The concurrent option controls intra-query parallelism, which MongoDB Vector Search enables by default. In this mode, MongoDB Vector Search utilizes more resources, but improves each individual query latency. You can set concurrent to false to run a query single-threaded.
concurrent 选项限制
When you run queries with the concurrent option, MongoDB Vector Search doesn't guarantee that each query will be executed concurrently. For example, when too many concurrent queries are queued, MongoDB Vector Search might fallback to single-threaded execution.
语法
concurrent 通过以下语法实现：
{ "$vectorSearch": { "index": "<index name>", "path": "<field-to-search>", "queryVector": [<array-of-numbers>], "numCandidates": <number-of-candidates>, "limit": <number-of-results>, "concurrent": true | false, ... } }
行为
The concurrent boolean option controls whether MongoDB Vector Search parallelizes query execution across segments, which, in many cases, improves the response time. You can set one of the following values for the concurrent option:
true- to request MongoDB Vector Search to run the query multi-threaded (default)false- to run the query single-threaded
If you omit the concurrent option, MongoDB Vector Search runs the query multi-threaded.
MongoDB Vector Search provides you control over this behavior on a per-query basis so that you can disable concurrent execution for light queries, which minimizes contention and improves overall query throughput. Concurrent execution is especially efficient on large datasets with many segments, and for heavy queries that use large numCandidates values.
Consider setting concurrent to false if your workload has a high query rate that saturates CPU. For these workloads, disabling intra-query parallelism can increase peak throughput and reduce tail latency.
例子
Consider the following query against the sample_mflix.embedded_movies collection in the sample data. The query disables concurrent search for movies with plots similar to a fun and adventurous road trip.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_4_large", 6 "queryVector": [-0.023570144549012184, -0.031474415212869644, 0.02209206111729145, -0.0032499704975634813, -0.025738606229424477, -0.052831362932920456, 0.01666576974093914, 0.01732834056019783, 0.013391619548201561, 0.009882909245789051, 0.020283298566937447, 0.0476372130215168, -0.018276246264576912, -0.0017313831485807896, 0.017947381362318993, -0.021153826266527176, -0.013988898135721684, -0.038661107420921326, 0.004928154405206442, -0.0006154148140922189, 0.02795361541211605, 0.017923804000020027, 0.0036973250098526478, 0.006466086953878403, -0.007650971878319979, 0.02559351734817028, -0.013176405802369118, -0.016704462468624115, 0.02807452157139778, -0.02783149853348732, -0.00957338698208332, 0.01386632863432169, 0.020321987569332123, -0.019893676042556763, 0.0384676568210125, 0.020597655326128006, 0.010284318588674068, -0.030860209837555885, 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