What is Pipeline Aggregation in Elasticsearch: Explained Simply

In Elasticsearch, pipeline aggregation processes the output of other aggregations instead of raw data. It lets you perform calculations like moving averages or derivatives on aggregated results to analyze trends or changes over time.

How It Works

Imagine you have a report showing monthly sales totals. Pipeline aggregation works like a calculator that takes these monthly totals and computes new insights, such as the average change from month to month or the running total. Instead of looking at each sale individually, it looks at the summarized data from other aggregations.

In Elasticsearch, you first run a regular aggregation to group and summarize your data, like summing sales per month. Then, a pipeline aggregation takes that summarized data as input and performs further calculations on it. This two-step process helps you analyze trends, compare results, or detect patterns without extra queries.

Example

This example shows how to calculate a moving average of monthly sales using a pipeline aggregation in Elasticsearch.

json

{
  "size": 0,
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "monthly_sales": {
          "sum": {
            "field": "sales"
          }
        },
        "moving_avg_sales": {
          "moving_avg": {
            "buckets_path": "monthly_sales",
            "window": 3
          }
        }
      }
    }
  }
}

Output

{
  "aggregations": {
    "sales_over_time": {
      "buckets": [
        {
          "key_as_string": "2024-01-01T00:00:00.000Z",
          "monthly_sales": { "value": 1000 },
          "moving_avg_sales": { "value": null }
        },
        {
          "key_as_string": "2024-02-01T00:00:00.000Z",
          "monthly_sales": { "value": 1200 },
          "moving_avg_sales": { "value": null }
        },
        {
          "key_as_string": "2024-03-01T00:00:00.000Z",
          "monthly_sales": { "value": 1100 },
          "moving_avg_sales": { "value": 1100 }
        },
        {
          "key_as_string": "2024-04-01T00:00:00.000Z",
          "monthly_sales": { "value": 1300 },
          "moving_avg_sales": { "value": 1200 }
        }
      ]
    }
  }
}

When to Use

Use pipeline aggregations when you want to analyze or transform aggregated data further. For example, if you want to see trends like moving averages, percent changes, or cumulative sums over time, pipeline aggregations are perfect.

Real-world uses include monitoring sales trends, website traffic changes, or stock price movements where you first aggregate raw data and then calculate metrics on those results to understand patterns or anomalies.

Key Points

• Pipeline aggregations work on the output of other aggregations, not raw documents.
• They help calculate metrics like moving averages, derivatives, and cumulative sums.
• They enable advanced data analysis and trend detection in Elasticsearch.
• They require a base aggregation to provide input data.

Key Takeaways

Pipeline aggregations process results of other aggregations to analyze trends or changes.

They are useful for calculating moving averages, derivatives, and cumulative metrics.

You must have a base aggregation before applying a pipeline aggregation.

Pipeline aggregations help reveal insights from summarized data without extra queries.