Downsampling a time series data stream

Downsampling provides a method to reduce the footprint of your time series data by storing it at reduced granularity.

Metrics solutions collect large amounts of time series data that grow over time. As that data ages, it becomes less relevant to the current state of the system. The downsampling process rolls up documents within a fixed time interval into a single summary document. Each summary document includes statistical representations of the original data: the min, max, sum, value_count, and average for each metric. Data stream time series dimensions are stored unchanged.

Downsampling, in effect, lets you to trade data resolution and precision for storage size. You can include it in an index lifecycle management (ILM) policy to automatically manage the volume and associated cost of your metrics data at it ages.

Check the following sections to learn more:

A time series is a sequence of observations taken over time for a specific entity. The observed samples can be represented as a continuous function, where the time series dimensions remain constant and the time series metrics change over time.

In an Elasticsearch index, a single document is created for each timestamp, containing the immutable time series dimensions, together with the metrics names and the changing metrics values. For a single timestamp, several time series dimensions and metrics may be stored.

For your most current and relevant data, the metrics series typically has a low sampling time interval, so it’s optimized for queries that require a high data resolution.

Figure 2. Original metrics series

Downsampling works on older, less frequently accessed data by replacing the original time series with both a data stream of a higher sampling interval and statistical representations of that data. Where the original metrics samples may have been taken, for example, every ten seconds, as the data ages you may choose to reduce the sample granularity to hourly or daily. You may choose to reduce the granularity of cold archival data to monthly or less.

Figure 3. Downsampled metrics series

To downsample a time series index, use the Downsample API and set fixed_interval to the level of granularity that you’d like:

POST /my-time-series-index/_downsample/my-downsampled-time-series-index
{
    "fixed_interval": "1d"
}

To downsample time series data as part of ILM, include a Downsample action in your ILM policy and set fixed_interval to the level of granularity that you’d like:

PUT _ilm/policy/my_policy
{
  "policy": {
    "phases": {
      "warm": {
        "actions": {
          "downsample" : {
            "fixed_interval": "1h"
          }
        }
      }
    }
  }
}

You can use the _search and _async_search endpoints to query a downsampled index. Multiple raw data and downsampled indices can be queried in a single request, and a single request can include downsampled indices at different granularities (different bucket timespan). That is, you can query data streams that contain downsampled indices with multiple downsampling intervals (for example, 15m, 1h, 1d).

The result of a time based histogram aggregation is in a uniform bucket size and each downsampled index returns data ignoring the downsampling time interval. For example, if you run a date_histogram aggregation with "fixed_interval": "1m" on a downsampled index that has been downsampled at an hourly resolution ("fixed_interval": "1h"), the query returns one bucket with all of the data at minute 0, then 59 empty buckets, and then a bucket with data again for the next hour.

NOTE:

There are a few things to note when querying downsampled indices:

The following restrictions and limitations apply for downsampling:

To take downsampling for a test run, try our example of running downsampling manually.

Downsampling can easily be added to your ILM policy. To learn how, try our Run downsampling with ILM example.