/docs/en/sql-reference/aggregate-functions/reference/timeseriesgroupsum.md
Ivan Blinkov 7170f3c534
[docs] split aggregate function and system table references (#11742)
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* split aggregate function reference

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2020-06-18 11:24:31 +03:00

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toc_priority: 170
---
# timeSeriesGroupSum {#agg-function-timeseriesgroupsum}
Syntax: `timeSeriesGroupSum(uid, timestamp, value)`
`timeSeriesGroupSum` can aggregate different time series that sample timestamp not alignment.
It will use linear interpolation between two sample timestamp and then sum time-series together.
- `uid` is the time series unique id, `UInt64`.
- `timestamp` is Int64 type in order to support millisecond or microsecond.
- `value` is the metric.
The function returns array of tuples with `(timestamp, aggregated_value)` pairs.
Before using this function make sure `timestamp` is in ascending order.
Example:
``` text
┌─uid─┬─timestamp─┬─value─┐
│ 1 │ 2 │ 0.2 │
│ 1 │ 7 │ 0.7 │
│ 1 │ 12 │ 1.2 │
│ 1 │ 17 │ 1.7 │
│ 1 │ 25 │ 2.5 │
│ 2 │ 3 │ 0.6 │
│ 2 │ 8 │ 1.6 │
│ 2 │ 12 │ 2.4 │
│ 2 │ 18 │ 3.6 │
│ 2 │ 24 │ 4.8 │
└─────┴───────────┴───────┘
```
``` sql
CREATE TABLE time_series(
uid UInt64,
timestamp Int64,
value Float64
) ENGINE = Memory;
INSERT INTO time_series VALUES
(1,2,0.2),(1,7,0.7),(1,12,1.2),(1,17,1.7),(1,25,2.5),
(2,3,0.6),(2,8,1.6),(2,12,2.4),(2,18,3.6),(2,24,4.8);
SELECT timeSeriesGroupSum(uid, timestamp, value)
FROM (
SELECT * FROM time_series order by timestamp ASC
);
```
And the result will be:
``` text
[(2,0.2),(3,0.9),(7,2.1),(8,2.4),(12,3.6),(17,5.1),(18,5.4),(24,7.2),(25,2.5)]
```