explain_queries.md•4.59 kB
### Explaining Atlas Vector Search Results
When you run an Atlas Vector Search query using the `explain` method, MongoDB returns a BSON document containing detailed information about the query plan and execution statistics. This explanation helps developers understand how the database processes vector search queries internally, which is useful for optimizing performance.
The basic syntax for using `explain` with vector search looks like this:
```python
db.collection.explain("verbosity").aggregate([
{
"$vectorSearch": {
"index": "index_name",
"path": "field_to_search",
"queryVector": [vector_values],
"limit": number_of_results,
...
}
}
])
```
Here, `verbosity` controls how much detail is returned:
- `queryPlanner`: Returns the query plan without execution statistics.
- `executionStats`: Includes execution statistics but not partial data.
- `allPlansExecution`: Provides full execution data including stats from all possible plans.
### Understanding the Response Fields
The `explain` response includes several key fields that describe various aspects of the query execution:
#### Metadata
Contains helpful information such as:
- `mongotVersion`: Version of the `mongot` service.
- `indexName`: Name of the vector search index used.
- `totalLuceneDocs`: Total number of documents in the index.
#### Query Details
Describes how the query was structured and executed. The `args` field contains specific parameters based on the query type:
- For `WrappedKnnQuery`, it includes sub-queries like `KnnFloatVectorQuery` and `DocAndScoreQuery`.
- For `ExactVectorSearchQuery`, it includes details about similarity functions (`dotProduct`, `cosine`, or `euclidean`) and filters.
#### Execution Stats
This section provides timing breakdowns across different areas of query execution:
- **Context**: Related to query structure setup.
- **Match**: Time spent iterating and matching result documents.
- **Score**: Time spent scoring documents.
For example, in `executionStats` mode, you might see:
```json
"context": {
"millisElapsed": 3.226133,
"invocationCounts": {
"createWeight": 1,
"createScorer": 2
}
}
```
This shows that setup tasks took approximately 3.2 milliseconds, with one `createWeight` invocation and two `createScorer` invocations.
#### Resource Usage
The `resourceUsage` block details system resource consumption:
- `majorFaults` and `minorFaults`: Page faults indicating memory issues.
- `userTimeMs` and `systemTimeMs`: CPU time spent in user and system space.
- `maxReportingThreads` and `numBatches`: Thread usage and batch processing details.
Example:
```json
"resourceUsage": {
"majorFaults": 0,
"minorFaults": 0,
"userTimeMs": 0,
"systemTimeMs": 0,
"maxReportingThreads": 1,
"numBatches": 1
}
```
This indicates no major memory issues and minimal CPU usage.
#### Collectors
The `collectors` field gives insight into the collector's performance:
- `allCollectorStats`: Aggregated stats for all collectors.
- `millisElapsed`: Total time spent collecting results.
- `invocationCounts`: Number of times operations were called.
Example:
```json
"collectors": {
"allCollectorStats": {
"millisElapsed": 0.211472,
"invocationCounts": {
"collect": 150,
"competitiveIterator": 1,
"setScorer": 1
}
}
}
```
Here, 150 results were collected over 0.21 milliseconds.
### Practical Example
Let’s consider a practical example of running an ANN (Approximate Nearest Neighbor) vector search query in `allPlansExecution` mode:
```python
result = db.embedded_movies.explain("allPlansExecution").aggregate([
{
"$vectorSearch": {
"index": "vector_index",
"path": "plot_embedding",
"queryVector": [-0.0016261312, -0.028070757, ...],
"numCandidates": 150,
"limit": 10
}
}
])
print(result)
```
The output will include detailed information about:
- Query types and structure.
- Execution timing across different components.
- Resource usage and collector behavior.
This can help identify bottlenecks in large-scale vector searches, such as excessive time spent in matching or scoring stages. By analyzing these metrics, developers can fine-tune parameters like `numCandidates` or optimize the underlying index to improve response times.
In summary, the `explain` feature in Atlas Vector Search is a powerful diagnostic tool that enables developers to gain visibility into query internals and make informed decisions to improve query efficiency.