May 2019
Intermediate to advanced
442 pages
11h 36m
English
Content preview from Hands-On Infrastructure Monitoring with Prometheus
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Longitudinal aggregation
Longitudinal aggregations are trickier to use because you need to select a time window over which to apply the aggregation. This means they work over rows, as we can see in the following representation:
| Location/Time | t=0 | t=1 | t=2 | t=3 | t=4 | t=5 | t=6 |
| Factory | 1,045 | 1 | 2 | 3 | 4 | 5 | 6 |
| Warehouse | 223 | 223 | 223 | 223 | 224 | 224 | 224 |
| Headquarters | 40,160 | 40,162 | 40,164 | 40,166 | 40,168 | 40,170 | 40,172 |
Since the current selectors we're using return three rows of data, this means we'll have three results when applying longitudinal aggregations. In this example, we've selected the last three minutes of data for aggregation (as we mentioned previously, we're considering a 1-minute sample interval). If we apply the max() aggregation over ...
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