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.

Prerequisites

To use DiskANN, note that

DiskANN is enabled by default. If you prefer in-memory index over on-disk index, you are advised to disable this feature for a better performance.
- To disable it, you can change queryNode.enableDisk to false in your milvus configuration file.
- To enable it again, you can set queryNode.enableDisk to true.
The Milvus instance runs on Ubuntu 18.04.6 or a later release.
The Milvus data path should be mounted to an NVMe SSD for full performance:
- For a Milvus Standalone instance, the data path should be /var/lib/milvus/data in the container where the instance runs.
- For a Milvus Cluster instance, the data path should be /var/lib/milvus/data in the containers where the QueryNodes and IndexNodes run.

To use DiskANN, ensure that you

Use only float vectors with at least 1 dimensions in your data.
Use only Euclidean Distance (L2) or Inner Product (IP) to measure the distance between vectors.

Index building parameters

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

Parameter Description Range

search_list Size of the candidate list, a larger size offers a higher recall rate with degraded performance. [topk, int32_max]

Parameter	Description	Range
`search_list`	Size of the candidate list, a larger size offers a higher recall rate with degraded performance.	[topk, int32_max]

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

...
DiskIndex:
  MaxDegree: 56
  SearchListSize: 100
  PQCodeBugetGBRatio: 0.125
  SearchCacheBudgetGBRatio: 0.125
  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, int32_max]	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
`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
`BeamWidthRatio`	Ratio between the maximum number of IO requests per search iteration and CPU number.	[1, max(128 / CPU number, 16)]	4.0