Scalar Index

Milvus supports filtered searches combining both scalar and vector fields. To enhance the efficiency of searches involving scalar fields, Milvus introduced scalar field indexing starting from version 2.1.0. This article provides an overview of scalar field indexing in Milvus, helping you understand its significance and implementation.

Overview

Once conducting vector similarity searches in Milvus, you can use logical operators to organize scalar fields into boolean expressions.

When Milvus receives a search request with such a boolean expression, it parses the boolean expression into an abstract syntax tree (AST) to generate a physical plan for attribute filtering. Milvus then applies the physical plan in each segment to generate a bitset as the filtering result and includes the result as a vector search parameter to narrow down the search scope. In this case, the speed of vector searches relies heavily on the speed of attribute filtering.

Scalar field indexing is a way of ensuring the speed of attribute filtering by sorting scalar field values in a particular way to accelerate information retrieval.

Scalar field indexing algorithms

Milvus aims to achieve low memory usage, high filtering efficiency, and short loading time with its scalar field indexing algorithms. These algorithms are categorized into two main types: default indexing and inverted indexing.

Default indexing

Milvus automatically creates a default index for a scalar field based on its data type, without requiring manual intervention. This default indexing is suitable for prefix match queries and frequent retrieval scenarios.

The following table lists the data types that Milvus supports and their corresponding default indexing algorithms.

Data type	Default indexing algorithm
VARCHAR	MARISA-trie
INT8	STL sort
INT16	STL sort
INT32	STL sort
INT64	STL sort
FLOAT	STL sort
DOUBLE	STL sort

Inverted indexing

Inverted indexing is a more flexible approach where you manually create an inverted index for a scalar field by specifying index parameters. This method is suitable for various scenarios, including point queries, pattern match queries, full-text search, JSON search, Boolean search, and even prefix match queries.

The inverted index consists of a term dictionary and an inverted list. The term dictionary contains all tokenized words sorted alphabetically, while the inverted list contains the list of documents where each word appears. This structure makes operations like point queries and range queries highly efficient by reducing the time complexity compared to brute-force searches.

The advantages of using an inverted index are particularly evident in the following operations:

• Point query: For example, when searching for documents containing the word Milvus, the process begins by checking if Milvus is present in the term dictionary. If it is not found, no documents contain the word. However, if it is found, the inverted list associated with Milvus is retrieved, indicating the documents that contain the word. This method is far more efficient than a brute-force search through a million documents, as the sorted term dictionary significantly reduces the time complexity of finding the word Milvus.
• Range query: The efficiency of range queries, such as finding documents with words alphabetically greater than very, is also enhanced by the sorted term dictionary. This approach is more efficient than a brute-force search, providing quicker and more accurate results.

Performance recommandations

To take full advantage of Milvus' capability in scalar field indexing and unleash its power in vector similarity searches, you may need a model to estimate the size of memory required based on the data you have.

The following tables list the estimation functions for all the data types that Milvus supports.

• Numeric fields

  Data type	Memory estimation function (MB)
  INT8	numOfRows * 12 / 1024 / 1024
  INT16	numOfRows * 12 / 1024 / 1024
  INT32	numOfRows * 12 / 1024 / 1024
  INT64	numOfRows * 24 / 1024 / 1024
  FLOAT32	numOfRows * 12 / 1024 / 1024
  DOUBLE	numOfRows * 24 / 1024 / 1024

• String fields

  String length	Memory estimation function (MB)
  (0, 8]	numOfRows * 128 / 1024 / 1024
  (8, 16]	numOfRows * 144 / 1024 / 1024
  (16, 32]	numOfRows * 160 / 1024 / 1024
  (32, 64]	numOfRows * 192 / 1024 / 1024
  (64, 128]	numOfRows * 256 / 1024 / 1024
  (128, 65535]	numOfRows * strLen * 1.5 / 1024 / 1024

What's next

• To index a scalar field, read Build an Index on Scalars.

• To learn more about the related terms and rules mentioned above, read

  • Bitset
  • Multi-Vector search
  • Boolean expression rules
  • Supported data types