Elasticsearch & Full Text Search Systems · Lesson

Metric Aggregations

Perform calculations like sum, average, min, max, and count across your data using various metric aggregation types.

Metric Aggregations is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 1 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.

Discover Data Insights

What if you could ask "how much total sales did we have last month?" or "what's the average price of our products?" Aggregations in Elasticsearch let you do exactly that!

They provide powerful analytical capabilities beyond simple search. Think of them as SQL's GROUP BY and aggregate functions, but for your search data.

Unpacking Metric Aggregations

Metric aggregations are the simplest type of aggregation. They calculate a single metric (like a sum, average, min, or max) over a set of documents.

They give you numerical summaries of your data, helping you quickly understand key statistics and trends without retrieving all individual documents.

Indexing Sample Sales Data

To demonstrate metric aggregations, let's index some sample sales data. We'll use documents with a price field.

Run this curl command to add our first product:

curl -X PUT "localhost:9200/sales_data/_doc/1?pretty" -H 'Content-Type: application/json' -d'
{
  "product": "Laptop",
  "price": 1200,
  "quantity": 1
}'

Adding More Sample Data

Great! To have more data for our aggregations, let's add two more products. Imagine you've run similar curl commands for these:

sales_data/_doc/2: {"product": "Mouse", "price": 25, "quantity": 2}
sales_data/_doc/3: {"product": "Keyboard", "price": 75, "quantity": 1}

Now we have a small dataset to work with!

Calculating the Average (`avg`)

The avg aggregation calculates the arithmetic mean of a numeric field. It's perfect for finding the average price of products, average age, or average score.

Let's find the average price of our indexed products:

curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "average_price": {
      "avg": {
        "field": "price"
      }
    }
  }
}'

Summing Up Values (`sum`)

The sum aggregation calculates the total sum of a numeric field's values. This is ideal for finding total sales, total quantity, or total revenue.

Let's calculate the total price of all indexed products:

curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "total_price": {
      "sum": {
        "field": "price"
      }
    }
  }
}'

Finding Min and Max (`min`, `max`)

The min and max aggregations return the smallest and largest values of a numeric field, respectively. They help identify outliers or boundary values in your data.

Let's find the cheapest and most expensive product prices:

curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "min_price": { "min": { "field": "price" } },
    "max_price": { "max": { "field": "price" } }
  }
}'

Counting Values (`value_count`)

The value_count aggregation counts the number of documents that have a value for a specific field. It's different from a document count because it only considers documents where the field exists and is not null.

Let's count how many products have a 'price' field:

curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "price_count": {
      "value_count": {
        "field": "price"
      }
    }
  }
}'

All Stats in One Go (`stats`)

The stats aggregation is a convenience aggregation that computes min, max, sum, avg, and count (of values) all at once for a numeric field.

It saves you from writing five separate aggregations and is great for a quick overview!

curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
  "size": 0,
  "aggs": {
    "price_stats": {
      "stats": {
        "field": "price"
      }
    }
  }
}'

Metric Aggregation Check

Consider a scenario where you have a list of product prices: 100, 50, 200, 50, null.

What would be the result of a value_count aggregation on the 'price' field?

Metric Aggregations Recap

In this lesson, you've learned about Elasticsearch metric aggregations, powerful tools for summarizing your data:

avg: Calculates the arithmetic mean.
sum: Computes the total sum.
min & max: Finds the smallest and largest values.
value_count: Counts non-null field values.
stats: A convenient way to get all basic stats at once.

These aggregations are fundamental for understanding the numerical characteristics of your data and are often combined for more complex analysis.

Frequently asked questions

Is the “Metric Aggregations” lesson free?

Yes — the full text of “Metric Aggregations” 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 “Metric Aggregations”?

Perform calculations like sum, average, min, max, and count across your data using various metric aggregation types. 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 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Metric Aggregations” 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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