Index Lifecycle Management in Elasticsearch

In the world of large-scale data storage, managing indices efficiently is crucial to ensure the performance and cost-effectiveness of Elasticsearch clusters. Index Lifecycle Management (ILM) is a feature in Elasticsearch that enables users to automate and optimize the management of their indices. By applying a set of lifecycle policies, ILM helps users manage indices throughout their lifespan, from creation to deletion, ensuring that data is stored efficiently, queries remain performant, and resources are used effectively.

This guide will cover the basics of ILM in Elasticsearch, how to set it up, use cases, and best practices for creating effective lifecycle policies.

What is Index Lifecycle Management (ILM)?

Index Lifecycle Management (ILM) is a feature that allows users to define policies to automatically transition indices through different stages based on age, size, or other conditions. These stages are known as phases, and each phase represents a point in the index's lifecycle. With ILM, users can automate the movement of data across various storage tiers, apply optimizations to indices, and eventually delete indices when they are no longer needed.

The main goal of ILM is to:

Reduce storage costs by automatically moving data to lower-cost hardware.
Improve query performance by reducing the number of shards and resources associated with older, less-accessed indices.
Simplify data retention and archiving by automating the deletion of stale or obsolete data.

Key Concepts in ILM

ILM operates using a few main concepts:

Index Lifecycle Policies: The set of rules and conditions that dictate how an index progresses through its lifecycle.
Phases: Each stage in the index’s lifecycle; typically, Hot, Warm, Cold, and Delete phases.
Actions: Actions are operations applied to indices in a given phase, such as rollover, shrink, freeze, and delete.

The Four Phases of ILM

1. Hot Phase:

The hot phase is where the data is actively written to and queried. This phase is suitable for recent data that is frequently accessed.
Actions available: Rollover (create a new index when certain size or age criteria are met), set the number of replicas, and force merge to optimize segments.

2. Warm Phase:

In the warm phase, data is still available for search but is less frequently queried. Here, optimizations are made to reduce the resource footprint.
Actions available: Shrink (reduces the number of shards to save resources), allocate (moves data to warm nodes with fewer resources), and read-only (prevents writes to the index to conserve memory).

3. Cold Phase:

The cold phase is for data that is rarely accessed but must still be kept online for occasional queries.
Actions available: Freeze (puts the index in a frozen state, making it queryable but at a lower cost), allocate (moves data to a node with lower resource usage), and read-only.

4. Delete Phase:

The delete phase is the final phase, where data is no longer needed, and indices are deleted to free up storage.
Actions available: Delete (removes the index permanently based on the conditions set in the ILM policy)

Setting Up ILM in Elasticsearch

To use ILM, Elasticsearch provides a REST API and Kibana interface for creating and managing policies. Here’s a step-by-step guide:

Define an ILM Policy: An ILM policy outlines the phases an index will go through and the actions to be taken in each phase. You can create a policy with a JSON structure defining each phase, conditions for moving between phases, and actions to take.

PUT _ilm/policy/my_policy
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "rollover": {
            "max_size": "50gb",
            "max_age": "30d"
          },
          "set_priority": { "priority": 100 }
        }
      },
      "warm": {
        "min_age": "30d",
        "actions": {
          "allocate": { "require": { "box_type": "warm" } },
          "set_priority": { "priority": 50 }
        }
      },
      "delete": {
        "min_age": "90d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

In this example:

Hot phase: Rollover occurs at 50 GB or 30 days, whichever comes first.
Warm phase: After 30 days, the index is moved to nodes tagged as "warm."
Delete phase: After 90 days, the index is deleted.

Apply the ILM Policy to an Index Template: Once you have a policy, you can apply it to new indices via an index template, which will automatically assign the policy to matching indices.
```
PUT _index_template/my_template
{
  "index_patterns": ["logs-*"],
  "template": {
    "settings": {
      "index.lifecycle.name": "my_policy",
      "index.lifecycle.rollover_alias": "logs"
    }
  }
}
```
This template applies the my_policy ILM policy to all indices matching the pattern logs-*.
Rollover Alias: If you are using rollover as part of your hot phase actions, ensure that a rollover alias is set up to manage the rollover process. For instance:
```
PUT /logs-000001
{
  "aliases": {
    "logs": {
      "is_write_index": true
    }
  }
}
```

Use Cases for Index Lifecycle Management

Log Management: ILM is widely used in log management, where recent logs are queried frequently but older logs are accessed rarely. A common ILM policy might keep recent logs in the hot phase, move slightly older logs to warm, and freeze or delete logs that are several months old.
Compliance and Data Retention: Many industries require data to be retained for a specific period. ILM can automate the retention of this data while efficiently archiving or deleting it once the compliance period expires.
Optimizing Storage Costs: By moving older data to lower-cost nodes or deleting stale data, ILM helps businesses optimize storage costs without sacrificing data accessibility.

Best Practices for ILM

Understand Your Data Access Patterns: Tailor your ILM policy based on how frequently data is accessed over time.
Use Rollover with High-Volume Indices: Rollover allows you to start a new index based on criteria like size or age, ensuring that high-volume indices don’t become too large and unmanageable.
Monitor ILM Policies: Regularly monitor ILM policies to ensure they are performing as expected. Kibana’s ILM UI provides insights into which indices are in each phase.
Utilize Dedicated Hardware for Phases: Set up dedicated hardware nodes for each phase (e.g., hot, warm, cold) to optimize performance and cost-efficiency.

Conclusion

Index Lifecycle Management is a powerful tool in Elasticsearch that helps maintain efficient and scalable index storage. By automating data transitions across hot, warm, cold, and delete phases, ILM ensures that resources are optimized, costs are minimized, and performance remains high. With a well-thought-out ILM strategy, organizations can handle data growth more effectively, meet compliance requirements, and streamline their Elasticsearch management.

Incorporate ILM into your Elasticsearch practices to gain better control over data lifecycles, and ensure that your Elasticsearch cluster remains optimized and ready to scale!