In-memory Index

This topic lists various types of in-memory indexes Milvus supports, scenarios each of them best suits, and parameters users can configure to achieve better search performance. For on-disk indexes, see On-disk Index.

Indexing is the process of efficiently organizing data, and it plays a major role in making similarity search useful by dramatically accelerating time-consuming queries on large datasets.

To improve query performance, you can specify an index type for each vector field.

ANNS vector indexes

Most of the vector index types supported by Milvus use approximate nearest neighbors search (ANNS) algorithms. Compared with accurate retrieval, which is usually very time-consuming, the core idea of ANNS is no longer limited to returning the most accurate result, but only searching for neighbors of the target. ANNS improves retrieval efficiency by sacrificing accuracy within an acceptable range.

According to the implementation methods, the ANNS vector index can be divided into four categories:

• Tree-based index
• Graph-based index
• Hash-based index
• Quantization-based index

Indexes supported in Milvus

According to the suited data type, the supported indexes in Milvus can be divided into two categories:

• Indexes for floating-point embeddings

  • For 128-dimensional floating-point embeddings, the storage they take up is 128 * the size of float = 512 bytes. And the distance metrics used for float-point embeddings are Euclidean distance (L2) and Inner product.

  • These types of indexes include FLAT, IVF_FLAT, IVF_PQ, IVF_SQ8, HNSW, and SCANN^(beta) for CPU-based ANN searches.

• Indexes for binary embeddings

  • For 128-dimensional binary embeddings, the storage they take up is 128 / 8 = 16 bytes. And the distance metrics used for binary embeddings are Jaccard and Hamming.

  • This type of indexes include BIN_FLAT and BIN_IVF_FLAT.

• Indexes for sparse embeddings

  • The distance metric supported for sparse embeddings is IP (Inner Product) only.

  • The types of indexes include SPARSE_INVERTED_INDEX and SPARSE_WAND.

The following table classifies the indexes that Milvus supports:

FLAT

For vector similarity search applications that require perfect accuracy and depend on relatively small (million-scale) datasets, the FLAT index is a good choice. FLAT does not compress vectors, and is the only index that can guarantee exact search results. Results from FLAT can also be used as a point of comparison for results produced by other indexes that have less than 100% recall.

FLAT is accurate because it takes an exhaustive approach to search, which means for each query the target input is compared to every set of vectors in a dataset. This makes FLAT the slowest index on our list, and poorly suited for querying massive vector data. There are no parameters required for the FLAT index in Milvus, and using it does not need data training.

• Search parameters

  Parameter	Description	Range
  metric_type	[Optional] The chosen distance metric.	See Supported Metrics.

IVF_FLAT

IVF_FLAT divides vector data into nlist cluster units, and then compares distances between the target input vector and the center of each cluster. Depending on the number of clusters the system is set to query (nprobe), similarity search results are returned based on comparisons between the target input and the vectors in the most similar cluster(s) only — drastically reducing query time.

By adjusting nprobe, an ideal balance between accuracy and speed can be found for a given scenario. Results from the IVF_FLAT performance test demonstrate that query time increases sharply as both the number of target input vectors (nq), and the number of clusters to search (nprobe), increase.

IVF_FLAT is the most basic IVF index, and the encoded data stored in each unit is consistent with the original data.

• Index building parameters

  Parameter	Description	Range	Default Value
  nlist	Number of cluster units	[1, 65536]	128

• Search parameters

  • Common search

    Parameter	Description	Range	Default Value
    nprobe	Number of units to query	[1, nlist]	8

  • Range search

    Parameter	Description	Range	Default Value
    max_empty_result_buckets	Maximum number of buckets not returning any search results. This is a range-search parameter and terminates the search process whilst the number of consecutive empty buckets reaches the specified value. Increasing this value can improve recall rate at the cost of increased search time.	[1, 65535]	2

IVF_SQ8

IVF_FLAT does not perform any compression, so the index files it produces are roughly the same size as the original, raw non-indexed vector data. For example, if the original 1B SIFT dataset is 476 GB, its IVF_FLAT index files will be slightly smaller (~470 GB). Loading all the index files into memory will consume 470 GB of storage.

When disk, CPU, or GPU memory resources are limited, IVF_SQ8 is a better option than IVF_FLAT. This index type can convert each FLOAT (4 bytes) to UINT8 (1 byte) by performing Scalar Quantization (SQ). This reduces disk, CPU, and GPU memory consumption by 70–75%. For the 1B SIFT dataset, the IVF_SQ8 index files require just 140 GB of storage.

• Index building parameters

  Parameter	Description	Range
  nlist	Number of cluster units	[1, 65536]

• Search parameters

  • Common search

    Parameter	Description	Range	Default Value
    nprobe	Number of units to query	[1, nlist]	8

  • Range search

    Parameter	Description	Range	Default Value
    max_empty_result_buckets	Maximum number of buckets not returning any search results. This is a range-search parameter and terminates the search process whilst the number of consecutive empty buckets reaches the specified value. Increasing this value can improve recall rate at the cost of increased search time.	[1, 65535]	2

IVF_PQ

PQ (Product Quantization) uniformly decomposes the original high-dimensional vector space into Cartesian products of m low-dimensional vector spaces, and then quantizes the decomposed low-dimensional vector spaces. Instead of calculating the distances between the target vector and the center of all the units, product quantization enables the calculation of distances between the target vector and the clustering center of each low-dimensional space and greatly reduces the time complexity and space complexity of the algorithm.

IVF_PQ performs IVF index clustering before quantizing the product of vectors. Its index file is even smaller than IVF_SQ8, but it also causes a loss of accuracy during searching vectors.

• Index building parameters

  Parameter	Description	Range
  nlist	Number of cluster units	[1, 65536]
  m	Number of factors of product quantization	dim mod m == 0
  nbits	[Optional] Number of bits in which each low-dimensional vector is stored.	[1, 16] (8 by default)

• Search parameters

  • Common search

    Parameter	Description	Range	Default Value
    nprobe	Number of units to query	[1, nlist]	8

  • Range search

    Parameter	Description	Range	Default Value
    max_empty_result_buckets	Maximum number of buckets not returning any search results. This is a range-search parameter and terminates the search process whilst the number of consecutive empty buckets reaches the specified value. Increasing this value can improve recall rate at the cost of increased search time.	[1, 65535]	2

SCANN

SCANN (Score-aware quantization loss) is similar to IVF_PQ in terms of vector clustering and product quantization. What makes them different lies in the implementation details of product quantization and the use of SIMD (Single-Instruction / Multi-data) for efficient calculation.

• Index building parameters

  Parameter	Description	Range
  nlist	Number of cluster units	[1, 65536]
  with_raw_data	Whether to include the raw data in the index	True or False. Defaults to True.

  Unlike IVF_PQ, default values apply to m and nbits for optimized performance.

• Search parameters

  • Common search

    Parameter	Description	Range	Default value
    nprobe	Number of units to query	[1, nlist]
    reorder_k	Number of candidate units to query	[top_k, ∞]

  • Range search

    Parameter	Description	Range	Default Value
    max_empty_result_buckets	Maximum number of buckets not returning any search results. This is a range-search parameter and terminates the search process whilst the number of consecutive empty buckets reaches the specified value. Increasing this value can improve recall rate at the cost of increased search time.	[1, 65535]	2

HNSW

HNSW (Hierarchical Navigable Small World Graph) is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and the distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple iterations, it can quickly approach the target position.

In order to improve performance, HNSW limits the maximum degree of nodes on each layer of the graph to M. In addition, you can use efConstruction (when building index) or ef (when searching targets) to specify a search range.

• Index building parameters

  Parameter	Description	Range
  M	Maximum degree of the node	(2, 2048)
  efConstruction	Size of the dynamic list for the nearest neighbors during the index time. Higher efConstruction leads to a may improve index quality at the cost of increased indexing time.	(1, int_max)

• Search parameters

  Parameter	Description	Range
  ef	Size of the dynamic list for the nearest neighbors during the search time. Higher ef leads to more accurate but slower search.	[1, int_max]

BIN_FLAT

This index is exactly the same as FLAT except that this can only be used for binary embeddings.

For vector similarity search applications that require perfect accuracy and depend on relatively small (million-scale) datasets, the BIN_FLAT index is a good choice. BIN_FLAT does not compress vectors, and is the only index that can guarantee exact search results. Results from BIN_FLAT can also be used as a point of comparison for results produced by other indexes that have less than 100% recall.

BIN_FLAT is accurate because it takes an exhaustive approach to search, which means for each query the target input is compared to vectors in a dataset. This makes BIN_FLAT the slowest index on our list, and poorly suited for querying massive vector data. There are no parameters for the BIN_FLAT index in Milvus, and using it does not require data training or additional storage.

• Search parameters

  Parameter	Description	Range
  metric_type	[Optional] The chosen distance metric.	See Supported Metrics.

BIN_IVF_FLAT

This index is exactly the same as IVF_FLAT except that this can only be used for binary embeddings.

BIN_IVF_FLAT divides vector data into nlist cluster units, and then compares distances between the target input vector and the center of each cluster. Depending on the number of clusters the system is set to query (nprobe), similarity search results are returned based on comparisons between the target input and the vectors in the most similar cluster(s) only — drastically reducing query time.

By adjusting nprobe, an ideal balance between accuracy and speed can be found for a given scenario. Query time increases sharply as both the number of target input vectors (nq), and the number of clusters to search (nprobe), increase.

BIN_IVF_FLAT is the most basic BIN_IVF index, and the encoded data stored in each unit is consistent with the original data.

• Index building parameters

  Parameter	Description	Range
  nlist	Number of cluster units	[1, 65536]

• Search parameters

  • Common search

    Parameter	Description	Range	Default Value
    nprobe	Number of units to query	[1, nlist]	8

  • Range search

    Parameter	Description	Range	Default Value
    max_empty_result_buckets	Maximum number of buckets not returning any search results. This is a range-search parameter and terminates the search process whilst the number of consecutive empty buckets reaches the specified value. Increasing this value can improve recall rate at the cost of increased search time.	[1, 65535]	2

SPARSE_INVERTED_INDEX

Each dimension maintains a list of vectors that have a non-zero value at that dimension. During search, Milvus iterates through each dimension of the query vector and computes scores for vectors that have non-zero values in those dimensions.

• Index building parameters

  Parameter	Description	Range
  drop_ratio_build	The proportion of small vector values that are excluded during the indexing process. This option allows fine-tuning of the indexing process, making a trade-off between efficiency and accuracy by disregarding small values when building the index.	[0, 1]

• Search parameters

  Parameter	Description	Range
  drop_ratio_search	The proportion of small vector values that are excluded during the search process. This option allows fine-tuning of the search process by specifying the ratio of the smallest values in the query vector to ignore. It helps balance search precision and performance. The smaller the value set for drop_ratio_search, the less these small values contribute to the final score. By ignoring some small values, search performance can be improved with minimal impact on accuracy.	[0, 1]

SPARSE_WAND

This index shares similarities with SPARSE_INVERTED_INDEX, while it utilizes the Weak-AND algorithm to further reduce the number of full IP distance evaluations during the search process.

Based on our testing, SPARSE_WAND generally outperforms other methods in terms of speed. However, its performance can deteriorate rapidly as the density of the vectors increases. To address this issue, introducing a non-zero drop_ratio_search can significantly enhance performance while only incurring minimal accuracy loss. For more information, refer to Sparse Vector.

• Index building parameters

  Parameter	Description	Range
  drop_ratio_build	The proportion of small vector values that are excluded during the indexing process. This option allows fine-tuning of the indexing process, making a trade-off between efficiency and accuracy by disregarding small values when building the index.	[0, 1]

• Search parameters

  Parameter	Description	Range
  drop_ratio_search	The proportion of small vector values that are excluded during the search process. This option allows fine-tuning of the search process by specifying the ratio of the smallest values in the query vector to ignore. It helps balance search precision and performance. The smaller the value set for drop_ratio_search, the less these small values contribute to the final score. By ignoring some small values, search performance can be improved with minimal impact on accuracy.	[0, 1]

FAQ

What's next

• Learn more about the Similarity Metrics supported in Milvus.