Time Series Collections时间序列集合

Create a Time Series Collection创建一个时间序列集合

Before you can insert data into a time series collection, you must explicitly create the collection using either the db.createCollection() method or the create command:在将数据插入时间序列集合之前，必须使用db.createCollection()方法或create命令显式创建集合：

db.createCollection(
    "weather",
    {
       timeseries: {
          timeField: "timestamp",
          metaField: "metadata",
          granularity: "hours"
       }
    }
)

When creating a time series collection, specify the following options:创建时间序列集合时，请指定以下选项：

Other options allowed with the timeseries option are:timeseries选项允许的其他选项包括：

• storageEngine
• indexOptionDefaults
• collation
• writeConcern
• comment

Insert Measurements into a Time Series Collection将测量值插入到时间序列集合中

Each document you insert should contain a single measurement. 插入的每个文档都应包含一个测量值。To insert multiple documents at once, issue the following command:要同时插入多个文档，请发出以下命令：

db.weather.insertMany( [
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T00:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T04:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T08:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T12:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T16:00:00.000Z"),
      "temp": 16
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-18T20:00:00.000Z"),
      "temp": 15
   }, {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T00:00:00.000Z"),
      "temp": 13
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T04:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T08:00:00.000Z"),
      "temp": 11
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T12:00:00.000Z"),
      "temp": 12
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T16:00:00.000Z"),
      "temp": 17
   },
   {
      "metadata": { "sensorId": 5578, "type": "temperature" },
      "timestamp": ISODate("2021-05-19T20:00:00.000Z"),
      "temp": 12
   }
] )

To insert a single document, use the db.collection.insertOne() method.要插入单个文档，请使用db.collection.insertOne()方法。

Query a Time Series Collection查询时间序列集合

To retrieve one document from a time series collection, issue the following command:要从时间序列集合中检索一个文档，请发出以下命令：

db.weather.findOne({
   "timestamp": ISODate("2021-05-18T00:00:00.000Z")
})

Run Aggregations on a Time Series Collection在时间序列集合上运行聚合

For additional query functionality, use an aggregation pipeline such as:要获得其他查询功能，请使用聚合管道，例如：

db.weather.aggregate( [
   {
      $project: {
         date: {
            $dateToParts: { date: "$timestamp" }
         },
         temp: 1
      }
   },
   {
      $group: {
         _id: {
            date: {
               year: "$date.year",
               month: "$date.month",
               day: "$date.day"
            }
         },
         avgTmp: { $avg: "$temp" }
      }
   }
] )

The example aggregation pipeline groups all documents by the date of the measurement and then returns the average of all temperature measurements that day:示例聚合管道按测量日期对所有文档进行分组，然后返回当天所有温度测量的平均值：

 {
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 18
    }
  },
  "avgTmp" : 12.714285714285714
}
{
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 19
    }
  },
  "avgTmp" : 13
}

Check if a Collection is of Type Time Series检查集合是否为时间序列类型

To determine if a collection is of type time series, use the listCollections command:要确定集合是否为时间序列类型，请使用listCollections命令：

db.runCommand( { listCollections: 1.0 } )

If the collection is a time series collection, it returns this:如果集合是时间序列集合，它将返回以下内容：

{
    cursor: {
       id: <number>,
       ns: 'test.$cmd.listCollections',
       firstBatch: [
         {
            name: <string>,
            type: 'timeseries',
            options: {
               expireAfterSeconds: <number>,
               timeseries: { ... }
            },
            ...
         },
         ...
       ]
    }
 }

Index索引

The implementation of time series collections uses internal collections that reduce disk usage and improve query efficiency. 时间序列集合的实现使用内部集合来减少磁盘使用并提高查询效率。Time series collections automatically order and index data by time. 时间序列集合自动按时间排序和索引数据。The internal index for a time series collection is not displayed by listIndexes.listIndexes不显示时间序列集合的内部索引。

Default Compression Algorithm默认压缩算法

Time series collections ignore the global default compression algorithm, snappy, in favor of zstd, unless a different compression algorithm is specified using the storageEngine option when the collection was created. 时间序列集合忽略全局默认压缩算法snappy，而支持zstd，除非在创建集合时使用storageEngine选项指定了不同的压缩算法。For example, to change the compression algorithm to snappy for a new weather collection, add the following option:例如，要将新weather集合的压缩算法更改为snappy，请添加以下选项：

db.createCollection(
  "weather",
  {
     timeseries: {
        timeField: "timestamp"
     },
     storageEngine: {
        wiredTiger: {
           configString: "block_compressor=snappy"
        }
     }
  }
)

Valid block_compressor options are:有效的block_compressor选项包括：

• snappy
• zlib
• zstd (default)
• none

Compression Improvements压缩改进

Starting in MongoDB 5.2, time series collection data is further compressed to save database space. 从MongoDB 5.2开始，时间序列采集数据被进一步压缩以节省数据库空间。This compression does not affect query results, nor does it negatively affect performance.这种压缩不会影响查询结果，也不会对性能产生负面影响。