---
title: Elasticsearch rollup groups and configuration
description: Learn how to configure Elasticsearch rollup jobs with groupings for rollup queries and aggregations, as well as best practices for grouping time series...
url: https://www.elastic.co/docs/manage-data/lifecycle/rollup/understanding-groups
products:
  - Elasticsearch
applies_to:
  - Elastic Cloud Serverless: Unavailable
  - Elastic Stack: Generally available
---

# Elasticsearch rollup groups and configuration
<admonition title="Deprecated in 8.11.0.">
  Rollups will be removed in a future version. [Migrate](https://www.elastic.co/docs/manage-data/lifecycle/rollup/migrating-from-rollup-to-downsampling) to [downsampling](https://www.elastic.co/docs/manage-data/data-store/data-streams/downsampling-time-series-data-stream) instead.
</admonition>

Learn how to configure Elasticsearch rollup jobs with groupings for rollup queries and aggregations, as well as best practices for grouping time series data.
To preserve flexibility, Rollup Jobs are defined based on how future queries may need to use the data. Traditionally, systems force the admin to make decisions about what metrics to rollup and on what interval. For example, the average of `cpu_time` on an hourly basis. This is limiting; if, in the future, the admin wishes to see the average of `cpu_time` on an hourly basis *and* partitioned by `host_name`, they are out of luck.
Of course, the admin can decide to rollup the `[hour, host]` tuple on an hourly basis, but as the number of grouping keys grows, so do the number of tuples the admin needs to configure. Furthermore, these `[hours, host]` tuples are only useful for hourly rollups… daily, weekly, or monthly rollups all require new configurations.
Rather than force the admin to decide ahead of time which individual tuples should be rolled up, Elasticsearch’s Rollup jobs are configured based on which groups are potentially useful to future queries. For example, this configuration:
```js
"groups" : {
  "date_histogram": {
    "field": "timestamp",
    "fixed_interval": "1h",
    "delay": "7d"
  },
  "terms": {
    "fields": ["hostname", "datacenter"]
  },
  "histogram": {
    "fields": ["load", "net_in", "net_out"],
    "interval": 5
  }
}
```

Allows `date_histogram` to be used on the `"timestamp"` field, `terms` aggregations to be used on the `"hostname"` and `"datacenter"` fields, and `histograms` to be used on any of `"load"`, `"net_in"`, `"net_out"` fields.
Importantly, these aggs/fields can be used in any combination. This aggregation:
```js
"aggs" : {
  "hourly": {
    "date_histogram": {
      "field": "timestamp",
      "fixed_interval": "1h"
    },
    "aggs": {
      "host_names": {
        "terms": {
          "field": "hostname"
        }
      }
    }
  }
}
```

is just as valid as this aggregation:
```js
"aggs" : {
  "hourly": {
    "date_histogram": {
      "field": "timestamp",
      "fixed_interval": "1h"
    },
    "aggs": {
      "data_center": {
        "terms": {
          "field": "datacenter"
        }
      },
      "aggs": {
        "host_names": {
          "terms": {
            "field": "hostname"
          }
        },
        "aggs": {
          "load_values": {
            "histogram": {
              "field": "load",
              "interval": 5
            }
          }
        }
      }
    }
  }
}
```

You’ll notice that the second aggregation is not only substantially larger, it also swapped the position of the terms aggregation on `"hostname"`, illustrating how the order of aggregations does not matter to rollups. Similarly, while the `date_histogram` is required for rolling up data, it isn’t required while querying (although often used). For example, this is a valid aggregation for Rollup Search to execute:
```js
"aggs" : {
  "host_names": {
    "terms": {
      "field": "hostname"
    }
  }
}
```

Ultimately, when configuring `groups` for a job, think in terms of how you might wish to partition data in a query at a future date… then include those in the config. Because Rollup Search allows any order or combination of the grouped fields, you only need to decide if a field is useful for aggregating later, and how you might wish to use it (terms, histogram, and so on).

## Calendar versus fixed time intervals

Each rollup-job must have a date histogram group with a defined interval. Elasticsearch understands both [calendar and fixed time intervals](https://www.elastic.co/docs/reference/aggregations/search-aggregations-bucket-datehistogram-aggregation#calendar_and_fixed_intervals). Fixed time intervals are fairly easy to understand; `60s` means sixty seconds. But what does `1M` mean? One month of time depends on which month we are talking about, some months are longer or shorter than others. This is an example of calendar time and the duration of that unit depends on context. Calendar units are also affected by leap-seconds, leap-years, and so on.
This is important because the buckets generated by rollup are in either calendar or fixed intervals and this limits how you can query them later. See [Requests must be multiples of the config](/docs/manage-data/lifecycle/rollup/rollup-search-limitations#rollup-search-limitations-intervals).
We recommend sticking with fixed time intervals, since they are easier to understand and are more flexible at query time. It will introduce some drift in your data during leap-events and you will have to think about months in a fixed quantity (30 days) instead of the actual calendar length. However, it is often easier than dealing with calendar units at query time.
Multiples of units are always "fixed". For example, `2h` is always the fixed quantity `7200` seconds. Single units can be fixed or calendar depending on the unit:

| Unit        | Calendar | Fixed                    |
|-------------|----------|--------------------------|
| millisecond | NA       | `1ms`, `10ms`, and so on |
| second      | NA       | `1s`, `10s`, and so on   |
| minute      | `1m`     | `2m`, `10m`, and so on   |
| hour        | `1h`     | `2h`, `10h`, and so on   |
| day         | `1d`     | `2d`, `10d`, and so on   |
| week        | `1w`     | NA                       |
| month       | `1M`     | NA                       |
| quarter     | `1q`     | NA                       |
| year        | `1y`     | NA                       |

For some units where there are both fixed and calendar, you may need to express the quantity in terms of the next smaller unit. For example, if you want a fixed day (not a calendar day), you should specify `24h` instead of `1d`. Similarly, if you want fixed hours, specify `60m` instead of `1h`. This is because the single quantity entails calendar time, and limits you to querying by calendar time in the future.

## Grouping limitations with heterogeneous indices

There was previously a limitation in how Rollup could handle indices that had heterogeneous mappings (multiple, unrelated/non-overlapping mappings). The recommendation at the time was to configure a separate job per data "type". For example, you might configure a separate job for each Beats module that you had enabled (one for `process`, another for `filesystem`, and so on).
This recommendation was driven by internal implementation details that caused document counts to be potentially incorrect if a single "merged" job was used.
This limitation has since been alleviated. As of 6.4.0, it is now considered best practice to combine all rollup configurations into a single job.
As an example, if your index has two types of documents:
```js
{
  "timestamp": 1516729294000,
  "temperature": 200,
  "voltage": 5.2,
  "node": "a"
}
```

and
```js
{
  "timestamp": 1516729294000,
  "price": 123,
  "title": "Foo"
}
```

the best practice is to combine them into a single rollup job which covers both of these document types, like this:
```js
PUT _rollup/job/combined
{
  "index_pattern": "data-*",
  "rollup_index": "data_rollup",
  "cron": "*/30 * * * * ?",
  "page_size": 1000,
  "groups": {
    "date_histogram": {
      "field": "timestamp",
      "fixed_interval": "1h",
      "delay": "7d"
    },
    "terms": {
      "fields": [ "node", "title" ]
    }
  },
  "metrics": [
    {
      "field": "temperature",
      "metrics": [ "min", "max", "sum" ]
    },
    {
      "field": "price",
      "metrics": [ "avg" ]
    }
  ]
}
```


## Doc counts and overlapping jobs

There was previously an issue with document counts on "overlapping" job configurations, driven by the same internal implementation detail. If there were two Rollup jobs saving to the same index, where one job is a "subset" of another job, it was possible that document counts could be incorrect for certain aggregation arrangements.
This issue has also since been eliminated in 6.4.0.