Bases du mappage et des types de données

When you put data into Elasticsearch, it needs to understand what kind of data each piece is. This is where mapping comes in.

Mapping is like a schema that defines the fields in your documents and their data types, such as text, numbers, or dates.

Think of mapping as a blueprint for your search engine. It tells Elasticsearch:

• How to store each field
• How to index (prepare for search) each field
• How each field can be searched and analyzed

Without proper mapping, your searches might not work as expected!

Elasticsearch is smart! If you index a document without defining a mapping first, it tries to guess the data type for each field. This is called dynamic mapping.

It's convenient for quick starts, but not always ideal for precise control over your data.

Let's see dynamic mapping in action. We'll index a document into a new index called my_books. Elasticsearch will automatically create the index and guess the field types.

After indexing, we can retrieve the automatically generated mapping for my_books to see what data types Elasticsearch inferred for each field.

You'll see types like text, keyword, long, and boolean.

While handy, dynamic mapping has limits. For example, Elasticsearch might map a city name as text (for full-text search) when you really need it as a keyword (for exact filtering).

This can lead to inefficient searches or unexpected results. That's why explicit mapping is crucial.

Explicit mapping means you define the schema yourself before indexing documents. This gives you full control over how your data is stored and indexed.

You define mappings when you create an index, inside the mappings object, under properties.

Two fundamental types are text and keyword:

• text: Used for full-text search (e.g., book descriptions). Text is analyzed, broken into words, and processed.
• keyword: Used for exact value matching, filtering, and sorting (e.g., product IDs, tags). Keywords are stored as-is.

Here’s how you'd explicitly map a product_name for search and a product_id for exact filtering.

Notice how we create the index with the mapping before adding any documents.

You're building an Elasticsearch index for an e-commerce platform. Which data types would be most appropriate for the following fields to enable efficient searching and filtering?

Great job! You've learned the basics of Elasticsearch mapping.

• Mapping defines the structure and types of your document fields.
• Dynamic mapping is Elasticsearch's auto-guessing feature.
• Explicit mapping gives you full control, letting you define types like text (for search) and keyword (for exact matches), and numeric types like integer and float.

Controlling your mappings is key to powerful and accurate search!