db.collection.analyzeShardKey() (mongosh method方法)

Background Information背景信息

• The app has 1500 users.该应用程序有1500名用户。
• There are 30 last names and 45 first names, some more common than others.有30个姓氏和45个名字，有些比其他名字更常见。
• There are three celebrity users.有三位名人用户。
• Each user follows exactly five other users and has a very high probability of following at least one celebrity user.每个用户只关注另外五个用户，并且很有可能关注至少一个名人用户。

Sample Workload示例工作量

• Each user posts about two posts a day at random times. They edit each post once, right after it is posted.每个用户每天随机发布大约两条帖子。他们在每篇帖子发布后立即编辑一次。
• 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.每个用户每六个小时登录一次，阅读他们自己的个人资料以及他们在过去24小时内关注的用户的帖子。他们还随机回复了过去三个小时的帖子。
• For every user, the app removes posts that are more than three days old at midnight.对于每个用户，该应用程序都会在午夜删除超过三天的帖子。

Workload Query Patterns工作负载查询模式

This workload has the following query patterns:此工作负载具有以下查询模式：

• find command with filter { userId: , firstName: , lastName: }使用筛选器{ userId: , firstName: , lastName: }的find命令
• find command with filter { $or: [{ userId: , firstName: , lastName:, date: { $gte: }, ] }使用筛选器{ $or: [{ userId: , firstName: , lastName:, date: { $gte: }, ] }的find命令。
• findAndModify command with filter { userId: , firstName: , lastName: , date: } to update the body and date field.使用筛选器{ userId: , firstName: , lastName: , date: }的findAndModify命令以更新body和date字段。
• update command with multi: false and filter { userId: , firstName: , lastName: , date: { $gte: , $lt: } } to update the body and date field.使用multi: false和筛选器{ userId: , firstName: , lastName: , date: { $gte: , $lt: } }的update命令以更新body和date命令。
• delete command with multi: true and filter { userId: , firstName: , lastName: , date: { $lt: } }使用multi: true和筛选器{ userId: , firstName: , lastName: , date: { $lt: } }的delete命令。

Below are example metrics returned by db.collection.analyzeShardKey method for some candidate shard keys, with sampled queries collected from seven days of workload.下面是db.collection.analyzeShardKey方法为一些候选分片键返回的示例指标，其中包含从七天的工作负载中集合的采样查询。

{ _id: 1 } keyCharacteristics

This example uses the db.collection.analyzeShardKey method to provide metrics on the { _id: 1 } shard key on the social.post collection.此示例使用db.collection.analyzeShardKey方法提供social.post集合上{ _id: 1 }分片键的度量。

The following code block uses db.collection.configureQueryAnalyzer() to turn on query sampling:以下代码块使用db.collection.configureQueryAnalyzer()打开查询采样：

use social
db.post.configureQueryAnalyzer(
   {
      mode: "full",
      samplesPerSecond: 5
   }
)

After db.collection.configureQueryAnalyzer() collects query samples, the following code block uses the db.collection.analyzeShardKey method to sample 10,000 documents and calculate results:db.collection.configureQueryAnalyzer()集合查询样本后，以下代码块使用db.collection.analyzeShardKey方法对10000个文档进行采样并计算结果：

use social
db.post.analyzeShardKey(
   { _id: 1 },
   {
      keyCharacteristics: true,
      readWriteDistribution: false,
      sampleSize: 10000
   }
)

{ lastName: 1 } keyCharacteristics

This db.collection.analyzeShardKey method provides metrics on the { lastName: 1 } shard key on the social.post collection:此db.collection.analyzeShardKey方法提供social.post集合上{ lastName: 1 }分片键的度量：

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

The output for this example resembles the following:此示例的输出类似于以下内容：

{
   "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 db.collection.analyzeShardKey method provides metrics on the { userId: 1 } shard key on the social.post collection:此db.collection.analyzeShardKey方法提供了social.post集合上{userId:1}分片键的度量：

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

The output for this example resembles the following:此示例的输出类似于以下内容：

{
  "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 db.collection.analyzeShardKey method provides metrics on the { userId: 1 } shard key on the social.post collection:此db.collection.analyzeShardKey方法提供了social.post集合上{userId:1}分片键的度量：

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

The output for this example resembles the following:此示例的输出类似于以下内容：

{
   "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
    }
}