How to use Missing Aggregation In Databases
To use missing aggregation, you need to specify the field you want to check for missing values. Here is a step-by-step guide on how to do this.
Step 1: Indexing the Data
First, make sure you have indexed your data in Elasticsearch. If you haven’t done so already, you can use the following command to index the example dataset:
POST /products/_bulk
{ "index": { "_id": 1 } }
{ "product_id": 1, "name": "Laptop", "category": "electronics", "price": 1000, "quantity_sold": 5 }
{ "index": { "_id": 2 } }
{ "product_id": 2, "name": "T-shirt", "category": "clothing", "quantity_sold": 20 }
{ "index": { "_id": 3 } }
{ "product_id": 3, "name": "Book", "category": "books", "price": 15 }
Step 2: Running the Missing Aggregation Query
Now, let’s run a missing aggregation query to find out how many products are missing the price field.
Query
GET /products/_search
{
"size": 0,
"aggs": {
"missing_price": {
"missing": {
"field": "price"
}
}
}
}
Output
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 3,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"missing_price": {
"doc_count": 1
}
}
}
In this example, the aggregation named missing_price shows that there is 1 document (product) 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.