INVERTED

When you need to perform frequent filter queries on your data, INVERTED indexes can dramatically improve query performance. Instead of scanning through all documents, Milvus uses inverted indexes to quickly locate the exact records that match your filter conditions.

When to use INVERTED indexes

Use INVERTED indexes when you need to:

• Filter by specific values: Find all records where a field equals a specific value (e.g., category == "electronics")

• Filter text content: Perform efficient searches on VARCHAR fields

• Query JSON field values: Filter on specific keys within JSON structures

Performance benefit: INVERTED indexes can reduce query time from seconds to milliseconds on large datasets by eliminating the need for full collection scans.

How INVERTED indexes work

Milvus uses Tantivy to implement inverted indexing. Here’s the process:

1. Tokenization: Milvus breaks down your data into searchable terms

2. Term dictionary: Creates a sorted list of all unique terms

3. Inverted lists: Maps each term to the documents containing it

For example, given these two sentences:

• “Milvus is a cloud-native vector database”

• “Milvus is very good at performance”

The inverted index maps terms like “Milvus” → [Document 0, Document 1], “cloud-native” → [Document 0], and “performance” → [Document 1].

Inverted Index

When you filter by a term, Milvus looks up the term in the dictionary and instantly retrieves all matching documents.

INVERTED indexes support all scalar field types: BOOL, INT8, INT16, INT32, INT64, FLOAT, DOUBLE, VARCHAR, JSON, and ARRAY. However, the index parameters for indexing a JSON field are slightly different from regular scalar fields.

Create indexes on non-JSON fields

To create an index on a non-JSON field, follow these steps:

1. Prepare your index parameters:

   from pymilvus import MilvusClient
   
   client = MilvusClient(uri="http://localhost:19530") # Replace with your server address
   
   # Create an empty index parameter object
   index_params = client.prepare_index_params()
   

2. Add the INVERTED index:

   index_params.add_index(
       field_name="category",           # Name of the field to index
       index_type="INVERTED",          # Specify INVERTED index type
       index_name="category_index"     # Give your index a name
   )
   

3. Create the index:

   client.create_index(
       collection_name="my_collection", # Replace with your collection name
       index_params=index_params
   )
   

Create indexes on JSON fieldsCompatible with Milvus 2.5.11+

You can also create INVERTED indexes on specific paths within JSON fields. This requires additional parameters to specify the JSON path and data type:

# Build index params
index_params.add_index(
    field_name="metadata",                    # JSON field name
    index_type="INVERTED",
    index_name="metadata_category_index",
    params={
        "json_path": "metadata[\"category\"]",    # Path to the JSON key
        "json_cast_type": "varchar"              # Data type to cast to during indexing
    }
)

# Create index
client.create_index(
    collection_name="my_collection", # Replace with your collection name
    index_params=index_params
)

For detailed information about JSON field indexing, including supported paths, data types, and limitations, refer to JSON Field.

Best practices

• Create indexes after loading data: Build indexes on collections that already contain data for better performance

• Use descriptive index names: Choose names that clearly indicate the field and purpose

• Monitor index performance: Check query performance before and after creating indexes

• Consider your query patterns: Create indexes on fields you frequently filter by

Next steps

• Learn about other index types

• See JSON field indexing for advanced JSON indexing scenarios