On-disk Index

This article introduces an on-disk indexing algorithm named DiskANN. Based on Vamana graphs, DiskANN powers efficient searches within large datasets.

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

Currently, a vector field only supports one index type. Milvus automatically deletes the old index when switching the index type.


To use DiskANN, note that

  • You have run make disk_index=ON when you compile Milvus from the source.
  • Your Milvus instance runs on Ubuntu 18.04.6 or a later release.
  • The path ${MILVUS_ROOT_PATH}/milvus/data has been mounted to an NVMe SSD for full performance.


To use DiskANN, ensure that you

  • Use only float vectors with at least 32 dimensions in your data.
  • Use only Euclidean Distance (L2) or Inner Product (IP) to measure the distance between vectors.

Index and search settings

  • Index building parameters

    When building a DiskANN index, use DISKANN as the index type. No index parameters are necessary.

  • Search parameters

    Parameter Description Range
    k Number of closest vectors to return [1, 12768]
    search_list Size of the candidate list, a larger size offers a higher recall rate with degraded performance. [k, min( 10 * k, 65535)] for k > 20
    [k, 200] for k <= 20

DiskANN is tunable. You can modify DiskANN-related parameters in ${MILVUS_ROOT_PATH}/configs/milvus.yaml to improve its performance.

  MaxDegree: 56
  SearchListSize: 100
  PQCodeBugetGBRatio: 0.125
  BuildNumThreadsRatio: 1.0
  SearchCacheBudgetGBRatio: 0.125
  LoadNumThreadRatio: 8.0
  BeamWidthRatio: 4.0
Parameter Description Value Range Default Value
MaxDegree Maximum degree of the Vamana graph.
A larger value offers a higher recall rate but increases the size of and time to build the index.
[1, 512] 56
SearchListSize Size of the candidate list.
A larger value increases the time spent on building the index but offers a higher recall rate.
Set it to a value smaller than MaxDegree unless you need to reduce the index-building time.
[1, ∞] 100
PQCodeBugetGBRatio Size limit on the PQ code.
A larger value offers a higher recall rate but increases memory usage.
(0.0, 0.25] 0.125
BuildNumThreadsRatio Ratio between the number of threads used to build the index and the number of CPUs. [1.0, 128.0 / CPU number] 1.0
SearchCacheBudgetGBRatio Ratio of cached node numbers to raw data.
A larger value improves index-building performance with increased memory usage.
[0.0, 0.3) 0.10
LoadNumThreadRatio Ratio between the number of threads used to load index/search and the number of CPUs. For details, refer to the first item in References and Facts. [1, 65536 / 32 / CPU number] 8.0
BeamWidthRatio Ratio between the maximum number of IO requests per search iteration and CPU number. [1, max(128 / CPU number, 16)] 4.0
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