Example Dataset
Let’s consider an Elasticsearch index called products with documents like this:
{
"product_id": 1,
"name": "Laptop",
"category": "electronics",
"price": 1000,
"quantity_sold": 5
},
{
"product_id": 2,
"name": "T-shirt",
"category": "clothing",
"quantity_sold": 20
},
{
"product_id": 3,
"name": "Book",
"category": "books",
"price": 15
}
In this dataset, the second product (T-shirt) is missing the price field.
Missing Aggregation in Elasticsearch
Elasticsearch is a powerful tool for full-text search and data analytics, and one of its core features is the aggregation framework. Aggregations allow you to summarize and analyze your data flexibly and efficiently.
Among the various types of aggregations available, the “missing” aggregation is particularly useful for dealing with incomplete data. This guide will explain what missing aggregation is, how it works, and provide detailed examples to help you understand its usage.
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