```yaml {applies_to}
serverless: preview
stack: preview 9.2.0
```

**Brief description**

The `TS` source command is similar to the [`FROM`](/reference/query-languages/esql/commands/from.md)
source command, with the following key differences:

 - Targets only [time series indices](docs-content://manage-data/data-store/data-streams/time-series-data-stream-tsds.md)
 - Enables the use of [time series aggregation functions](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md) inside the
   [STATS](/reference/query-languages/esql/commands/stats-by.md) command

**Syntax**

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

**Parameters**

`index_pattern`
:   A list of indices, data streams or aliases. Supports wildcards and date math.

`fields`
:   A comma-separated list of [metadata fields](/reference/query-languages/esql/esql-metadata-fields.md) to retrieve.

**Description**

The `TS` source command enables time series semantics and adds support for
[time series aggregation functions](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md) to the `STATS` command, such as
[`AVG_OVER_TIME()`](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md#esql-avg_over_time),
or [`RATE`](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md#esql-rate).
These functions are implicitly evaluated per time series, then aggregated by group using a secondary aggregation
function. See the Examples section for a query that calculates the total rate of search requests per host and hour.

This paradigm—a pair of aggregation functions—is standard for time series
querying. For supported inner (time series) functions per
[metric type](docs-content://manage-data/data-store/data-streams/time-series-data-stream-tsds.md#time-series-metric), refer to
[](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md). 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()`](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md#esql-last_over_time)
is assumed and used implicitly. For instance, two equivalent queries are shown in the Examples section that return the average of the last memory usage values per time series. To calculate the average memory usage across per-time-series averages, see the Examples section.
::::

You can use [time series aggregation functions](/reference/query-languages/esql/functions-operators/time-series-aggregation-functions.md)
directly in the `STATS` command ({applies_to}`stack: preview 9.3`). The output will contain one aggregate value per time series and time bucket (if specified). See the Examples section for examples.

You can also combine time series aggregation functions with regular [aggregation functions](/reference/query-languages/esql/functions-operators/aggregation-functions.md) such as `SUM()`, as outer aggregation functions. See the Examples section for examples.

However, using a time series aggregation function in combination with an inner time series function causes an error. See the Examples section for an invalid query example.

**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 procesing time series data and may produce
  unexpected results.
- The `TS` command can't be combined with certain operations (such as
  [`FORK`](/reference/query-languages/esql/commands/fork.md)) 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.

**Examples**

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
```

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 memory usage across per-time-series averages:

```esql
TS metrics | STATS AVG(AVG_OVER_TIME(memory_usage))
```

Using a time series aggregation function directly ({applies_to}`stack: preview 9.3`):

```esql
TS metrics
| WHERE TRANGE(1 day)
| STATS RATE(search_requests) BY TBUCKET(1 hour)
```

Combining a time series aggregation function with a regular aggregation function:

```esql
TS metrics | STATS SUM(RATE(search_requests)) BY host
```

Combining a time series aggregation function with a regular aggregation function:

```esql
TS metrics
| WHERE @timestamp >= now() - 1 day
| STATS SUM(AVG_OVER_TIME(memory_usage)) BY host, TBUCKET(1 hour)
```

The following query is invalid because 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))
```