Time series aggregationedit

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

The time series aggregation queries data created using a time series index. This is typically data such as metrics or other data streams with a time component, and requires creating an index using the time series mode.

Data can be added to the time series index like other indices:

PUT /my-time-series-index-0/_bulk
{ "index": {} }
{ "key": "a", "val": 1, "@timestamp": "2022-01-01T00:00:10Z" }
{ "index": {}}
{ "key": "a", "val": 2, "@timestamp": "2022-01-02T00:00:00Z" }
{ "index": {} }
{ "key": "b", "val": 2, "@timestamp": "2022-01-01T00:00:10Z" }
{ "index": {}}
{ "key": "b", "val": 3, "@timestamp": "2022-01-02T00:00:00Z" }

To perform a time series aggregation, specify "time_series" as the aggregation type. When the boolean "keyed" is true, each bucket is given a unique key.

GET /_search
  "aggs": {
    "ts": {
      "time_series": { "keyed": false }

This will return all results in the time series, however a more typical query will use sub aggregations to reduce the date returned to something more relevant.


By default, time series aggregations return 10000 results. The "size" parameter can be used to limit the results further. Alternatively, using sub aggregations can limit the amount of values returned as a time series aggregation.


The keyed parameter determines if buckets are returned as a map with unique keys per bucket. By default with keyed set to false, buckets are returned as an array.


The time_series aggregation has many limitations. Many aggregation performance optimizations are disabled when using the time_series aggregation. For example the filter by filter optimization or collect mode breath first (terms and multi_terms aggregation forcefully use the depth first collect mode).

The following aggregations also fail to work if used in combination with the time_series aggregation: auto_date_histogram, variable_width_histogram, rare_terms, global, composite, sampler, random_sampler and diversified_sampler.