Elasticsearch & Full Text Search Systems · Lesson

Time-Series Data Management

Optimize Elasticsearch for time-series data, including data streams, ILM (Index Lifecycle Management), and hot-warm-cold architectures.

Time-Series Data Management is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What is Time-Series Data?

Time-series data is information collected over a period of time, often at regular intervals. Think of it as a sequence of data points indexed by time.

Examples include:

System logs: Events happening on a server.
IoT sensor readings: Temperature, humidity from devices.
Financial data: Stock prices over days or hours.

This data is typically append-only and usually immutable once recorded.

Why Elasticsearch for Time-Series?

Elasticsearch is an excellent choice for managing time-series data due to its:

Scalability: Handles massive volumes of data.
Speed: Fast indexing and search capabilities.
Analytics: Powerful aggregations for insights.
Flexibility: Can store structured and unstructured data.

It's particularly strong for use cases like log analytics, metric monitoring, and security event management.

Introducing Data Streams

Data streams are a core feature in Elasticsearch designed specifically for time-series data. They simplify management by automatically rolling over to new indices as needed.

When you index data into a data stream, it always writes to the current write index. Queries, however, search across all backing indices transparently. This makes managing large, continuously growing datasets much easier.

Creating Your First Data Stream

To create a data stream, you first need an index template that defines its structure and points to a data stream.

Then, simply indexing a document into the stream's name will create it automatically based on the template:

PUT _index_template/my-data-stream-template
{
  "index_patterns": ["my-data-stream-*"],
  "data_stream": {},
  "priority": 200,
  "template": {
    "settings": {
      "number_of_shards": 1
    },
    "mappings": {
      "properties": {
        "@timestamp": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_millis"
        },
        "message": { "type": "text" }
      }
    }
  }
}

// Indexing a document creates the stream
POST my-data-stream/_doc
{
  "@timestamp": "2023-10-27T10:00:00Z",
  "message": "Sensor reading: 25.5C"
}

Automating with ILM

Index Lifecycle Management (ILM) is a powerful feature that automates the management of your indices through various phases of their life.

For time-series data, ILM helps you:

Automatically roll over indices when they reach a certain size or age.
Move older, less frequently accessed data to cheaper storage.
Delete data that is no longer needed.

The Four ILM Phases

ILM policies define actions for indices across four main phases:

Hot: Indices are actively being written to and frequently queried. Requires fast storage.
Warm: Indices are no longer being written to, but are still queried. Can be read-only and potentially on slower storage.
Cold: Indices are rarely queried and often stored on very cheap, slow storage.
Delete: Indices are no longer needed and are safely removed from the cluster.

Crafting an ILM Policy

Here's an example of an ILM policy that defines rollover, forcemerge (for warm phase), and deletion stages for time-series data:

PUT _ilm/policy/my-ts-policy
{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_age": "7d",
            "max_docs": 10000000,
            "max_size": "50gb"
          }
        }
      },
      "warm": {
        "min_age": "30d",
        "actions": {
          "forcemerge": {
            "max_num_segments": 1
          },
          "shrink": {
            "number_of_shards": 1
          }
        }
      },
      "cold": {
        "min_age": "90d",
        "actions": {
          "freeze": {}
        }
      },
      "delete": {
        "min_age": "180d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Hot-Warm-Cold Architecture

A Hot-Warm-Cold architecture optimizes resource utilization and cost for time-series data by using different types of nodes for different ILM phases.

Hot nodes: Use fast CPUs, SSDs for high indexing and query throughput.
Warm nodes: Use less powerful CPUs, HDDs (or slower SSDs) for older, read-only data.
Cold nodes: Use very cheap, high-capacity storage, potentially even object storage, for rarely accessed data.

Assigning Node Roles

You can configure nodes to serve specific roles (hot, warm, cold) by setting node.attr in their elasticsearch.yml configuration file.

ILM policies then use these attributes to automatically move indices to the appropriate node types as they transition through phases.

# For a hot node
node.roles: [ data ]
node.attr.data: hot

# For a warm node
node.roles: [ data ]
node.attr.data: warm

# For a cold node
node.roles: [ data ]
node.attr.data: cold

ILM & Data Stream Check

Which of the following statements about Elasticsearch's time-series features are TRUE?

Time-Series Recap

Great job! You've explored how Elasticsearch is optimized for time-series data.

We covered:

Data Streams: Simplifying index management and rollovers.
ILM (Index Lifecycle Management): Automating index transitions through hot, warm, cold, and delete phases.
Hot-Warm-Cold Architectures: Optimizing cluster resources and costs by assigning different node types to specific ILM phases.

These features are essential for building scalable and cost-effective time-series solutions with Elasticsearch.

Frequently asked questions

Is the “Time-Series Data Management” lesson free?

Yes — the full text of “Time-Series Data Management” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.

What will I learn in “Time-Series Data Management”?

Optimize Elasticsearch for time-series data, including data streams, ILM (Index Lifecycle Management), and hot-warm-cold architectures. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Elasticsearch & Full Text Search Systems?

No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Time-Series Data Management” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?

Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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