The k-NN `stats` API provides information about the current status of the k-NN plugin. The plugin keeps track of both cluster-level and node-level statistics. Cluster-level statistics have a single value for the entire cluster. Node-level statistics have a single value for each node in the cluster. You can filter the query by `nodeId` and `statName`:
`total_load_time` | The time in nanoseconds that k-NN has taken to load native library indices into the cache. This statistic is only relevant to approximate k-NN search.
`eviction_count` | The number of native library indices that have been evicted from the cache due to memory constraints or idle time. This statistic is only relevant to approximate k-NN search. <br/>**Note**: Explicit evictions that occur because of index deletion aren't counted.
`hit_count` | The number of cache hits. A cache hit occurs when a user queries a native library index that's already loaded into memory. This statistic is only relevant to approximate k-NN search.
`miss_count` | The number of cache misses. A cache miss occurs when a user queries a native library index that isn't loaded into memory yet. This statistic is only relevant to approximate k-NN search.
`graph_memory_usage` | The amount of native memory native library indices are using on the node in kilobytes.
`graph_memory_usage_percentage` | The amount of native memory native library indices are using on the node as a percentage of the maximum cache capacity.
`graph_index_requests` | The number of requests to add the `knn_vector` field of a document into a native library index.
`graph_index_errors` | The number of requests to add the `knn_vector` field of a document into a native library index that have produced an error.
`graph_query_requests` | The number of native library index queries that have been made.
`graph_query_errors` | The number of native library index queries that have produced an error.
`load_success_count` | The number of times k-NN successfully loaded a native library index into the cache. This statistic is only relevant to approximate k-NN search.
`load_exception_count` | The number of times an exception occurred when trying to load a native library index into the cache. This statistic is only relevant to approximate k-NN search.
`indices_in_cache` | For each OpenSearch index with a `knn_vector` field and approximate k-NN turned on, this statistic provides the number of native library indices that OpenSearch index has and the total `graph_memory_usage` that the OpenSearch index is using, in kilobytes.
`script_compilations` | The number of times the k-NN script has been compiled. This value should usually be 1 or 0, but if the cache containing the compiled scripts is filled, the k-NN script might be recompiled. This statistic is only relevant to k-NN score script search.
`nmslib_initialized` | Boolean value indicating whether the *nmslib* JNI library has been loaded and initialized on the node.
`faiss_initialized` | Boolean value indicating whether the *faiss* JNI library has been loaded and initialized on the node.
`model_index_status` | Status of model system index. Valid values are "red", "yellow", "green". If the index does not exist, this will be null.
`indexing_from_model_degraded` | Boolean value indicating if indexing from a model is degraded. This will happen if there is not enough JVM memory to cache the models.
`training_requests` | The number of training requests made to the node.
`training_errors` | The number of training errors that have occurred on the node.
`training_memory_usage` | The amount of native memory training is using on the node in kilobytes.
`training_memory_usage_percentage` | The amount of native memory training is using on the node as a percentage of the maximum cache capacity.
**Note**: Some stats contain *graph* in the name. In these cases, *graph* is synonymous with *native library index*. The term *graph* is a legacy detail, coming from when the plugin only supported the HNSW algorithm, which consists of hierarchical graphs.
The native library indices used to perform approximate k-Nearest Neighbor (k-NN) search are stored as special files with other Apache Lucene segment files. In order for you to perform a search on these indices using the k-NN plugin, the plugin needs to load these files into native memory.
If the plugin hasn't loaded the files into native memory, it loads them when it receives a search request. The loading time can cause high latency during initial queries. To avoid this situation, users often run random queries during a warmup period. After this warmup period, the files are loaded into native memory and their production workloads can begin. This loading process is indirect and requires extra effort.
As an alternative, you can avoid this latency issue by running the k-NN plugin warmup API operation on whatever indices you're interested in searching. This operation loads all the native library files for all of the shards (primaries and replicas) of all the indices specified in the request into native memory.
After the process finishes, you can start searching against the indices with no initial latency penalties. The warmup API operation is idempotent, so if a segment's native library files are already loaded into memory, this operation has no impact. It only loads files that aren't currently in memory.
`total` indicates how many shards the k-NN plugin attempted to warm up. The response also includes the number of shards the plugin succeeded and failed to warm up.
The call doesn't return results until the warmup operation finishes or the request times out. If the request times out, the operation still continues on the cluster. To monitor the warmup operation, use the OpenSearch `_tasks` API:
* Don't run merge operations on indices that you want to warm up. During merge, the k-NN plugin creates new segments, and old segments are sometimes deleted. For example, you could encounter a situation in which the warmup API operation loads native library indices A and B into native memory, but segment C is created from segments A and B being merged. The native library indices for A and B would no longer be in memory, and native library index C would also not be in memory. In this case, the initial penalty for loading native library index C is still present.
* Confirm that all native library indices you want to warm up can fit into native memory. For more information about the native memory limit, see the [knn.memory.circuit_breaker.limit statistic]({{site.url}}{{site.baseurl}}/search-plugins/knn/settings#cluster-settings). High graph memory usage causes cache thrashing, which can lead to operations constantly failing and attempting to run again.
* Don't index any documents that you want to load into the cache. Writing new information to segments prevents the warmup API operation from loading the native library indices until they're searchable. This means that you would have to run the warmup operation again after indexing finishes.
`model_id` | (Optional) The id of the fetched model. If not specified, a random id will be generated.
`node_id` | (Optional) Preferred node to execute training. If set, this node will be used to perform training if it is deemed to be capable.
Request Parameter | Description
:--- | :---
`training_index` | Index from where training data from.
`training_field` | `knn_vector` field from `training_index` to grab training data from. Dimension of this field must match `dimension` passed in to this request.
`dimension` | Dimension this model is for.
`max_training_vector_count` | (Optional) Maximum number of vectors from the training index to use for training. Defaults to all of the vectors in the index.
`search_size` | (Optional) Training data is pulled from the training index with scroll queries. Defines the number of results to return per scroll query. Defaults to 10,000.
`description` | (Optional) User provided description of the model.
`method` | Configuration of ANN method used for search. For more information on possible methods, refer to the [method documentation]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index#method-definitions). Method must require training to be valid.
