Add Secondary Indexes to Time Series Collections向时间序列集合添加辅助索引

Use Secondary Indexes to Improve Sort Performance使用辅助索引提高排序性能

Sort operations on time series collections can use secondary indexes on the timeField field. 时间序列集合上的排序操作可以使用timeField字段上的辅助索引。Under certain conditions, sort operations can also use compound secondary indexes on the metaField and timeField fields.在某些条件下，排序操作还可以对metaField和timeField字段使用复合辅助索引。

The aggregation pipeline stages $match and $sort determine which indexes a time series collection can use. 聚合管道阶段$match和$sort决定时间序列集合可以使用哪些索引。An index can be used in the following scenarios:索引可用于以下场景：

• Sort on { <timeField>: ±1 } uses a secondary index on <timeField>在{ <timeField>: ±1 }上排序在<timeField>上使用辅助索引
• Sort on { <metaField>: ±1, timeField: ±1 } uses the default compound index on { <metaField>: ±1, timeField: ±1 }在{ <metaField>: ±1, timeField: ±1 }上排序在{ <metaField>: ±1, timeField: ±1 }上使用默认复合索引
• Sort on { <timeField>: ±1 } uses a secondary index on { metaField: ±1, timeField: ±1 } when there is a point predicate on <metaField>在{ <timeField>: ±1 }上排序在{ metaField: ±1, timeField: ±1 }上使用辅助索引，当在<metaField>上存在点谓语时

For example, the following sensorData collection contains measurements from weather sensors:例如，以下sensorData集合包含来自天气传感器的测量值：

db.sensorData.insertMany( [ {
     "metadata": {
         "sensorId": 5578,
         "type": "omni",
         "location": {
             type: "Point",
             coordinates: [-77.40711, 39.03335]
         }
     },
     "timestamp": ISODate("2022-01-15T00:00:00.000Z"),
     "currentConditions": {
         "windDirection": 127.0,
         "tempF": 71.0,
         "windSpeed": 2.0,
         "cloudCover": null,
         "precip": 0.1,
         "humidity": 94.0,
     }
   },
   {
     "metadata": {
         "sensorId": 5578,
         "type": "omni",
         "location": {
             type: "Point",
             coordinates: [-77.40711, 39.03335]
         }
     },
     "timestamp": ISODate("2022-01-15T00:01:00.000Z"),
     "currentConditions": {
         "windDirection": 128.0,
         "tempF": 69.8,
         "windSpeed": 2.2,
         "cloudCover": null,
         "precip": 0.1,
         "humidity": 94.3,
     }
   },
   {
     "metadata": {
         "sensorId": 5579,
         "type": "omni",
         "location": {
             type: "Point",
             coordinates: [-80.19773, 25.77481]
         }
     },
     "timestamp": ISODate("2022-01-15T00:01:00.000Z"),
     "currentConditions": {
         "windDirection": 115.0,
         "tempF": 88.0,
         "windSpeed": 1.0,
         "cloudCover": null,
         "precip": 0.0,
         "humidity": 99.0,
     }
    }
  ]
)

Create a secondary single-field index on the timestamp field:在timestamp字段上创建辅助单字段索引：

db.sensorData.createIndex( { "timestamp": 1 } )

The following sort operation on the timestamp field uses the Secondary Index to improve performance:以下对timestamp字段的排序操作使用辅助索引来提高性能：

db.sensorData.aggregate( [
  { $match: { "timestamp" : { $gte: ISODate("2022-01-15T00:00:00.000Z") } } },
  { $sort: { "timestamp": 1 } }
] )

To confirm that the sort operation used the Secondary Index, run the operation again with the .explain( "executionStats" ) option:要确认排序操作使用了辅助索引，请使用.explain( "executionStats" )选项再次运行该操作：

db.sensorData.explain( "executionStats" ).aggregate( [
  { $match: { "timestamp": { $gte: ISODate("2022-01-15T00:00:00.000Z") } } },
  { $sort: { "timestamp": 1 } }
] )

{ "metadata.sensorId": 1,  "timestamp": 1 }
{ "metadata.sensorId": 1,  "timestamp": -1 }
{ "metadata.sensorId": -1, "timestamp": 1 }
{ "metadata.sensorId": -1, "timestamp": -1 }

db.sensorData.createIndex( { "metadata.sensorId": 1,  "timestamp": -1 } )

db.sensorData.aggregate( [
   {
      $sort: { "metadata.sensorId": 1, "timestamp": -1 }
   },
   {
      $group: {
         _id: "$metadata.sensorId",
         ts: { $first: "$timestamp" },
         temperatureF: { $first: "$currentConditions.tempF" }
      }
   }
] )

db.getCollection( 'sensorData' ).explain( "executionStats" ).aggregate( [
   {
      $sort: { "metadata.sensorId": 1, "timestamp": -1 }
   },
   {
      $group: {
         _id: "$metadata.sensorId",
         ts: { $first: "$timestamp" },
         temperatureF: { $first: "$currentConditions.tempF" }
      }
   }
] )

db.sensorData.find( { "metadata.sensorId": 5578 } ).hint( "timestamp_1_metadata.sensorId_1" )

Time Series Secondary Indexes in MongoDB 6.0 and LaterMongoDB 6.0及更高版本中的时间序列辅助索引

Starting in MongoDB 6.3, you can create a partial TTL index for a time series collection. You can only filter on the metaField.从MongoDB 6.3开始，您可以为时间序列集合创建部分TTL索引。您只能在metaField上进行筛选。

If the collection doesn't use the expireAfterSeconds option to expire documents, creating a partial TTL index sets an expiration time for matching documents only. 如果集合不使用expireAfterSeconds选项使文档过期，则创建部分TTL索引将仅为匹配的文档设置过期时间。If the collection uses expireAfterSeconds for all documents, a partial TTL index lets you expire matching documents sooner.如果集合对所有文档使用expireAfterSeconds，则部分TTL索引可以使匹配的文档提前过期。

Starting in MongoDB 6.0, you can:从MongoDB 6.0开始，您可以：

• Add a compound index on the timeField, metaField, or measurement fields.在timeField、metaField或度量字段上添加复合索引。
• Use the $or, $in, and $geoWithin operators with partial indexes on a time series collection.对时间序列集合使用带有部分索引的$or、$in和$geoWithin运算符。
• Add a partial filter expression on the metaField.在metaField上添加部分筛选器表达式。
• Add a secondary index to any field or subfield.向任何字段或子字段添加辅助索引。
• Use the metaField with 2dsphere indexes.将metaField与2dsphere索引一起使用。