Docs 菜单
Docs 主页
/
MongoDB Manual
/
聚合操作
/
Map-Reduce

Map-Reduce 到聚合管道

聚合管道提供比映射化简操作更好的性能和可用性。

可以使用聚合管道阶段（例如 $group 和 $merge）重写 map-reduce 操作。

对于需要自定义功能的 map-reduce 操作，MongoDB 提供了 $accumulator 和 $function 聚合操作符。可以使用这些操作符在 JavaScript 中定义自定义聚合表达式。

您可以重写 map-reduce 表达式，如以下各部分所示。

Map-reduce 到聚合管道转换表

该表仅为一个近似转换。例如，该表显示了使用 $project 对 mapFunction 进行近似转换。

  • 但是，mapFunction 逻辑可能需要其他阶段，例如当此逻辑包含数组迭代时：

    function() {
       this.items.forEach(function(item){ emit(item.sku, 1); });
    }

    然后，该聚合管道会包括 $unwind 和 $project：

    { $unwind: "$items "},
    { $project: { emits: { key: { "$items.sku" }, value: 1 } } },

  • $project 中的 emits 字段可能被命名为其他名称。为进行直观比较，我们选择了字段名称 emits。

Map-Reduce
聚合管道 (Aggregation Pipeline)
db.collection.mapReduce(
<mapFunction>,
<reduceFunction>,
{
query: <queryFilter>,
sort: <sortOrder>,
limit: <number>,
finalize: <finalizeFunction>,
out: <collection>
}
)
db.collection.aggregate( [
{ $match: <queryFilter> },
{ $sort: <sortOrder> },
{ $limit: <number> },
{ $project: { emits: { k: <expression>, v: <expression> } } },
{ $unwind: "$emits" },
{ $group: {
_id: "$emits.k"},
value: { $accumulator: {
init: <initCode>,
accumulate: <reduceFunction>,
accumulateArgs: [ "$emit.v"],
merge: <reduceFunction>,
finalize: <finalizeFunction>,
lang: "js" }}
} },
{ $out: <collection> }
] )
db.collection.mapReduce(
<mapFunction>,
<reduceFunction>,
{
query: <queryFilter>,
sort: <sortOrder>,
limit: <number>,
finalize: <finalizeFunction>,
out: { replace: <collection>, db:<db> }
}
)
db.collection.aggregate( [
{ $match: <queryFilter> },
{ $sort: <sortOrder> },
{ $limit: <number> },
{ $project: { emits: { k: <expression>, v: <expression> } } },
{ $unwind: "$emits" },
{ $group: {
_id: "$emits.k"},
value: { $accumulator: {
init: <initCode>,
accumulate: <reduceFunction>,
accumulateArgs: [ "$emit.v"],
merge: <reduceFunction>,
finalize: <finalizeFunction>,
lang: "js" }}
} },
{ $out: { db: <db>, coll: <collection> } }
] )
db.collection.mapReduce(
<mapFunction>,
<reduceFunction>,
{
query: <queryFilter>,
sort: <sortOrder>,
limit: <number>,
finalize: <finalizeFunction>,
out: { merge: <collection>, db: <db> }
}
)
db.collection.aggregate( [
{ $match: <queryFilter> },
{ $sort: <sortOrder> },
{ $limit: <number> },
{ $project: { emits: { k: <expression>, v: <expression> } } },
{ $unwind: "$emits" },
{ $group: {
_id: "$emits.k"},
value: { $accumulator: {
init: <initCode>,
accumulate: <reduceFunction>,
accumulateArgs: [ "$emit.v"],
merge: <reduceFunction>,
finalize: <finalizeFunction>,
lang: "js" }}
} },
{ $merge: {
into: { db: <db>, coll: <collection>},
on: "_id"
whenMatched: "replace",
whenNotMatched: "insert"
} },
] )
db.collection.mapReduce(
<mapFunction>,
<reduceFunction>,
{
query: <queryFilter>,
sort: <sortOrder>,
limit: <number>,
finalize: <finalizeFunction>,
out: { reduce: <collection>, db: <db> }
}
)
db.collection.aggregate( [
{ $match: <queryFilter> },
{ $sort: <sortOrder> },
{ $limit: <number> },
{ $project: { emits: { k: <expression>, v: <expression> } } },
{ $unwind: "$emits" },
{ $group: {
_id: "$emits.k"},
value: { $accumulator: {
init: <initCode>,
accumulate: <reduceFunction>,
accumulateArgs: [ "$emit.v"],
merge: <reduceFunction>,
finalize: <finalizeFunction>,
lang: "js" }}
} },
{ $merge: {
into: { db: <db>, coll: <collection> },
on: "_id"
whenMatched: [
{ $project: {
value: { $function: {
body: <reduceFunction>,
args: [
"$_id",
[ "$value", "$$new.value" ]
],
lang: "js"
} }
} }
]
whenNotMatched: "insert"
} },
] )
db.collection.mapReduce(
<mapFunction>,
<reduceFunction>,
{
query: <queryFilter>,
sort: <sortOrder>,
limit: <number>,
finalize: <finalizeFunction>,
out: { inline: 1 }
}
)
db.collection.aggregate( [
{ $match: <queryFilter> },
{ $sort: <sortOrder> },
{ $limit: <number> },
{ $project: { emits: { k: <expression>, v: <expression> } } },
{ $unwind: "$emits" },
{ $group: {
_id: "$emits.k"},
value: { $accumulator: {
init: <initCode>,
accumulate: <reduceFunction>,
accumulateArgs: [ "$emit.v"],
merge: <reduceFunction>,
finalize: <finalizeFunction>,
lang: "js" }}
} }
] )

示例

您可以使用聚合管道运算符（例如 $group、$merge 等）重写各种 map-reduce 表达式，而无需使用自定义函数。但为进行说明，以下示例提供了两种替代方案。

示例 1

针对 orders 集合的以下 map-reduce 操作会按 cust_id 进行分组，并为每个 cust_id 计算 price 之和

var mapFunction1 = function() {
   emit(this.cust_id, this.price);
};
var reduceFunction1 = function(keyCustId, valuesPrices) {
   return Array.sum(valuesPrices);
};
db.orders.mapReduce(
   mapFunction1,
   reduceFunction1,
   { out: "map_reduce_example" }
)

替代方案 1：（推荐）您可将此操作重写为聚合管道，而无需将 map-reduce 函数转换为等效的管道阶段：

db.orders.aggregate([
   { $group: { _id: "$cust_id", value: { $sum: "$price" } } },
   { $out: "agg_alternative_1" }
])

替代方案 2：（仅用于说明）通过使用 $accumulator 来定义自定义函数，以下聚合管道提供对各种 map-reduce 函数的转换：

db.orders.aggregate( [
   { $project: { emit: { key: "$cust_id", value: "$price" } } },  // equivalent to the map function
   { $group: {                                                    // equivalent to the reduce function
         _id: "$emit.key",
         valuesPrices: { $accumulator: {
                     init: function() { return 0; },
                     initArgs: [],
                     accumulate: function(state, value) { return state + value; },
                     accumulateArgs: [ "$emit.value" ],
                     merge: function(state1, state2) { return state1 + state2; },
                     lang: "js"
         } }
   } },
   { $out: "agg_alternative_2" }
] )

  1. 首先，$project 阶段会输出包含 emit 字段的文档。emit 字段是包含以下字段的文档：

    • key 包含该文档的 cust_id 值

    • value 包含该文档的 price 值

    { "_id" : 1, "emit" : { "key" : "Ant O. Knee", "value" : 25 } }
    { "_id" : 2, "emit" : { "key" : "Ant O. Knee", "value" : 70 } }
    { "_id" : 3, "emit" : { "key" : "Busby Bee", "value" : 50 } }
    { "_id" : 4, "emit" : { "key" : "Busby Bee", "value" : 25 } }
    { "_id" : 5, "emit" : { "key" : "Busby Bee", "value" : 50 } }
    { "_id" : 6, "emit" : { "key" : "Cam Elot", "value" : 35 } }
    { "_id" : 7, "emit" : { "key" : "Cam Elot", "value" : 25 } }
    { "_id" : 8, "emit" : { "key" : "Don Quis", "value" : 75 } }
    { "_id" : 9, "emit" : { "key" : "Don Quis", "value" : 55 } }
    { "_id" : 10, "emit" : { "key" : "Don Quis", "value" : 25 } }

  2. 然后，$group 会使用 $accumulator 运算符添加发出的值：

    { "_id" : "Don Quis", "valuesPrices" : 155 }
    { "_id" : "Cam Elot", "valuesPrices" : 60 }
    { "_id" : "Ant O. Knee", "valuesPrices" : 95 }
    { "_id" : "Busby Bee", "valuesPrices" : 125 }

  3. 最后，$out 在集合 agg_alternative_2 中写入输出结果。或者，你可以使用 $merge 代替 $out。

示例 2

针对 orders 集合的以下 map-reduce 操作会按 item.sku 字段进行分组，并为每个 sku 计算订单数和订购总量。然后，此操作会为每个 sku 值计算每个订单的平均数量，并将计算结果合并到输出集合中。

var mapFunction2 = function() {
      for (var idx = 0; idx < this.items.length; idx++) {
         var key = this.items[idx].sku;
         var value = { count: 1, qty: this.items[idx].qty };
         emit(key, value);
      }
};
var reduceFunction2 = function(keySKU, countObjVals) {
   reducedVal = { count: 0, qty: 0 };
   for (var idx = 0; idx < countObjVals.length; idx++) {
         reducedVal.count += countObjVals[idx].count;
         reducedVal.qty += countObjVals[idx].qty;
   }
   return reducedVal;
};
var finalizeFunction2 = function (key, reducedVal) {
   reducedVal.avg = reducedVal.qty/reducedVal.count;
   return reducedVal;
};
db.orders.mapReduce(
   mapFunction2,
   reduceFunction2,
   {
      out: { merge: "map_reduce_example2" },
      query: { ord_date: { $gte: new Date("2020-03-01") } },
      finalize: finalizeFunction2
   }
   );

替代方案 1：（推荐）您可将此操作重写为聚合管道，而无需将 map-reduce 函数转换为等效的管道阶段：

db.orders.aggregate( [
   { $match: { ord_date: { $gte: new Date("2020-03-01") } } },
   { $unwind: "$items" },
   { $group: { _id: "$items.sku", qty: { $sum: "$items.qty" }, orders_ids: { $addToSet: "$_id" } }  },
   { $project: { value: { count: { $size: "$orders_ids" }, qty: "$qty", avg: { $divide: [ "$qty", { $size: "$orders_ids" } ] } } } },
   { $merge: { into: "agg_alternative_3", on: "_id", whenMatched: "replace",  whenNotMatched: "insert" } }
] )

替代方案 2：（仅用于说明）通过使用 $accumulator 来定义自定义函数，以下聚合管道提供对各种 map-reduce 函数的转换：

db.orders.aggregate( [
      { $match: { ord_date: {$gte: new Date("2020-03-01") } } },
      { $unwind: "$items" },
      { $project: { emit: { key: "$items.sku", value: { count: { $literal: 1 }, qty: "$items.qty" } } } },
      { $group: {
            _id: "$emit.key",
            value: { $accumulator: {
               init: function() { return { count: 0, qty: 0 }; },
               initArgs: [],
               accumulate: function(state, value) {
                  state.count += value.count;
                  state.qty += value.qty;
                  return state;
               },
               accumulateArgs: [ "$emit.value" ],
               merge: function(state1, state2) {
                  return { count: state1.count + state2.count, qty: state1.qty + state2.qty };
               },
               finalize: function(state) {
                  state.avg = state.qty / state.count;
                  return state;
               },
               lang: "js"}
            }
      } },
      { $merge: {
         into: "agg_alternative_4",
         on: "_id",
         whenMatched: "replace",
         whenNotMatched: "insert"
      } }
] )

  1. $match 阶段仅选择 ord_date 大于等于 new Date("2020-03-01") 的文档。

  2. $unwind 阶段按 items 数组字段对文档进行分解，为每个数组元素输出一个文档。例如：

    { "_id" : 1, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-01T00:00:00Z"), "price" : 25, "items" : { "sku" : "oranges", "qty" : 5, "price" : 2.5 }, "status" : "A" }
    { "_id" : 1, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-01T00:00:00Z"), "price" : 25, "items" : { "sku" : "apples", "qty" : 5, "price" : 2.5 }, "status" : "A" }
    { "_id" : 2, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 70, "items" : { "sku" : "oranges", "qty" : 8, "price" : 2.5 }, "status" : "A" }
    { "_id" : 2, "cust_id" : "Ant O. Knee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 70, "items" : { "sku" : "chocolates", "qty" : 5, "price" : 10 }, "status" : "A" }
    { "_id" : 3, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 50, "items" : { "sku" : "oranges", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 3, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-08T00:00:00Z"), "price" : 50, "items" : { "sku" : "pears", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 4, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-18T00:00:00Z"), "price" : 25, "items" : { "sku" : "oranges", "qty" : 10, "price" : 2.5 }, "status" : "A" }
    { "_id" : 5, "cust_id" : "Busby Bee", "ord_date" : ISODate("2020-03-19T00:00:00Z"), "price" : 50, "items" : { "sku" : "chocolates", "qty" : 5, "price" : 10 }, "status" : "A" }
    ...

  3. $project 阶段会输出包含 emit 字段的文档。emit 字段是包含以下字段的文档：

    • key 包含 items.sku 值

    • value 包含具有 qty 值和 count 值的文档

    { "_id" : 1, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 5 } } }
    { "_id" : 1, "emit" : { "key" : "apples", "value" : { "count" : 1, "qty" : 5 } } }
    { "_id" : 2, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 8 } } }
    { "_id" : 2, "emit" : { "key" : "chocolates", "value" : { "count" : 1, "qty" : 5 } } }
    { "_id" : 3, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 10 } } }
    { "_id" : 3, "emit" : { "key" : "pears", "value" : { "count" : 1, "qty" : 10 } } }
    { "_id" : 4, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 10 } } }
    { "_id" : 5, "emit" : { "key" : "chocolates", "value" : { "count" : 1, "qty" : 5 } } }
    ...

  4. $group 使用 $accumulator 运算符会将发出的 count 和 qty 相加并计算 avg 字段：

    { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
    { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
    { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
    { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
    { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }

  5. 最后，$merge 将输出写入集合 agg_alternative_4。如果现有文档的键 _id 与新结果相同，则操作会覆盖现有文档。如果没有具有相同键的现有文档，操作将插入该文档。

提示

另请参阅：

聚合命令比较

后退

对 Reduce 进行故障排除

来年

索引