Pipeline Aggregations | Aggregations | Elasticsearch Tutorial

Introduction

Pipeline aggregations in Elasticsearch allow you to perform complex calculations on the aggregated data returned by other aggregations. They are a powerful tool for deriving insights from your data by applying transformations, moving averages, and other statistical calculations.

Types of Pipeline Aggregations

There are several types of pipeline aggregations available in Elasticsearch:

Derivative Aggregation
Max Bucket Aggregation
Min Bucket Aggregation
Avg Bucket Aggregation
Sum Bucket Aggregation
Stats Bucket Aggregation
Extended Stats Bucket Aggregation
Percentiles Bucket Aggregation
Moving Average Aggregation
Cumulative Sum Aggregation

Example: Derivative Aggregation

The Derivative Aggregation calculates the derivative of a specified metric in a parent histogram (or date histogram) aggregation.

Consider the following example where we use a date histogram aggregation on a field called "date" and then apply a derivative aggregation on the "sales" metric:

{
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "sales"
          }
        },
        "sales_derivative": {
          "derivative": {
            "buckets_path": "sales"
          }
        }
      }
    }
  }
}

Example: Moving Average Aggregation

The Moving Average Aggregation computes the moving average of a specified metric in a parent histogram (or date histogram) aggregation.

Here is an example where we apply a moving average aggregation on the "sales" metric:

{
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "sales"
          }
        },
        "sales_moving_avg": {
          "moving_avg": {
            "buckets_path": "sales"
          }
        }
      }
    }
  }
}

Example: Cumulative Sum Aggregation

The Cumulative Sum Aggregation calculates the cumulative sum of a specified metric in a parent histogram (or date histogram) aggregation.

Below is an example where we apply a cumulative sum aggregation on the "sales" metric:

{
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "date",
        "calendar_interval": "month"
      },
      "aggs": {
        "sales": {
          "sum": {
            "field": "sales"
          }
        },
        "cumulative_sales": {
          "cumulative_sum": {
            "buckets_path": "sales"
          }
        }
      }
    }
  }
}

Visualization and Interpretation

Once you have applied pipeline aggregations, it's crucial to visualize and interpret the results correctly. Tools like Kibana can help in visualizing the results of these aggregations and deriving insights from them.

For instance, the derivative aggregation can help in identifying trends over time, while the moving average aggregation can smooth out fluctuations to show the overall trend.

Conclusion

Pipeline aggregations are a powerful feature in Elasticsearch that allow for advanced data analysis and manipulation. By understanding and utilizing these aggregations, you can perform complex calculations and derive meaningful insights from your data.

Pipeline Aggregations in Elasticsearch