Creating an index with mapping is necessary when you want to define the structure of your documents before indexing data in Elasticsearch. This task is particularly important when:
- You need to specify data types for fields
- You want to control how fields are analyzed for search
- You need to set up custom analyzers or field-specific settings
- You want to optimize your index for specific use cases or query patterns
Steps to create an index with mapping
Prepare your mapping definition:
{ "mappings": { "properties": { "field1": { "type": "text" }, "field2": { "type": "keyword" }, "field3": { "type": "date" } } } }Send a PUT request to Elasticsearch to create the index:
PUT /your_index_name { "mappings": { "properties": { "field1": { "type": "text" }, "field2": { "type": "keyword" }, "field3": { "type": "date" } } } }Verify the index creation:
GET /your_index_name
Additional information and best practices
- Choose appropriate field types based on your data and search requirements
- Use dynamic mapping cautiously and consider disabling it for production indices
- Utilize multi-fields when you need different analyzers for the same field
- Consider using index templates for consistent mapping across multiple indices
- Be aware that changing existing field mappings often requires reindexing
Frequently Asked Questions
Q: Can I modify an existing index mapping?
A: You can add new fields to an existing mapping, but you cannot modify the mapping of existing fields. To change existing fields, you need to create a new index with the updated mapping and reindex your data.
Q: What's the difference between 'text' and 'keyword' field types?
A: 'Text' fields are analyzed and are suitable for full-text search. 'Keyword' fields are not analyzed and are better for exact matches, sorting, and aggregations.
Q: How do I set up custom analyzers in my mapping?
A: You can define custom analyzers in the index settings and then reference them in your field mappings. For example:
{
"settings": {
"analysis": {
"analyzer": {
"my_custom_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "asciifolding"]
}
}
}
},
"mappings": {
"properties": {
"my_field": {
"type": "text",
"analyzer": "my_custom_analyzer"
}
}
}
}
Q: What is the purpose of the '_source' field in mappings?
A: The '_source' field contains the original JSON document that was indexed. You can disable it to save space, but this will prevent you from retrieving the original document or performing reindexing without the source data.
Q: How can I optimize my mapping for better search performance?
A: To optimize for search performance, consider using appropriate field types, disable unnecessary features (like '_all' field), use doc values for fields used in sorting and aggregations, and apply index-time analysis to reduce query-time processing.
