Data tiers

A data tier is a collection of nodes within a cluster that share the same data node role, and a hardware profile that’s appropriately sized for the role. Elastic recommends that nodes in the same tier share the same hardware profile to avoid hot spotting.

The data tiers that you use, and the way that you use them, depends on the data’s category.

The following data tiers are can be used with each data category:

Content data:

Time series data:

The way data tiers are used often depends on the data’s category:

Learn more about each data tier, including when and how it should be used.

Data stored in the content tier is generally a collection of items such as a product catalog or article archive. Unlike time series data, the value of the content remains relatively constant over time, so it doesn’t make sense to move it to a tier with different performance characteristics as it ages. Content data typically has long data retention requirements, and you want to be able to retrieve items quickly regardless of how old they are.

Content tier nodes are usually optimized for query performance—​they prioritize processing power over IO throughput so they can process complex searches and aggregations and return results quickly. While they are also responsible for indexing, content data is generally not ingested at as high a rate as time series data such as logs and metrics. From a resiliency perspective the indices in this tier should be configured to use one or more replicas.

The content tier is required and is often deployed within the same node grouping as the hot tier. System indices and other indices that aren’t part of a data stream are automatically allocated to the content tier.

The hot tier is the Elasticsearch entry point for time series data and holds your most-recent, most-frequently-searched time series data. Nodes in the hot tier need to be fast for both reads and writes, which requires more hardware resources and faster storage (SSDs). For resiliency, indices in the hot tier should be configured to use one or more replicas.

The hot tier is required. New indices that are part of a data stream are automatically allocated to the hot tier.

Time series data can move to the warm tier once it is being queried less frequently than the recently-indexed data in the hot tier. The warm tier typically holds data from recent weeks. Updates are still allowed, but likely infrequent. Nodes in the warm tier generally don’t need to be as fast as those in the hot tier. For resiliency, indices in the warm tier should be configured to use one or more replicas.

When you no longer need to search time series data regularly, it can move from the warm tier to the cold tier. While still searchable, this tier is typically optimized for lower storage costs rather than search speed.

For better storage savings, you can keep fully mounted indices of searchable snapshots on the cold tier. Unlike regular indices, these fully mounted indices don’t require replicas for reliability. In the event of a failure, they can recover data from the underlying snapshot instead. This potentially halves the local storage needed for the data. A snapshot repository is required to use fully mounted indices in the cold tier. Fully mounted indices are read-only.

Alternatively, you can use the cold tier to store regular indices with replicas instead of using searchable snapshots. This lets you store older data on less expensive hardware but doesn’t reduce required disk space compared to the warm tier.

Once data is no longer being queried, or being queried rarely, it may move from the cold tier to the frozen tier where it stays for the rest of its life.

The frozen tier requires a snapshot repository. The frozen tier uses partially mounted indices to store and load data from a snapshot repository. This reduces local storage and operating costs while still letting you search frozen data. Because Elasticsearch must sometimes fetch frozen data from the snapshot repository, searches on the frozen tier are typically slower than on the cold tier.

Follow the instructions for your deployment type to configure data tiers.

The default configuration for an Elastic Cloud deployment includes a shared tier for hot and content data. This tier is required and can’t be removed.

To add a warm, cold, or frozen tier when you create a deployment:

To add a data tier to an existing deployment:

To remove a data tier, refer to Disable a data tier.

For self-managed deployments, each node’s data role is configured in elasticsearch.yml. For example, the highest-performance nodes in a cluster might be assigned to both the hot and content tiers:

node.roles: ["data_hot", "data_content"]

The index.routing.allocation.include._tier_preference setting determines which tier the index should be allocated to.

When you create an index, by default Elasticsearch sets the _tier_preference to data_content to automatically allocate the index shards to the content tier.

When Elasticsearch creates an index as part of a data stream, by default Elasticsearch sets the _tier_preference to data_hot to automatically allocate the index shards to the hot tier.

At the time of index creation, you can override the default setting by explicitly setting the preferred value in one of two ways:

You can override this setting after index creation by updating the index setting to the preferred value.

This setting also accepts multiple tiers in order of preference. This prevents indices from remaining unallocated if no nodes are available in the preferred tier. For example, when index lifecycle management migrates an index to the cold phase, it sets the index _tier_preference to data_cold,data_warm,data_hot.

To remove the data tier preference setting, set the _tier_preference value to null. This allows the index to allocate to any data node within the cluster. Setting the _tier_preference to null does not restore the default value. Note that, in the case of managed indices, a migrate action might apply a new value in its place.

You can check an existing index’s data tier preference by polling its settings for index.routing.allocation.include._tier_preference:

resp = client.indices.get_settings(
    index="my-index-000001",
    filter_path="*.settings.index.routing.allocation.include._tier_preference",
)
print(resp)

const response = await client.indices.getSettings({
  index: "my-index-000001",
  filter_path: "*.settings.index.routing.allocation.include._tier_preference",
});
console.log(response);

GET /my-index-000001/_settings?filter_path=*.settings.index.routing.allocation.include._tier_preference

The _tier_preference setting might conflict with other allocation settings. This conflict might prevent the shard from allocating. A conflict might occur when a cluster has not yet been completely migrated to data tiers.

This setting will not unallocate a currently allocated shard, but might prevent it from migrating from its current location to its designated data tier. To troubleshoot, call the cluster allocation explain API and specify the suspected problematic shard.

ILM automatically transitions managed indices through the available data tiers using the migrate action. By default, this action is automatically injected in every phase. You can explicitly specify the migrate action with "enabled": false to disable automatic migration, for example, if you’re using the allocate action to manually specify allocation rules.