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Add practical tips for downsampling. #3340

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Add practical tips for downsampling
gmarouli Oct 6, 2025
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32 changes: 32 additions & 0 deletions manage-data/data-store/data-streams/run-downsampling.md
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Expand Up @@ -102,6 +102,38 @@ Set `fixed_interval` to your preferred level of granularity. The original time s
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## Practical tips

Downsampling requires reading and indexing the contents of a backing index. The following guidelines can help you get the most out of it.

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Do we need a note about rollover? To avoid creating backing indices that are too big..

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I have been going back and forth for this. For ILM it's easy because it's part of the policy, for data stream lifecycle, I would suggest that if we really think that it should be less maybe we should set it to something less. Right?

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You mean, update the default? We can do that at a later point, but what about older versions, or ILM configurations with existing rollover overrides? It could still help to suggest a best practice here.

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Yes, we could update the default, that would apply on all version unless the user chose to overwrite it. I restructure it a bit so we can have ILM focused recommendations. But if we think it should be reduced, we should consider updating the default for DLM as well.

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Let's file a tracking issue for this, so that we don't forget.

### Choosing the downsampling interval

When choosing the downsampling interval, you need to consider the original sampling rate of your measurements. Ideally, you would like an interval that would reduce your number of documents by a significant amount. For example, if a sensor sends data every 10 seconds downsampling to 1 minute would reduce the number of documents by 83%, compared to downsampling to 5 minutes by 96%.
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When choosing the downsampling interval, you need to consider the original sampling rate of your measurements. Ideally, you would like an interval that would reduce your number of documents by a significant amount. For example, if a sensor sends data every 10 seconds downsampling to 1 minute would reduce the number of documents by 83%, compared to downsampling to 5 minutes by 96%.
When choosing the downsampling interval, you need to consider the original sampling rate of your measurements. Ideally, you would like an interval that would reduce your number of documents by a significant amount. For example, if a sensor sends data every 10 seconds, downsampling to 1 minute would reduce the number of documents by 83%, compared to downsampling to 5 minutes by 96%.


The same applied when downsampling already downsampled data.

### Downsampling with Index Lifecycle Management

The following tips apply to data streams downsampled by index lifecycle management (ILM).

#### Reducing index size

When configuring an ILM policy with downsampling, it is necessary to define the [rollover action](elasticsearch://reference/elasticsearch/index-lifecycle-actions/ilm-rollover.md) in the `hot` phase. The rollover action consists of the conditions that would trigger a rollover hence it determines the size of an index and its shards. The size of an index can influence the impact that downsampling has on a cluster's performance.

The downsampling operation runs over a whole index, so in certain cases downsampling can increase the load on a cluster. One of the ways to reduce that load is to reduce the size of the index; this way you can have smaller downsampling tasks that get better distributed. You can achieve that either by reducing the number of primary shards or by using setting [`max_primary_shard_docs`](https://www.elastic.co/docs/reference/elasticsearch/index-lifecycle-actions/ilm-rollover#ilm-rollover-options) to reduce the number of docs in a single shard. Using a lower value than the default of 200 million is expected to help smoothen load spikes due to downsampling.

#### Phases and tiers

When using ILM, you can define at most one downsampling round in the following phases:

- `hot` phase: it will execute the downsampling after the [index time series end time](elasticsearch://reference/elasticsearch/index-settings/time-series.md#index-time-series-end-time) has passed
- `warm` phase: it will execute the downsampling `min_age` time after the rollover (respecting the [index time series end time](elasticsearch://reference/elasticsearch/index-settings/time-series.md#index-time-series-end-time))
- `cold` phase: it will execute the downsampling `min_age` time after the rollover (respecting the [index time series end time](elasticsearch://reference/elasticsearch/index-settings/time-series.md#index-time-series-end-time))

The phases do not require the respective tiers to exist. However, when a cluster has tiers, ILM automatically migrates the data processed in the phase to the respective tier. This can be disabled by adding the [migrate action](elasticsearch://reference/elasticsearch/index-lifecycle-actions/ilm-migrate.md#ilm-migrate-options) with `enabled: false`.

The migrate action is implicitly enabled, so unless explicitly disabled, the downsampling data will have to move to the respective tier; the downsampling operation occurs at the same tier as the source index and then the downsampled data gets migrated, this implementation choice allows downsampling to leverage the better resources from the "hotter" tier and move less data to the next tier.

## Additional resources

* [](downsampling-concepts.md)
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