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* Some documentation fixes * Address review comments * Fixed lint issues using prettifier. Co-authored-by: achatterjee-grafana <aparajita.chatterjee@grafana.com>
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5.4 KiB
Markdown
86 lines
5.4 KiB
Markdown
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title = "Time series dimensions"
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description = "time series dimensions"
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keywords = ["grafana", "intro", "guide", "concepts", "timeseries", "labels"]
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aliases = ["/docs/grafana/latest/guides/timeseries-dimensions", "/docs/grafana/latest/getting-started/timeseries-dimensions"]
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weight = 600
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# Time series dimensions
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In [Introduction to time series]({{< relref "timeseries.md#time-series-databases" >}}), the concept of _labels_, also called _tags_, is introduced:
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> Another feature of a TSDB is the ability to filter measurements using _tags_. Each data point is labeled with a tag that adds context information, such as where the measurement was taken.
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With time series data, the data often contain more than a single series, and is a set of multiple time series. Many Grafana data sources support this type of data.
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{{< figure src="/static/img/docs/example_graph_multi_dim.png" class="docs-image--no-shadow" max-width="850px" >}}
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The common case is issuing a single query for a measurement with one or more additional properties as dimensions. For example, querying a temperature measurement along with a location property. In this case, multiple series are returned back from that single query and each series has unique location as a dimension.
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To identify unique series within a set of time series, Grafana stores dimensions in _labels_.
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## Labels
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Each time series in Grafana optionally has labels. Labels are set a of key/value pairs for identifying dimensions. Example labels could be `{location=us}` or `{country=us,state=ma,city=boston}`. Within a set of time series, the combination of its name and labels identifies each series. For example, `temperature {country=us,state=ma,city=boston}` could identify the series of temperature values for the city of Boston in the US.
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Different sources of time series data have dimensions stored natively, or common storage patterns that allow the data to be extracted into dimensions.
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Time series databases (TSDBs) usually natively support dimensionality. Prometheus also stores dimensions in _labels_. In TSDBs such as Graphite or OpenTSDB the term _tags_ is used instead.
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In table databases such SQL, these dimensions are generally the `GROUP BY` parameters of a query.
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## Multiple dimensions in table format
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In SQL or SQL-like databases that return table responses, additional dimensions are usually represented as columns in the query response table.
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### Single dimension
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For example, consider a query like:
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```sql
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SELECT BUCKET(StartTime, 1h), AVG(Temperature) AS Temp, Location FROM T
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GROUP BY BUCKET(StartTime, 1h), Location
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ORDER BY time asc
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```
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This query would return a table with three columns with data types time, number, and string respectively:
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| StartTime | Temp | Location |
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| --------- | ---- | -------- |
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| 09:00 | 24 | LGA |
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| 09:00 | 20 | BOS |
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| 10:00 | 26 | LGA |
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| 10:00 | 22 | BOS |
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The table format is a _long_ formatted time series, also called _tall_. It has repeated time stamps, and repeated values in Location. In this case, we have two time series in the set that would be identified as `Temp {Location=LGA}` and `Temp {Location=BOS}`.
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Individual time series from the set are extracted by using the time typed column `StartTime` as the time index of the time series, the numeric typed column `Temp` as the series name, and the name and values of the string typed `Location` column to build the labels, such as Location=LGA.
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### Multiple dimensions
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If the query is updated to select and group by more than just one string column, for example, `GROUP BY BUCKET(StartTime, 1h), Location, Sensor`, then an additional dimension is added:
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| StartTime | Temp | Location | Sensor |
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| --------- | ---- | -------- | ------ |
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| 09:00 | 24 | LGA | A |
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| 09:00 | 24.1 | LGA | B |
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| 09:00 | 20 | BOS | A |
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| 09:00 | 20.2 | BOS | B |
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| 10:00 | 26 | LGA | A |
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| 10:00 | 26.1 | LGA | B |
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| 10:00 | 22 | BOS | A |
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| 10:00 | 22.2 | BOS | B |
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In this case the labels that represent the dimensions will have two keys based on the two string typed columns `Location` and `Sensor`. This data results four series: `Temp {Location=LGA,Sensor=A}`, `Temp {Location=LGA,Sensor=B}`, `Temp {Location=BOS,Sensor=A}`, and `Temp {Location=BOS,Sensor=B}`.
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> **Note:** More than one dimension is currently only supported in the Logs queries within the Azure Monitor service as of version 7.1.
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> **Note:** Multiple dimensions are not supported in a way that maps to multiple alerts in Grafana, but rather they are treated as multiple conditions to a single alert. See the documentation on [creating alerts with multiple series]({{< relref "../alerting/old-alerting/create-alerts.md#multiple-series" >}}).
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### Multiple values
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In the case of SQL-like data sources, more than one numeric column can be selected, with or without additional string columns to be used as dimensions. For example, `AVG(Temperature) AS AvgTemp, MAX(Temperature) AS MaxTemp`. This, if combined with multiple dimensions, can result in a lot of series. Selecting multiple values is currently only designed to be used with visualization.
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Additional technical information on tabular time series formats and how dimensions are extracted can be found in [the developer documentation on data frames as time series]({{< relref "../developers/plugins/data-frames.md#data-frames-as-time-series" >}}).
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