What is ILM in Elasticsearch: Explanation and Example

ILM (Index Lifecycle Management) in Elasticsearch is a feature that automates the management of index data through different phases like hot, warm, and cold. It helps keep your data organized and storage efficient by automatically moving or deleting indexes based on rules you set.

How It Works

Think of ILM as a smart organizer for your Elasticsearch data. Just like you might sort your emails into folders like Inbox, Archive, and Trash based on their age or importance, ILM moves your data through different stages called phases.

Each phase has specific actions: the hot phase is for new, frequently accessed data; the warm phase stores older data that is accessed less often; and the cold phase holds data rarely accessed but still kept for reference. Finally, the delete phase removes data that is no longer needed.

This automation saves you time and storage space by handling data lifecycle without manual intervention.

Example

This example shows a simple ILM policy that moves an index from hot to warm phase after 7 days, then deletes it after 30 days.

json

{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_age": "7d",
            "max_size": "50gb"
          }
        }
      },
      "warm": {
        "min_age": "7d",
        "actions": {
          "forcemerge": {
            "max_num_segments": 1
          },
          "shrink": {
            "number_of_shards": 1
          }
        }
      },
      "delete": {
        "min_age": "30d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Output

{"acknowledged":true}

When to Use

Use ILM when you want to manage large amounts of data efficiently without manual work. It is especially helpful for logs, metrics, or time-series data that grows quickly and becomes less important over time.

For example, a company storing server logs can keep recent logs in fast storage for quick search, move older logs to cheaper storage, and delete logs after a certain period to save space and costs.

Key Points

• ILM automates index management by moving data through lifecycle phases.
• Phases include hot, warm, cold, and delete, each with specific actions.
• Saves storage and improves performance by optimizing data placement.
• Ideal for time-based data like logs and metrics.

Key Takeaways

ILM automates managing Elasticsearch indexes through lifecycle phases.

It moves data from hot to warm to cold storage, then deletes it based on rules.

ILM helps save storage space and keeps search performance efficient.

It is best used for time-series or log data that changes importance over time.