Types of Metric Aggregations
Elasticsearch offers several types of metric aggregations, each serving a different purpose:
- Average Aggregation: Calculates the average of numeric values.
- Sum Aggregation: Computes the sum of numeric values.
- Min Aggregation: Finds the minimum value.
- Max Aggregation: Finds the maximum value.
- Stats Aggregation: Provides a summary of statistics (count, min, max, sum, and average).
- Extended Stats Aggregation: Includes additional statistics such as variance, standard deviation, and sum of squares.
- Value Count Aggregation: Counts the number of values.
- Percentiles Aggregation: Calculates percentiles over numeric values.
- Percentile Ranks Aggregation: Computes the percentile rank of specific values.
- Cardinality Aggregation: Estimates the count of distinct values.
- Geo Bounds Aggregation: Computes the bounding box containing all geo-points in the field.
Example Dataset
To make the explanations concrete, let’s assume we have an Elasticsearch index called products with documents that look like this:
{
"product_id": 1,
"name": "Laptop",
"category": "electronics",
"price": 1000,
"quantity_sold": 5,
"rating": 4.5
}
Metric Aggregation in Elasticsearch
Elasticsearch is a powerful tool not just for search but also for performing complex data analytics. Metric aggregations are a crucial aspect of this capability, allowing users to compute metrics like averages, sums, and more on numeric fields within their data.
This guide will delve into metric aggregations in Elasticsearch, explaining what they are, how they work, and providing detailed examples to illustrate their use.