[[search-aggregations-bucket-time-series-aggregation]]
=== Time series aggregation
++++
<titleabbrev>Time series</titleabbrev>
++++

preview::[]

The time series aggregation queries data created using a <<tsds,Time series data stream (TSDS)>>. 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.

[NOTE]
====
Refer to the <<differences-from-regular-data-stream, TSDS documentation>> to learn more about the key differences from regular data streams.
====

//////////////////////////

Creating a time series mapping

To create an index with the time series mapping, specify "mode" as "time_series" in the index settings,
"routing_path" specifying the a list of time series fields, and a start and end time for the series. Each of the
"routing_path" fields must be keyword fields with "time_series_dimension" set to true. Additionally, add a
date field used as the timestamp.

[source,js]
--------------------------------------------------
PUT /my-time-series-index
{
  "settings": {
    "index": {
      "number_of_shards": 3,
      "number_of_replicas": 2,
      "mode": "time_series",
      "routing_path": ["key"],
      "time_series": {
        "start_time": "2022-01-01T00:00:00Z",
        "end_time": "2023-01-01T00:00:00Z"
      }
    }
  },
  "mappings": {
    "properties": {
        "key": {
            "type": "keyword",
            "time_series_dimension": true
        },
        "@timestamp": {
          "type": "date"
        }
    }
  }
}
-------------------------------------------------
// NOTCONSOLE

//////////////////////////

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

[source,js]
--------------------------------------------------
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" }
--------------------------------------------------
// NOTCONSOLE

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.

[source,js,id=time-series-aggregation-example]
--------------------------------------------------
GET /_search
{
  "aggs": {
    "ts": {
      "time_series": { "keyed": false }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

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.

[[search-aggregations-bucket-time-series-aggregation-size]]
==== Size

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.

[[search-aggregations-bucket-time-series-aggregation-keyed]]
==== Keyed

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.

[[times-series-aggregations-limitations]]
==== Limitations

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`.
