Analyze Query Performance

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The cursor.explain("executionStats") and the db.collection.explain("executionStats") methods provide statistics about the performance of a query. These statistics can be useful in measuring if and how a query uses an index.

db.collection.explain() provides information on the execution of other operations, such as db.collection.update(). See db.collection.explain() for details.

MongoDB Compass provides an Explain Plan tab, which displays statistics about the performance of a query. These statistics can be useful in measuring if and how a query uses an index.

Evaluate the Performance of a Query

Consider a collection inventory with the following documents:

{ "_id" : 1, "item" : "f1", type: "food", quantity: 500 }
{ "_id" : 2, "item" : "f2", type: "food", quantity: 100 }
{ "_id" : 3, "item" : "p1", type: "paper", quantity: 200 }
{ "_id" : 4, "item" : "p2", type: "paper", quantity: 150 }
{ "_id" : 5, "item" : "f3", type: "food", quantity: 300 }
{ "_id" : 6, "item" : "t1", type: "toys", quantity: 500 }
{ "_id" : 7, "item" : "a1", type: "apparel", quantity: 250 }
{ "_id" : 8, "item" : "a2", type: "apparel", quantity: 400 }
{ "_id" : 9, "item" : "t2", type: "toys", quantity: 50 }
{ "_id" : 10, "item" : "f4", type: "food", quantity: 75 }

The documents appear in MongoDB Compass as the following:

Compass Inventory collection documents

Query with No Index

The following query retrieves documents where the quantity field has a value between 100 and 200, inclusive:

db.inventory.find( { quantity: { $gte: 100, $lte: 200 } } )

The query returns the following documents:

{ "_id" : 2, "item" : "f2", "type" : "food", "quantity" : 100 }
{ "_id" : 3, "item" : "p1", "type" : "paper", "quantity" : 200 }
{ "_id" : 4, "item" : "p2", "type" : "paper", "quantity" : 150 }

To view the query plan selected, chain the cursor.explain("executionStats") cursor method to the end of the find command:

db.inventory.find(
   { quantity: { $gte: 100, $lte: 200 } }
).explain("executionStats")

explain() returns the following results:

{
   "queryPlanner" : {
         "plannerVersion" : 1,
         ...
         "winningPlan" : {
            "stage" : "COLLSCAN",
            ...
         }
   },
   "executionStats" : {
      "executionSuccess" : true,
      "nReturned" : 3,
      "executionTimeMillis" : 0,
      "totalKeysExamined" : 0,
      "totalDocsExamined" : 10,
      "executionStages" : {
         "stage" : "COLLSCAN",
         ...
      },
      ...
   },
   ...
}
  • queryPlanner.winningPlan.stage displays COLLSCAN to indicate a collection scan.

    Collection scans indicate that the mongod had to scan the entire collection document by document to identify the results. This is a generally expensive operation and can result in slow queries.

  • executionStats.nReturned displays 3 to indicate that the query matches and returns three documents.
  • executionStats.totalKeysExamined displays 0 to indicate that this is query is not using an index.
  • executionStats.totalDocsExamined displays 10 to indicate that MongoDB had to scan ten documents (i.e. all documents in the collection) to find the three matching documents.

The following query retrieves documents where the quantity field has a value between 100 and 200, inclusive:

Copy the following filter into the Compass query bar and click Find:将以下筛选器复制到Compass查询栏中，然后单击“查找”：

{ quantity: { $gte: 100, $lte: 200 } }

The query returns the following documents:

Compass no index query results

To view the query plan selected:

  1. Click the Explain Plan tab for the test.inventory collection.
  2. Click Explain.

MongoDB Compass displays the query plan as follows:

Compass no index query plan

Note

Because we are working with such a small dataset for the purposes of this tutorial, the Actual Query Execution Time displays 0 seconds, even though we are not using an index.

In a larger dataset, the difference in query execution time between an indexed query versus a non-indexed query would be much more substantial.

Visual Tree

  • The Query Performance Summary shows the execution stats of the query:

    • Documents Returned displays 3 to indicate that the query matches and returns three documents.
    • Index Keys Examined displays 0 to indicate that this query is not using an index.
    • Documents Examined displays 10 to indicate that MongoDB had to scan ten documents (i.e. all documents in the collection) to find the three matching documents.
  • Below the Query Performance Summary, MongoDB Compass displays the COLLSCAN query stage to indicate that a collection scan was used for this query.

    Collection scans indicate that the mongod had to scan the entire collection document by document to identify the results. This is a generally expensive operation and can result in slow queries.

Raw JSON

The explain details can also be viewed in raw JSON format by clicking Raw JSON below the query bar:

Compass no index query plan raw JSON

The difference between the number of matching documents and the number of examined documents may suggest that, to improve efficiency, the query might benefit from the use of an index.

Query with Index

To support the query on the quantity field, add an index on the quantity field:

db.inventory.createIndex( { quantity: 1 } )

To view the query plan statistics, use the explain("executionStats") method:

db.inventory.find(
   { quantity: { $gte: 100, $lte: 200 } }
).explain("executionStats")

The explain() method returns the following results:

{
   "queryPlanner" : {
         "plannerVersion" : 1,
         ...
         "winningPlan" : {
               "stage" : "FETCH",
               "inputStage" : {
                  "stage" : "IXSCAN",
                  "keyPattern" : {
                     "quantity" : 1
                  },
                  ...
               }
         },
         "rejectedPlans" : [ ]
   },
   "executionStats" : {
         "executionSuccess" : true,
         "nReturned" : 3,
         "executionTimeMillis" : 0,
         "totalKeysExamined" : 3,
         "totalDocsExamined" : 3,
         "executionStages" : {
            ...
         },
         ...
   },
   ...
}
  1. Click the Indexes tab for the test.inventory collection.
  2. Click Create Index.
  3. Select quantity from the Select a field name dropdown.
  4. Select 1 (asc) from the type dropdown.
  5. Click Create.
Create inventory index in Compass

Note

Leaving the index name field blank causes MongoDB Compass to create a default name for the index.

You can now see your newly created index in the Indexes tab:

Compass show new index

Return to the Explain Plan tab for the inventory collection and re-run the query from the previous step:

{ quantity: { $gte: 100, $lte: 200 } }

MongoDB Compass displays the query plan as follows:

Compass explain plan with index

Visual Tree

  • The Query Performance Summary shows the execution stats of the query:
    • Documents Returned displays 3 to indicate that the query matches and returns three documents.
    • Index Keys Examined displays 3 to indicate that MongoDB scanned three index entries. The number of keys examined match the number of documents returned, meaning that the mongod only had to examine index keys to return the results. The mongod did not have to scan all of the documents, and only the three matching documents had to be pulled into memory. This results in a very efficient query.
    • Documents Examined displays 3 to indicate that MongoDB scanned three documents.
    • On the right-hand side of the Query Performance Summary, MongoDB Compass shows that the query used the quantity index.
  • Below the Query Performance Summary, MongoDB Compass displays the query stages FETCH and IXSCAN. IXSCAN indicates that the mongod used an index to satisfy the query before exeuting the FETCH stage and retrieving the documents.

Raw JSON

The explain details can also be viewed in raw JSON format by clicking Raw JSON below the query bar:

Compass query plan with index raw JSON

Without the index, the query would scan the whole collection of 10 documents to return 3 matching documents. The query also had to scan the entirety of each document, potentially pulling them into memory. This results in an expensive and potentially slow query operation.

When run with an index, the query scanned 3 index entries and 3 documents to return 3 matching documents, resulting in a very efficient query.

Compare Performance of Indexes

To manually compare the performance of a query using more than one index, you can use the hint() method in conjunction with the explain() method.

Consider the following query:

db.inventory.find( {
   quantity: {
      $gte: 100, $lte: 300
   },
   type: "food"
} )

The query returns the following documents:

{ "_id" : 2, "item" : "f2", "type" : "food", "quantity" : 100 }
{ "_id" : 5, "item" : "f3", "type" : "food", "quantity" : 300 }

To support the query, add a compound index. With compound indexes, the order of the fields matter.

For example, add the following two compound indexes. The first index orders by quantity field first, and then the type field. The second index orders by type first, and then the quantity field.

db.inventory.createIndex( { quantity: 1, type: 1 } )
db.inventory.createIndex( { type: 1, quantity: 1 } )

Evaluate the effect of the first index on the query:

db.inventory.find(
   { quantity: { $gte: 100, $lte: 300 }, type: "food" }
).hint({ quantity: 1, type: 1 }).explain("executionStats")

The explain() method returns the following output:

{
   "queryPlanner" : {
      ...
      "winningPlan" : {
         "stage" : "FETCH",
         "inputStage" : {
            "stage" : "IXSCAN",
            "keyPattern" : {
               "quantity" : 1,
               "type" : 1
            },
            ...
            }
         }
      },
      "rejectedPlans" : [ ]
   },
   "executionStats" : {
      "executionSuccess" : true,
"nReturned" : 2,      "executionTimeMillis" : 0,
"totalKeysExamined" : 5,      "totalDocsExamined" : 2,
      "executionStages" : {
      ...
      }
   },
   ...
}

MongoDB scanned 5 index keys (executionStats.totalKeysExamined) to return 2 matching documents (executionStats.nReturned).

Evaluate the effect of the second index on the query:

db.inventory.find(
   { quantity: { $gte: 100, $lte: 300 }, type: "food" }
).hint({ type: 1, quantity: 1 }).explain("executionStats")

The explain() method returns the following output:

{
   "queryPlanner" : {
      ...
      "winningPlan" : {
         "stage" : "FETCH",
         "inputStage" : {
            "stage" : "IXSCAN",
            "keyPattern" : {
               "type" : 1,
               "quantity" : 1
            },
            ...
         }
      },
      "rejectedPlans" : [ ]
   },
   "executionStats" : {
      "executionSuccess" : true,
"nReturned" : 2,      "executionTimeMillis" : 0,
"totalKeysExamined" : 2,      "totalDocsExamined" : 2,
      "executionStages" : {
         ...
      }
   },
   ...
}

MongoDB scanned 2 index keys (executionStats.totalKeysExamined) to return 2 matching documents (executionStats.nReturned).

The second compound index, { type: 1, quantity: 1 }, is therefore the more efficient index for supporting the example query, as the MongoDB server only needs to scan 2 index keys to find all matching documents using this index, compared to 5 when when using the compound index { quantity: 1, type: 1 }.

See also参阅

Query Optimization, Query Plans, Optimize Query Performance, Indexing Strategies

See also参阅