Elasticsearch is a powerful and versatile open-source search and analytics engine. It falls under the category of NoSQL databases and is specifically designed to handle large volumes of data in near real-time. In this article, we will explore the different types of data that Elasticsearch can store and how it organizes and retrieves this data efficiently.
Structured Data
Elasticsearch can store structured data, which is organized into fields with specific data types. These fields can contain various types of information such as numbers, strings, dates, boolean values, and more. By defining the mapping for each field, Elasticsearch ensures that the data is stored correctly and can be searched or filtered efficiently.
Example:
PUT /my_index
{
“mappings”: {
“properties”: {
“title”: { “type”: “text” },
“price”: { “type”: “float” },
“date_published”: { “type”: “date” }
}
}
}
In the above example, we define an index called “my_index” with three fields: “title”, “price”, and “date_published”. The “title” field has a data type of text, which means it can store textual data.
The “price” field has a float data type to store decimal values. The “date_published” field uses the date data type to store date values.
Unstructured Data
In addition to structured data, Elasticsearch is also capable of storing unstructured or semi-structured data. This type of data does not conform to a predefined schema and can vary in its format. Elasticsearch handles this flexibility by indexing the data in a way that allows for efficient search and retrieval.
PUT /my_index/_doc/1
{
“message”: “Elasticsearch is amazing!”
}
In the above example, we index a document containing an “amazing” message. The field name “message” is not predefined, allowing us to store any textual content without prior schema definition.
Nested Data
Elasticsearch also supports nested data structures, which are useful when dealing with hierarchical or nested objects. This feature allows us to index and query complex data structures within Elasticsearch efficiently.
PUT /my_index
{
“mappings”: {
“properties”: {
“author”: {
“type”: “nested“,
& nbsp; & nbsp; & nbsp; & nbsp; & nbsp; & nbsp; & nbsp; “properties”: {
n bsp; & nbsp; & nbsp; & nbsp; “name”: { “type”: “text” },
& nbsp; & nbsp; & nbsp; “age”: { “type”: “integer” }
& nbsp; & nbsp; }
}
}
}
In the above example, we define an index called “my_index” with a nested field called “author”. The “author” field contains two properties: “name” and “age”. This structure allows us to store and query information about authors with their names and ages.
Conclusion
Elasticsearch is a versatile database that can handle various types of data. Whether it’s structured, unstructured, or nested data, Elasticsearch provides efficient indexing and querying capabilities. By understanding the different types of data that Elasticsearch can store, you can make the most out of this powerful search and analytics engine.