我不知道七年前问这个问题的时候是不是真的,但这完全可以在聚合框架内解决。假设集合名称为AdjacentDocument
,则以下聚合将获得您要查找的结果:
db.AdjacentDocument.aggregate(
{$sort: {time: 1}},
{$group: {_id: 0, document: {$push: '$$ROOT'}}},
{$project: {documentAndPrevTime: {$zip: {inputs: ['$document', {$concatArrays: [[null], '$document.time']}]}}}},
{$unwind: {path: '$documentAndPrevTime'}},
{$replaceWith: {$mergeObjects: [{$arrayElemAt: ['$documentAndPrevTime', 0]}, {prevTime: {$arrayElemAt: ['$documentAndPrevTime', 1]}}]}},
{$set: {time_difference: {$trunc: [{$divide: [{$subtract: ['$time', '$prevTime']}, 1000]}]}}},
{$unset: 'prevTime'}
);
聚合管道演练
首先,文档从最旧到最新排序。它们被分组到一个文档中,文档存储在有序数组字段中:
{$sort: {time: 1}},
{$group: {_id: 0, document: {$push: '$$ROOT'}}}
/*
{
"_id" : 0,
"document" : [
{
"_id" : ObjectId("50ed90a55502684f440001ac"),
"time" : ISODate("2013-02-13T15:45:41.148Z")
},
{
"_id" : ObjectId("50ed90a55502684f440001ad"),
"time" : ISODate("2013-02-13T15:45:42.148Z")
},
{
"_id" : ObjectId("50ed90a55502684f440001ae"),
"time" : ISODate("2013-02-13T15:45:45.148Z")
}
]
}
*/
接下来,将之前的时间压缩到文档数组中,创建一个数组[document, previousTime]
:
{$project: {documentAndPrevTime: {$zip: {inputs: ['$document', {$concatArrays: [[null], '$document.time']}]}}}}
/*
{
"_id" : 0,
"documentAndPrevTime" : [
[
{
"_id" : ObjectId("50ed90a55502684f440001ac"),
"time" : ISODate("2013-02-13T15:45:41.148Z")
},
null
],
[
{
"_id" : ObjectId("50ed90a55502684f440001ad"),
"time" : ISODate("2013-02-13T15:45:42.148Z")
},
ISODate("2013-02-13T15:45:41.148Z")
],
[
{
"_id" : ObjectId("50ed90a55502684f440001ae"),
"time" : ISODate("2013-02-13T15:45:45.148Z")
},
ISODate("2013-02-13T15:45:42.148Z")
]
]
}
*/
接下来,展开文档和时间数组,为每个初始文档创建一个文档:
{$unwind: {path: '$documentAndPrevTime'}}
/*
{
"_id" : 0,
"documentAndPrevTime" : [
{
"_id" : ObjectId("50ed90a55502684f440001ac"),
"time" : ISODate("2013-02-13T15:45:41.148Z")
},
null
]
}
{
"_id" : 0,
"documentAndPrevTime" : [
{
"_id" : ObjectId("50ed90a55502684f440001ad"),
"time" : ISODate("2013-02-13T15:45:42.148Z")
},
ISODate("2013-02-13T15:45:41.148Z")
]
}
{
"_id" : 0,
"documentAndPrevTime" : [
{
"_id" : ObjectId("50ed90a55502684f440001ae"),
"time" : ISODate("2013-02-13T15:45:45.148Z")
},
ISODate("2013-02-13T15:45:42.148Z")
]
}
*/
接下来,我们将文档替换为文档数组元素的值,并与上一个时间元素合并(如果它是初始索引,则使用 null):
{$replaceWith: {$mergeObjects: [{$arrayElemAt: ['$documentAndPrevTime', 0]}, {prevTime: {$arrayElemAt: ['$documentAndPrevTime', 1]}}]}}
/*
{
"_id" : ObjectId("50ed90a55502684f440001ac"),
"time" : ISODate("2013-02-13T15:45:41.148Z"),
"prevTime" : null
}
{
"_id" : ObjectId("50ed90a55502684f440001ad"),
"time" : ISODate("2013-02-13T15:45:42.148Z"),
"prevTime" : ISODate("2013-02-13T15:45:41.148Z")
}
{
"_id" : ObjectId("50ed90a55502684f440001ae"),
"time" : ISODate("2013-02-13T15:45:45.148Z"),
"prevTime" : ISODate("2013-02-13T15:45:42.148Z")
}
*/
time_difference
最后,我们通过将 设置为两个时间字段的差异并删除临时字段来更新文档prevTime
。由于两个日期之间的差异以毫秒为单位,并且您的示例使用秒,因此我们通过除以 1000 并截断来计算秒数。
{$set: {time_difference: {$trunc: [{$divide: [{$subtract: ['$time', '$prevTime']}, 1000]}]}}},
{$unset: 'prevTime'}
/*
{
"_id" : ObjectId("50ed90a55502684f440001ac"),
"time" : ISODate("2013-02-13T15:45:41.148Z"),
"time_difference" : null
}
{
"_id" : ObjectId("50ed90a55502684f440001ad"),
"time" : ISODate("2013-02-13T15:45:42.148Z"),
"time_difference" : 1
}
{
"_id" : ObjectId("50ed90a55502684f440001ae"),
"time" : ISODate("2013-02-13T15:45:45.148Z"),
"time_difference" : 3
}
*/