The standard k-NN query and custom scoring option perform differently.
Approximate nearest neighbor versus score script For index-level settings, see Index settings. The default parameters work on a broader set of use cases, but make sure to run your own experiments on your data sets and choose the appropriate values. That being said, it’s important to understand your system’s requirements for latency and accuracy, and then choose the number of segments you want your index to have based on experimentation. Choosing larger values for algorithm parameters should help solve this issue but sacrifices search latency and indexing time. The larger the native library index, the more chances of losing recall if you’re using smaller algorithm parameters. Searching over a large number of small segments and aggregating the results leads to better recall than searching over a small number of large segments and aggregating results. Recall depends on multiple factors like number of vectors, number of dimensions, segments, and so on. Note: This is an expert-level setting that requires closing the index before updating the setting and reopening it after the update.
Spike performance and tuning update#
To change the default list of extensions set by the plugin, update the .extensions setting at the cluster level using the Cluster Settings API. The mmap file I/O uses the system file cache rather than memory allocated for the Java heap, so no additional allocation is required. For more information about these file extensions, see the Lucene documentation. The two file extensions specific to vector values that use mmap are. This leads to fast file I/O operations and improves the overall performance of both data ingestion and search. Starting with version 2.5, k-NN enables mmap file I/O by default when the store type is hybridfs (the default store type in OpenSearch). In versions 2.4 and earlier of the Lucene-based approximate k-NN search, read/write operations were based on Java NIO by default, which can be slow, depending on the Lucene version and number of segments per shard. Instead, the plugin relies on the existing caching mechanism in OpenSearch core. If your use case is simply to read the IDs and scores of the nearest neighbors, you can disable reading stored fields, which saves time retrieving the vectors from stored fields.įor the Lucene-based approximate k-NN search, there is no dedicated cache layer that speeds up read/write operations.
If a merge or refresh operation finishes after the API runs, or if you add new documents, you need to rerun the API to load those native library indexes into memory. Note: This API operation only loads the segments of the indexes it sees into the cache. The warmup API operation loads all native library indexes for all shards (primary and replica) for the specified indexes into the cache, so there’s no penalty to load native library indexes during initial searches. To learn more about how and for what purposes Amazon uses personal information (such as Amazon Store order history), please visit our Privacy Notice.PUT //_settings
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