analyzeShardKey

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Definition

Compatibility
Syntax
Command Fields
Behavior
Access Control
Output
Examples
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Definition

analyzeShardKey: New in version 7.0.
Calculates metrics for evaluating a shard key for an unsharded or sharded collection. Metrics are based on sampled queries. You can use configureQueryAnalyzer to configure query sampling on a collection.

Compatibility

This command is available in deployments hosted in the following environments:

MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

Note

This command is supported in all MongoDB Atlas clusters. For information on Atlas support for all commands, see Unsupported Commands.

MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB

Syntax

analyzeShardKey has this syntax:

db.adminCommand(
   {
     analyzeShardKey: <string>,
     key: <shardKey>,
     keyCharacteristics: <bool>,
     readWriteDistribution: <bool>,
     sampleRate: <double>,
     sampleSize: <int>
   }
 )

Command Fields

Field	Type	Necessity	Description
`analyzeShardKey`	string	Required	Namespace of the collection to analyze. There is no default value.
`key`	document	Required	Shard key to analyze. This can be a candidate shard key for an unsharded collection or sharded collection or the current shard key for a sharded collection. There is no default value.
`keyCharacteristics`	boolean	Optional	Whether or not the metrics about the characteristics of the shard key are calculated. For details, see keyCharacteristics. Defaults to `true`.
`readWriteDistribution`	boolean	Optional	Whether or not the metrics about the read and write distribution are calculated. For details, see readWriteDistribution. Defaults to `true`. To return read and write distribution metrics for a collection using `analyzeShardKey`, you must configure the query analyzer to sample the queries run on the collection. Otherwise, `analyzeShardKey` returns the read and write distribution metrics as `0` values. To configure the query analyzer, see configureQueryAnalyzer.
`sampleRate`	double	Optional	The proportion of the documents in the collection to sample when calculating the metrics about the characteristics of the shard key. If you set `sampleRate`, you cannot set `sampleSize`. Must greater than `0`, up to and including `1`. There is no default value.
`sampleSize`	integer	Optional	The number of documents to sample when calculating the metrics about the characteristics of the shard key. If you set `sampleSize`, you cannot set `sampleRate`. If not specified and `sampleRate` is not specified, the sample size defaults to sample size set by `analyzeShardKeyCharacteristicsDefaultSampleSize`.

Behavior

analyzeShardKey returns different metrics depending on the keyCharacteristic and readWriteDistribution values you specify when you run the method.

Metrics About Shard Key Characteristics

keyCharacteristic consists of the metrics about the cardinality, frequency, and monotonicity of the shard key. These metrics are only returned when keyCharacteristics is true.

The metrics are calculated when analyzeShardKey is run based on documents sampled from the collection. The calculation requires the shard key to have a supporting index. If there is no supporting index, no metrics are returned.

You can configure sampling with the sampleRate and sampleSize fields. Both are optional and only one can be specified. If neither is specified, the sample size is set to 10 . Configure this value by setting analyzeShardKeyCharacteristicsDefaultSampleSize.

To calculate metrics based on all documents in the collection, set the sampleRate to 1.

Metrics About the Read and Write Distribution

readWriteDistribution contains metrics about the query routing patterns and the hotness of shard key ranges. These metrics are based on sampled queries.

To configure query sampling for a collection, use the configureQueryAnalyzer command. The read and write distribution metrics are only returned if readWriteDistribution is true. The metrics are calculated when analyzeShardKey is run and the metrics use the sampled read and write queries. If there are no sampled queries, read and write distribution metrics aren't returned.

If there are no sampled read queries, the command returns writeDistribution but omits readDistribution.
If there are no sampled write queries, the command returns readDistribution but omits writeDistribution.

To return read and write distribution metrics for a collection using analyzeShardKey, you must configure the query analyzer to sample the queries run on the collection. Otherwise, analyzeShardKey returns the read and write distribution metrics as 0 values. To configure the query analyzer, see configureQueryAnalyzer.

`keyCharacteristics` Value	`readWriteDistribution` Value	Results Returned
`true`	`false`	`analyzeShardKey` returns keyCharacteristics metrics and omits readWriteDistribution metrics. If the shard key doesn't have a supporting index, `analyzeShardKey` returns an `IllegalOperation` error.
`false`	`true`	`analyzeShardKey` returns `readWriteDistribution` metrics and omits `keyCharacteristics` metrics.
`true`	`true`	`analyzeShardKey` returns both `readWriteDistribution` metrics and `keyCharacteristics` metrics. If the shard key doesn't have a supporting index, `analyzeShardKey` returns `readWriteDistribution` metrics and omits `keyCharacteristics` metrics.

Non-Blocking Behavior

analyzeShardKey does not block reads or writes to the collection.

Query Sampling

The quality of the metrics about the read and write distribution is determined by how representative the workload is when query sampling occurs. For some applications, returning representative metrics may require leaving query sampling on for several days.

Supporting Indexes

The supporting index required by analyzeShardKey is different from the supporting index required by the shardCollection command.

This table shows the supporting indexes for the same shard key for both analyzeShardKey and shardCollection:

Command	Shard Key	Supporting Indexes
`analyzeShardKey`	`{ a.x: 1, b: "hashed" }`	`{ a.x: 1, b: 1, ... }` `{ a.x: "hashed", b: 1, ... }` `{ a.x: 1, b: "hashed", ... }` `{ a.x: "hashed", b: "hashed", ...}`
`shardCollection`	`{ a.x: 1, b: "hashed" }`	`{ a.x: 1, b: “hashed”, ... }`

This allows you to analyze a shard key that may not yet have a supporting index required for sharding it.

Both analyzeShardKey and shardCollection have the following index requirements:

Index has a simple collation
Index is not multi-key
Index is not sparse
Index is not partial

To create supporting indexes, use the db.collection.createIndex() method.

Read Preference

To minimize the performance, run analyzeShardKey with the secondary or secondaryPreferred read preference. On a sharded cluster, mongos automatically sets the read preference to secondaryPreferred if not specified.

Limitations

You cannot run analyzeShardKey on Atlas shared clusters and serverless instances.
You cannot run analyzeShardKey on standalone deployments.
You cannot run analyzeShardKey directly against a --shardsvr replica set. When running on a sharded cluster, analyzeShardKey must run against a mongos.
You cannot run analyzeShardKey against time series collections.
You cannot run analyzeShardKey against collections with Queryable Encryption.

Access Control

analyzeShardKey requires one of these roles:

enableSharding privilege action against the collection being analyzed.
clusterManager role against the cluster.

Output

analyzeShardKey returns information regarding keyCharacteristics and readWriteDistribution.

keyCharacteristics provides metrics about the cardinality, frequency, and monotonicity of the shard key.
readWriteDistribution provides metrics about query routing patterns and the hotness of shard key ranges.

keyCharacteristics

This is the structure of the keyCharacteristics document that is returned when keyCharacteristics is set to true:

{
   keyCharacteristics: {
      numDocsTotal: <integer>,
      numOrphanDocs: <integer>,
      avgDocSizeBytes: <integer>,
      numDocsSampled: <integer>,
      isUnique: <bool>,
      numDistinctValues: <integer>,
      mostCommonValues: [
        { value: <shardkeyValue>, frequency: <integer> },
        ...
      ],
      monotonicity: {
        recordIdCorrelationCoefficient: <double>,
        type: "monotonic"|"not monotonic"|"unknown",
    }
  }
}

Field	Type	Description	Usage
`numDocsTotal`	integer	The number of documents in the collection.
`numOrphanDocs`	integer	The number of orphan documents.	Orphan documents are not excluded from metrics calculation for performance reasons. If `numOrphanDocs` is large relative to `numDocsTotal`, consider waiting until the number of orphan documents is very small compared to the total number of documents in the collection to run the command.
`avgDocSizeBytes`	integer	The average size of documents in the collection, in bytes.	If `numDocsTotal` is comparable to `numDocsSampled`, you can estimate the size of the largest chunks by multiplying the `frequency` of each `mostCommonValues` by `avgDocSizeBytes`.
`numDocsSampled`	integer	The number of sampled documents.
`numDistinctValues`	integer	The number of distinct shard key values.	Choose a shard key with a large `numDistinctValues` since the number of distinct shard key values is the maximum number of chunks that the balancer can create.
`isUnique`	boolean	Indicates whether the shard key is unique. This is only set to `true` if there is a unique index for the shard key.	If the shard key is unique, then the number of distinct values is equal to the number of documents.
`mostCommonValues`	array of documents	An array of value and `frequency` (number of documents) of the top most common shard key values.	The frequency of a shard key value is the minimum number of documents in the chunk containing that value. If the frequency is large, then the chunk can become a bottleneck for storage, reads and writes. Choose a shard key where the frequency for each most common value is low relative to `numDocsSampled`. The number of most common shard key values can be configured by setting `analyzeShardKeyNumMostCommonValues` which defaults to `5`. To avoid exceeding the 16MB BSON size limit for the response, each value is set to "truncated" if its size exceeds 15MB / analyzeShardKey NumMostCommonValues.
`mostCommonValues[n].value`	document	The shard key.
`mostCommonValues[n].frequency`	integer	The number of documents for a given shard key.	Choose a shard key where the frequency for each most common value is low relative to `numDocsSampled`.
`monotonicity.` `recordIdCorrelationCoefficient`	double	Only set if the monotonicity is known.	This is set to `"unknown"` when the one of the following is true: The shard key does not have a supporting index per `shardCollection` definition. The collection is clustered. The shard key is a hashed compound shard key where the hashed field is not the first field. The monotonicity check can return an incorrect result if the collection has gone through chunk migrations. Chunk migration deletes documents from the donor shard and re-inserts them on the recipient shard. There is no guarantee that the insertion order from the client is preserved. You can configure the threshold for the correlation coefficient with analyzeShardKeyMonotonicity CorrelationCoefficientThreshold.
`monotoncity.type`	string	Can be one of: `"monotonic"`, `"not monotonic"`, `"unknown"`	Avoid a shard key with type `"monotonic"` unless you do not expect to insert new documents often. If a collection is sharded on a shard key that is monotonically increasing or decreasing, new documents will be inserted onto the shard that owns the `MaxKey` or `MinKey` chunk. That shard can become the bottleneck for inserts and the data will likely be unbalanced most of the time since the balancer will need to compete with the inserts that come in.

readWriteDistribution

This is the structure of the document that is returned when readWriteDistribution is set to true:

{
   readDistribution: {
     sampleSize: {
       total: <integer>,
       find: <integer>,
       aggregate: <integer>,
       count: <integer>,
       distinct: <integer>
     },
     percentageOfSingleShardReads: <double>,
     percentageOfMultiShardReads: <double>,
     percentageOfScatterGatherReads: <double>,
     numReadsByRange: [
       <integer>,
       ...
     ]
   },
   writeDistribution: {
     sampleSize: {
       total: <integer>,
       update: <integer>,
       delete: <integer>,
       findAndModify: <integer>
     },
     percentageOfSingleShardWrites: <double>,
     percentageOfMultiShardWrites: <double>,
     percentageOfScatterGatherWrites: <double>,
     numWritesByRange: [
       <integer>,
       ...
     ],
     percentageOfShardKeyUpdates: <double>,
     percentageOfSingleWritesWithoutShardKey: <double>,
     percentageOfMultiWritesWithoutShardKey: <double>
   }
}

readDistribution Fields

Field	Type	Description	Usage
`sampleSize.total`	integer	Total number of sampled read queries.
`sampleSize.find`	integer	Total number of sampled `find` queries.
`sampleSize.aggregate`	integer	Total number of sampled `aggregate` queries.
`sampleSize.count`	integer	Total number of sampled `count` queries.
`sampleSize.distinct`	integer	Total number of sampled `distinct` queries.
`percentageOfSingleShardReads`	double	Percentage of reads that target a single shard, regardless of how the data is distributed.
`percentageOfMultiShardReads`	double	Percentage of reads that target multiple shards.	This category includes the reads that may target only a single shard if the data is distributed such that the values targeted by the read fall under a single shard. If the queries operate on a large amount of data, then targeting multiple shards instead of one may result in a decrease in latency due to the parallel query execution.
`percentageOfScatterGatherReads`	double	Percentage of reads that are scatter-gather, regardless of how the data is distributed.	Avoid a shard key with high value for this metric. While scatter-gather queries are low-impact on the shards that do not have the target data, they still have some performance impact. On a cluster with a large number of shards, scatter-gather queries perform significantly worse than queries that target a single shard.
`numReadsByRange`	array of integers	Array of numbers representing the number of times that each range sorted from `MinKey` to `MaxKey` is targeted.	Avoid a shard key where the distribution of `numReadsByRange` is very skewed since that implies that there is likely to be one or more hot shards for reads. Choose a shard key where the sum of `numReadsByRange` is similar to `sampleSize.total`. The number of ranges can be configured using the `analyzeShardKeyNumRanges` parameter which defaults to `100`. The value is `100` because the goal is to find a shard key that scales up to 100 shards.

writeDistribution Fields

Field	Type	Description	Usage
`sampleSize.total`	integer	Total number of sampled write queries.
`sampleSize.update`	integer	Total number of sampled `update` queries.
`sampleSize.delete`	integer	Total number of sampled `delete` queries.
`sampleSize.findAndModify`	integer	Total number of sampled `findAndModify` queries.
`percentageOfSingleShardWrites`	double	Percentage of writes that target a single shard, regardless of how the data is distributed.
`percentageOfMultiShardWrites`	double	Percentage of writes that target multiple shards.	This category includes the writes that may target only a single shard if the data is distributed such that the values targeted by the write fall under a single shard.
`percentageOfScatterGatherWrites`	double	Percentage of writes that are scatter-gather, regardless of how the data is distributed.	Avoid a shard key with a high value for this metric because it is generally more performant for a write to target a single shard.
`numWritesByRange`	array of integers	Array of numbers representing the number of times that each range sorted from `MinKey` to `MaxKey` is targeted.	Avoid a shard key where the distribution of `numWritesByRange` is a very skewed since that implies that there is likely to be one or more hot shards for writes. Choose a shard key where the sum of `numWritesByRange` is similar to `sampleSize.total`. The number of ranges can be configured using the `analyzeShardKeyNumRanges` parameter which defaults to `100`. The value is `100` because the goal is to find a shard key that scales up to 100 shards.
`percentageOfShardKeyUpdates`	double	Percentage of write queries that update a document’s shard key value.	Avoid a shard key with a high `percentageOfShardKeyUpdates`. Updates to a document’s shard key value may cause the document to move to a different shard, which requires executing an internal transaction on the shard that the query targets. For details on changing a document's shard key value, see Change a Shard Key. Updates are currently only supported as retryable writes or in a transaction, and have a batch size limit of `1`.
`percentageOfSingleWritesWithoutShardKey`	double	The percentage of write queries that are `multi=false` and not targetable to a single shard.	Avoid a shard key with a high value for this metric. Performing this type of write is expensive because they can involve running internal transactions.
`percentageOfMultiWritesWithoutShardKey`	double	The percentage of write queries that are `multi=true` and not targetable to a single shard.	Avoid a shard key with a high value for this metric.

Examples

Consider a simplified version of a social media app. The collection we are trying to shard is the post collection.

Documents in the post collection have the following schema:

{
   userId: <uuid>,
   firstName: <string>,
   lastName: <string>,
   body: <string>,  // the field that can be modified.
   date: <date>,    // the field that can be modified.
}

Background Information

The app has 1500 users.
There are 30 last names and 45 first names, some more common than others.
There are three celebrity users.
Each user follows exactly five other users and has a very high probability of following at least one celebrity user.

Sample Workload

Each user posts about two posts a day at random times. They edit each post once, right after it is posted.
Each user logs in every six hours to read their own profile and posts by the users they follow from the past 24 hours. They also reply under a random post from the past three hours.
For every user, the app removes posts that are more than three days old at midnight.

Workload Query Patterns

This workload has the following query patterns:

find command with filter { userId: , firstName: , lastName: }
find command with filter { $or: [{ userId: , firstName: , lastName:, date: { $gte: }, ] }
findAndModify command with filter { userId: , firstName: , lastName: , date: } to update the body and date field.
update command with multi: false and filter { userId: , firstName: , lastName: , date: { $gte: , $lt: } } to update the body and date field.
delete command with multi: true and filter { userId: , firstName: , lastName: , date: { $lt: } }

Below are example metrics returned by analyzeShardKey for some candidate shard keys, with sampled queries collected from seven days of workload.

Note

Before you run analyzeShardKey commands, read the Supporting Indexes section earlier on this page. If you require supporting indexes for the shard key you are analyzing, use the db.collection.createIndex() method to create the indexes.

{ lastName: 1 } keyCharacteristics

This analyzeShardKey command provides metrics on the { lastName: 1 } shard key on the social.post collection:

use social
db.post.analyzeShardKey(
   {
     key: { lastName: 1 },
     keyCharacteristics: true,
     readWriteDistribution: false,
   }
)

The output for this command is similar to the following:

{
   "keyCharacteristics": {
     "numDocsTotal" : 9039,
     "avgDocSizeBytes" : 153,
     "numDocsSampled" : 9039,
     "isUnique" : false,
     "numDistinctValues" : 30,
     "mostCommonValues" : [
         {
           "value" : {
               "lastName" : "Smith"
           },
           "frequency" : 1013
         },
         {
           "value" : {
               "lastName" : "Johnson"
           },
           "frequency" : 984
         },
         {
           "value" : {
               "lastName" : "Jones"
           },
           "frequency" : 962
         },
         {
           "value" : {
               "lastName" : "Brown"
           },
           "frequency" : 925
         },
         {
           "value" : {
               "lastName" : "Davies"
           },
           "frequency" : 852
         }
     ],
     "monotonicity" : {
       "recordIdCorrelationCoefficient" : 0.0771959161,
       "type" : "not monotonic"
   },
 }
}

{ userId: 1 } keyCharacteristics

This analyzeShardKey command provides metrics on the { userId: 1 } shard key on the social.post collection:

db.adminCommand(
   {
     analyzeShardKey: "social.post",
     key: { userId: 1 },
     keyCharacteristics: true,
     readWriteDistribution: false
   }
)

The output for this command is similar to the following:

{
  "keyCharacteristics": {
    "numDocsTotal" : 9039,
    "avgDocSizeBytes" : 162,
    "numDocsSampled" : 9039,
    "isUnique" : false,
    "numDistinctValues" : 1495,
    "mostCommonValues" : [
      {
        "value" : {
          "userId" : UUID("aadc3943-9402-4072-aae6-ad551359c596")
        },
        "frequency" : 15
      },
     {
       "value" : {
         "userId" : UUID("681abd2b-7a27-490c-b712-e544346f8d07")
       },
       "frequency" : 14
     },
     {
       "value" : {
         "userId" : UUID("714cb722-aa27-420a-8d63-0d5db962390d")
       },
       "frequency" : 14
     },
     {
       "value" : {
         "userId" : UUID("019a4118-b0d3-41d5-9c0a-764338b7e9d1")
       },
       "frequency" : 14
     },
     {
       "value" : {
         "userId" : UUID("b9c9fbea-3c12-41aa-bc69-eb316047a790")
       },
       "frequency" : 14
     }
   ],
   "monotonicity" : {
     "recordIdCorrelationCoefficient" : -0.0032039729,
     "type" : "not monotonic"
   },
 }
}

{ userId: 1 } readWriteDistribution

This analyzeShardKey command provides metrics on the { userId: 1 } shard key on the social.post collection:

db.adminCommand(
   {
     analyzeShardKey: "social.post",
     key: { userId: 1 },
     keyCharacteristics: false,
     readWriteDistribution: true
   }
)

The output for this command is similar to the following:

{
   "readDistribution" : {
     "sampleSize" : {
       "total" : 61363,
       "find" : 61363,
       "aggregate" : 0,
       "count" : 0,
       "distinct" : 0
     },
     "percentageOfSingleShardReads" : 50.0008148233,
     "percentageOfMultiShardReads" : 49.9991851768,
     "percentageOfScatterGatherReads" : 0,
     "numReadsByRange" : [
       688,
       775,
       737,
       776,
       652,
       671,
       1332,
       1407,
       535,
       428,
       985,
       573,
       1496,
       ...
       ],
     },
   "writeDistribution" : {
     "sampleSize" : {
       "total" : 49638,
       "update" : 30680,
       "delete" : 7500,
       "findAndModify" : 11458
     },
     "percentageOfSingleShardWrites" : 100,
     "percentageOfMultiShardWrites" : 0,
     "percentageOfScatterGatherWrites" : 0,
     "numWritesByRange" : [
       389,
       601,
       430,
       454,
       462,
       421,
       668,
       833,
       493,
       300,
       683,
       460,
       ...
      ],
      "percentageOfShardKeyUpdates" : 0,
      "percentageOfSingleWritesWithoutShardKey" : 0,
      "percentageOfMultiWritesWithoutShardKey" : 0
    }
}

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