---
title: ES|QL TS_COLLAPSE command
description: 
url: https://docs-v3-preview.elastic.dev/elastic/elasticsearch/pull/149405/reference/query-languages/esql/commands/ts_collapse
products:
  - Elasticsearch
---

# ES|QL TS_COLLAPSE command
<applies-to>
  - Elastic Cloud Serverless: Preview
  - Elastic Stack: Planned
</applies-to>

`TS_COLLAPSE` is a processing command that collapses the expanded, per-step output of a `PROMQL`
command into one row per time series. The metric column and the `step` column become multi-valued
fields; dimension columns remain single-valued.

## Syntax

```esql
... | TS_COLLAPSE
```


## Parameters

None.

## Description

A `PROMQL` command produces one row per `(series, step)` pair, with dimension columns (for
example `host`, `cluster`) repeated on every row. `TS_COLLAPSE` merges all rows that belong to
the same series into a single row:
- The metric column (the named result column, for example `rate`) and the `step` column become
  multi-valued (MV) fields whose elements are ordered chronologically.
- Dimension columns keep a single value per row because they are identical for all steps of a
  given series.
- Missing or `null` samples are omitted from both MV fields, so the result is sparse rather than
  null-filled.

The MV fields are **positionally aligned**: `step[i]` corresponds to the metric value at index
`i`. Applying MV functions independently to one of the two columns—for example
[`MV_SORT`](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/pull/149405/reference/query-languages/esql/functions-operators/mv-functions/mv_sort)—will
break this alignment.
`TS_COLLAPSE` can only appear immediately after a `PROMQL` command. Other processing commands
can follow `TS_COLLAPSE`.

## Examples


### Collapse a multi-step result

A `PROMQL` query without `TS_COLLAPSE` returns one row per `(series, step)` pair. Adding
`TS_COLLAPSE` produces one row per series, with the metric and `step` columns as positionally
aligned MV arrays:
```esql
PROMQL index=k8s step=1m start="2024-05-10T00:11:00.000Z" end="2024-05-10T00:14:00.000Z" cost=(round(sum(avg_over_time(network.cost[1m])), 0.0001))
| TS_COLLAPSE
```


| cost:double                       | step:datetime                                                                                            |
|-----------------------------------|----------------------------------------------------------------------------------------------------------|
| [13.25, 36.375, 37.9375, 39.5417] | [2024-05-10T00:11:00.000Z, 2024-05-10T00:12:00.000Z, 2024-05-10T00:13:00.000Z, 2024-05-10T00:14:00.000Z] |


### Collapse a per-dimension result

When the `PROMQL` query groups results by a dimension (for example `cluster`), `TS_COLLAPSE`
returns one row per unique dimension value, with metric values and steps collapsed into MV arrays:
```esql
PROMQL index=k8s step=1m start="2024-05-10T00:02:00.000Z" end="2024-05-10T00:04:00.000Z" clients=(avg by (cluster) (last_over_time(network.eth0.currently_connected_clients[1m])))
| TS_COLLAPSE
| SORT cluster
```


| clients:double        | step:datetime                                                                  | cluster:keyword |
|-----------------------|--------------------------------------------------------------------------------|-----------------|
| [396.5, 632.5, 742.0] | [2024-05-10T00:02:00.000Z, 2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z] | prod            |
| [440.0, 565.0, 454.0] | [2024-05-10T00:02:00.000Z, 2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z] | qa              |
| [205.0, 357.0]        | [2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z]                           | staging         |


### Post-process with EVAL

Use [`EVAL`](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/pull/149405/reference/query-languages/esql/commands/eval) with
[MV functions](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/pull/149405/reference/query-languages/esql/functions-operators/mv-functions) to compute
per-series aggregates over the collapsed arrays, then sort or filter on the results:
```esql
PROMQL index=k8s step=1m start="2024-05-10T00:02:00.000Z" end="2024-05-10T00:04:00.000Z" clients=(avg by (cluster) (last_over_time(network.eth0.currently_connected_clients[1m])))
| TS_COLLAPSE
| EVAL avg_clients = MV_AVG(clients)
| SORT avg_clients DESC
| LIMIT 10
```


| clients:double        | step:datetime                                                                  | cluster:keyword | avg_clients:double |
|-----------------------|--------------------------------------------------------------------------------|-----------------|--------------------|
| [396.5, 632.5, 742.0] | [2024-05-10T00:02:00.000Z, 2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z] | prod            | 590.3333333333334  |
| [440.0, 565.0, 454.0] | [2024-05-10T00:02:00.000Z, 2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z] | qa              | 486.3333333333333  |
| [205.0, 357.0]        | [2024-05-10T00:03:00.000Z, 2024-05-10T00:04:00.000Z]                           | staging         | 281.0              |