In Elasticsearch, the keyword data type is a fundamental concept that plays a crucial role in data indexing and searching. It is often used to represent structured or unstructured text fields that are not analyzed. Let’s dive deeper into understanding what the keyword data type is and how it can be leveraged in Elasticsearch.
What is a Keyword Data Type?
The keyword data type in Elasticsearch represents exact values as they are without any analysis. It is primarily used for fields that require exact matching and aggregation, such as IDs, email addresses, zip codes, or any other values that should not be broken down into individual terms.
To define a field as a keyword data type in Elasticsearch, you can specify the mapping explicitly or rely on the dynamic mapping feature to automatically identify keyword fields based on their characteristics.
Use Cases of the Keyword Data Type
The keyword data type finds its applications in various scenarios where exact matching and aggregations are essential. Some common use cases include:
- Identifiers: Unique identifiers like user IDs, product IDs, or document IDs are typically represented as keyword fields since they need to be precise for retrieval and filtering operations.
- Categorical Fields: Fields representing categories like product categories, tags, or states can benefit from being assigned the keyword data type. This allows efficient filtering and aggregation based on these categorical values.
- Analyzed vs. Not Analyzed Text: While text fields are usually analyzed by default to enable full-text search capabilities, there are cases where you may want to store and search text as-is without any analysis. In such cases, using the keyword data type ensures that the text is treated as a single value.
Mapping a Field as Keyword
There are two ways to map a field as a keyword data type:
Explicit Mapping
To explicitly define a field as a keyword data type, you can specify the mapping during index creation or update:
POST my-index
{
"mappings": {
"properties": {
"my_field": {
"type": "keyword"
}
}
}
}
Dynamic Mapping
If you don’t explicitly define the mapping, Elasticsearch will infer the field type based on its content. Fields with values that contain only non-analyzed strings (without any whitespace) will be automatically mapped as keyword fields.
Searching Keyword Fields
When it comes to searching keyword fields, you can use various query types such as term queries, match queries, or wildcard queries, depending on your requirements. These queries treat the search terms or phrases as exact values to find matches in the keyword fields.
It’s important to note that searching in keyword fields is case-sensitive by default. If you need case-insensitive search, you can leverage analyzers or lowercase normalizers to preprocess the search terms.
Conclusion
The keyword data type in Elasticsearch is a powerful tool for representing exact values without any analysis. It is widely used for fields that require precise matching and efficient aggregations. By understanding how to define and search keyword fields, you can effectively leverage this data type to enhance your Elasticsearch applications.