Downsampling a time series data streamedit

This functionality is in technical preview and may be changed or removed in a future release. Elastic will apply best effort to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

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 as it ages.

Check the following sections to learn more:

How it worksedit

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.

time series function

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.

time series metric anatomy

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.

time series original
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.

time series downsampled
Figure 3. Downsampled metrics series

Running downsampling on time series dataedit

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

POST /<source_index>/_downsample/<new_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"
          }
        }
      }
    }
  }
}

Querying downsampled indicesedit

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:

  • When you run queries in Kibana and through Elastic solutions, a normal response is returned without notification that some of the queried indices are downsampled.
  • For date histogram aggregations, only fixed_intervals (and not calendar-aware intervals) are supported.
  • Only Coordinated Universal Time (UTC) date-times are supported.

Restrictions and limitationsedit

The following restrictions and limitations apply for downsampling:

  • Only indices in a time series data stream are supported.
  • Data is downsampled based on the time dimension only. All other dimensions are copied to the new index without any modification.
  • Within a data stream, a downsampled index replaces the original index and the original index is deleted. Only one index can exist for a given time period.
  • A source index must be in read-only mode for the downsampling process to succeed. Check the Run downsampling manually example for details.
  • Downsampling data for the same period many times (downsampling of a downsampled index) is supported. The downsampling interval must be a multiple of the interval of the downsampled index.
  • Downsampling is provided as an ILM action. See Downsample.
  • The new, downsampled index is created on the data tier of the original index and it inherits its settings (for example, the number of shards and replicas).
  • The numeric gauge and counter metric types are supported.
  • The downsampling configuration is extracted from the time series data stream index mapping. The only additional required setting is the downsampling fixed_interval.

Try it outedit

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.