---
title: ES|QL TS command
description: 
url: https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/commands/ts
products:
  - Elasticsearch
---

# ES|QL TS command
<applies-to>
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Preview from 9.2 to 9.3
</applies-to>

The `TS` source command is similar to the [`FROM`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/commands/from)
source command, with the following key differences:
- Targets only [time series indices](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3202/manage-data/data-store/data-streams/time-series-data-stream-tsds)
- Enables the use of [time series aggregation functions](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions) inside the
  [STATS](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/commands/stats-by) command


## Syntax

```esql
TS index_pattern [METADATA fields]
```


## Parameters

<definitions>
  <definition term="index_pattern">
    A list of indices, data streams or aliases. Supports wildcards and date math.
  </definition>
  <definition term="fields">
    A comma-separated list of [metadata fields](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/esql-metadata-fields) to retrieve.
  </definition>
</definitions>


## Description

The `TS` source command enables time series semantics and adds support for
[time series aggregation functions](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions) to the `STATS` command, such as
[`AVG_OVER_TIME()`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions/avg_over_time),
or [`RATE`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions/rate).
These functions are implicitly evaluated per time series, then aggregated by group using a secondary aggregation
function. For an example, refer to [Calculate the rate of search requests per host](#calculate-the-rate-of-search-requests-per-host).
This paradigm (a pair of aggregation functions) is standard for time series
querying. For supported inner (time series) functions per
[metric type](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3202/manage-data/data-store/data-streams/time-series-data-stream-tsds#time-series-metric), refer to
[ES|QL time series aggregation functions](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions). These functions also
apply to downsampled data, with the same semantics as for raw data.
<note>
  If a query is missing an inner (time series) aggregation function,
  [`LAST_OVER_TIME()`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions/last_over_time)
  is assumed and used implicitly. For example, two equivalent queries that return the average of the last memory usage values per time series are shown in [Aggregate with implicit LAST_OVER_TIME](#aggregate-with-implicit-last_over_time). To calculate the average memory usage across per-time-series averages, refer to [Calculate the average of per-time-series averages](#calculate-the-average-of-per-time-series-averages).
</note>

You can use [time series aggregation functions](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions)
directly in the `STATS` command (<applies-to>Elastic Stack: Preview since 9.3</applies-to>). The output will contain one aggregate value per time series and time bucket (if specified). For an example, refer to [Use time series aggregation functions directly](#use-time-series-aggregation-functions-directly).
You can also combine time series aggregation functions with regular [aggregation functions](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/aggregation-functions) such as `SUM()`, as outer aggregation functions. For examples, refer to [Combine SUM and RATE](#combine-sum-and-rate) and [Combine SUM and AVG_OVER_TIME](#combine-sum-and-avg_over_time).
However, using a time series aggregation function in combination with an inner time series function causes an error. For an example, refer to [Invalid query: nested time series functions](#invalid-query-nested-time-series-functions).
If there is no `STATS` command in the query, the output of the `TS` command gets sorted by `@timestamp` in descending order by default. This helps listing recent values across many time series, as opposed to listing the results based on index sort configuration that may just return data points for a single time series.

## Grouping time series

When the first `STATS` after `TS` uses a bare
[time series aggregation function](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions)
(that is, a time series function not wrapped in an outer aggregation such as `AVG()` or `SUM()`),
the rows are implicitly grouped by **all** [dimensions](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3202/manage-data/data-store/data-streams/time-series-data-stream-tsds#time-series-dimension)
of each time series. The result set includes a `_timeseries` `keyword`
column that contains a JSON-encoded object with the dimension key/value pairs
identifying each group. Only the dimensions that are actually present for a given time
series are included — not every dimension declared in the index mappings — so different
rows in the result may carry different dimension keys. For an example, refer to
[Group by all dimensions implicitly](#group-by-all-dimensions-implicitly).
You can make this grouping explicit, or narrow it to a subset of dimensions, using the
[`WITHOUT`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/without)
grouping function in the `BY` clause (<applies-to>Elastic Stack: Planned</applies-to>):
- `BY WITHOUT(dim1, dim2, ...)` groups by **all** dimensions **except** those listed.
  See [Exclude dimensions with WITHOUT](#exclude-dimensions-with-without).
- `BY WITHOUT()` (no arguments) explicitly groups by every dimension; it is equivalent
  to the implicit "group by all" behavior.

When combining a bare time series function with other groupings, only grouping functions
(such as [`TBUCKET`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/tbucket)
or [`WITHOUT`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/without)) are allowed in the `BY` clause — bare dimension columns are not.
For example, `TS k8s | STATS rate(network.total_bytes_in) BY host` is rejected; use
`BY TBUCKET(1 hour)` or wrap the time series function with an outer aggregation instead.
<note>
  `WITHOUT` can only be used inside the first `STATS` command under `TS` source. Using it in a `FROM | STATS ... BY WITHOUT(...)`
  query leads to an error.
</note>


## Best practices

- Avoid aggregating multiple metrics in the same query when those metrics have different dimensional cardinalities.
  For example, in `STATS max(rate(foo)) + rate(bar))`, if `foo` and `bar` don't share the same dimension values, the rate
  for one metric will be null for some dimension combinations. Because the + operator returns null when either input
  is null, the entire result becomes null for those dimensions. Additionally, queries that aggregate a single metric
  can filter out null values more efficiently.
- Use the `TS` command for aggregations on time series data, rather than `FROM`. The `FROM` command is still available
  (for example, for listing document contents), but it's not optimized for processing time series data and may produce
  unexpected results.
- The `TS` command can't be combined with certain operations (such as
  [`FORK`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/commands/fork)) before the `STATS` command is applied. Once `STATS` is
  applied, you can process the tabular output with any applicable ES|QL operations.
- Add a time range filter on `@timestamp` to limit the data volume scanned and improve query performance.
- Time series aggregations produce large result sets, especially if they involve many dimensions and small time buckets.
  The limits are updated accordingly, with the default result truncation size increased to 10,000 rows. For more
  information on the limits and how to adjust them, refer to
  [Result set size limitation](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/_snippets/common/result-set-size-limitation).


## Examples

The following examples demonstrate common time series query patterns using `TS`.

### Calculate the rate of search requests per host

Calculate the total rate of search requests (tracked by the `search_requests` counter) per host and hour. The `RATE()`
function is applied per time series in hourly buckets. These rates are summed for each
host and hourly bucket (since each host can map to multiple time series):
```esql
TS metrics
  | WHERE @timestamp >= now() - 1 hour
  | STATS SUM(RATE(search_requests)) BY TBUCKET(1 hour), host
```


### Aggregate with implicit LAST_OVER_TIME

The following two queries are equivalent, returning the average of the last memory usage values per time series. If a query is missing an inner (time series) aggregation function, `LAST_OVER_TIME()` is assumed and used implicitly:
```esql
TS metrics | STATS AVG(memory_usage)

TS metrics | STATS AVG(LAST_OVER_TIME(memory_usage))
```


### Calculate the average of per-time-series averages

This query calculates the average memory usage across per-time-series averages, rather than the average of all raw values:
```esql
TS metrics | STATS AVG(AVG_OVER_TIME(memory_usage))
```


### Use time series aggregation functions directly

You can use a [time series aggregation function](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/time-series-aggregation-functions) directly in `STATS` (<applies-to>Elastic Stack: Preview since 9.3</applies-to>):
```esql
TS metrics
| WHERE TRANGE(1 day)
| STATS RATE(search_requests) BY TBUCKET(1 hour)
```


### Combine SUM and RATE

Use `SUM` as the outer aggregation to sum counter rates across groups:
```esql
TS metrics | STATS SUM(RATE(search_requests)) BY host
```


### Combine SUM and AVG_OVER_TIME

Use `AVG_OVER_TIME` to compute per-time-series averages, then group the results by host and time bucket:
```esql
TS metrics
| WHERE @timestamp >= now() - 1 day
| STATS SUM(AVG_OVER_TIME(memory_usage)) BY host, TBUCKET(1 hour)
```


### Group by all dimensions implicitly

When a bare time series aggregation function is used without a `BY` clause, results are
implicitly grouped by all dimensions of each time series and include a `_timeseries`
column with the dimension key/value pairs. Note how the `qa` cluster rows only carry
`cluster` and `pod` keys, while the `prod` and `staging` rows also include `region` —
only the dimensions that actually exist for a given time series appear in `_timeseries`:
```esql
TS k8s
| STATS avg = avg_over_time(network.cost)
| SORT avg DESC
```


| avg:double         | _timeseries:keyword                                   |
|--------------------|-------------------------------------------------------|
| 8.375              | {"cluster":"prod","pod":"three","region":["eu","us"]} |
| 7.262931034482759  | {"cluster":"qa","pod":"one"}                          |
| 6.870689655172414  | {"cluster":"qa","pod":"two"}                          |
| 6.635416666666667  | {"cluster":"qa","pod":"three"}                        |
| 6.46875            | {"cluster":"staging","pod":"two","region":"us"}       |
| 5.869791666666667  | {"cluster":"staging","pod":"three","region":"us"}     |
| 5.586956521739131  | {"cluster":"prod","pod":"one","region":["eu","us"]}   |
| 5.1826923076923075 | {"cluster":"staging","pod":"one","region":"us"}       |
| 4.0703125          | {"cluster":"prod","pod":"two","region":["eu","us"]}   |


### Exclude dimensions with WITHOUT

Use [`WITHOUT`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/without)
(<applies-to>Elastic Stack: Planned</applies-to>) in the `BY` clause to exclude specific dimensions from
the time series grouping. For example, group by every dimension except `pod`:
```esql
TS k8s
| STATS total_cost = sum(network.cost) BY WITHOUT(pod)
| SORT total_cost
```


| total_cost:double | _timeseries:keyword                     |
|-------------------|-----------------------------------------|
| 15.875            | {"cluster":"staging","region":"us"}     |
| 18.625            | {"cluster":"prod","region":["eu","us"]} |
| 26.5              | {"cluster":"qa"}                        |

`WITHOUT` can be combined with
[`TBUCKET`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/tbucket)
to add a time bucket to the grouping — useful for producing per-interval aggregates
across the surviving dimensions:
```esql
TS k8s
| STATS total_cost = sum(network.cost) BY WITHOUT(pod), tbucket = TBUCKET(1 hour)
| SORT total_cost
```


| total_cost:double | _timeseries:keyword                     | tbucket:datetime         |
|-------------------|-----------------------------------------|--------------------------|
| 15.875            | {"cluster":"staging","region":"us"}     | 2024-05-10T00:00:00.000Z |
| 18.625            | {"cluster":"prod","region":["eu","us"]} | 2024-05-10T00:00:00.000Z |
| 26.5              | {"cluster":"qa"}                        | 2024-05-10T00:00:00.000Z |

Passing no arguments (`WITHOUT()`) is equivalent to grouping by all dimensions — the same
as omitting the `BY` clause entirely. Refer to the
[`WITHOUT`](https://www.elastic.co/elastic/docs-builder/docs/3202/reference/query-languages/esql/functions-operators/grouping-functions/without)
function reference for more examples.

### Invalid query: nested time series functions

Using a time series aggregation function in combination with an inner time series function causes an error:
```esql
TS metrics | STATS AVG_OVER_TIME(RATE(memory_usage))
```