Hot-Warm Architecture Templateedit

A template that you typically use for time-series analytics and log aggregation workloads that benefit from tiered-storage automatic index curation. Includes features to manage resources efficiently when you need greater capacity, such as:

  • A tiered architecture with two different types of data nodes, hot and warm.
  • Time-based indices, with automatic index curation to move indices from hot to warm nodes over time by changing their shard allocation.

The two type of data nodes in a hot-warm architecture each have their own characteristics:

Hot data node
Handles all indexing of new data in the cluster and holds the most recent daily indices that tend to be queried most frequently. Indexing is an I/O intensive activity and the hardware these nodes run on needs to be more powerful and use SSD storage.
Warm data node
Handles a large amount of read-only indices that are not queried frequently. With read-only indices, warm nodes can use very large spindle drives instead of SSD storage.

Index Curationedit

One of the key features of a hot-warm architecture, time-based index curation automates the task of moving data from hot to warm nodes as it ages. When you deploy a hot-warm architecture, Elastic Cloud Enterprise performs regular index curation according to these rules:

  • Index curation moves indices from one Elasticsearch node to another by changing their shard allocation, always from hot to warm.
  • Index curation is always time-based and takes place when an index reaches the age specified, in days, weeks, or months.
  • Index curation always targets indexes according to one or more matching patterns. If an index matches a pattern, Elastic Cloud Enterprise moves it from a hot to a warm node.

You can define which indices get curated and when during the process of creating your deployment.

To learn more about how hot-warm architectures work with Elasticsearch, see “Hot-Warm” Architecture in Elasticsearch 5.x.

In This Templateedit

The following features are included with this template:

  • Elasticsearch:

    • Data nodes - hot: Starts at 4 GB memory x 1 availability zone. Uses the data.default instance configuration.
    • Data nodes - warm: Starts at 4 GB memory x 1 availability zone. Uses the data.highstorage instance configuration.
    • Master nodes: One master node per availability zone, plus a tiebreaker master node when you use two zones. With fewer than five data nodes, master nodes are co-located with the data nodes. With five or more data nodes, dedicated master-eligible nodes are used. Uses the master instance configuration.
  • Kibana: Starts at 1 GB memory x 1 availability zone. Uses the kibana instance configuration.
  • Machine learning (ML): Disabled by default. The functionality is pre-wired into the template, but you must explicitly enable it in the UI. Uses the ml instance configuration.

To use this template effectively, you must tag your allocators and edit the default instance configurations, so that ECE knows where to host the Elastic Stack products that are part of your deployment.