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Map-Reduce转换到聚合管道
从4.4版本开始,MongoDB添加了$accumulator$function aggregation运算符。这些运算符为用户提供了定义自定义聚合表达式的能力。使用这些操作,可以大致重写map-reduce表达式,如下表所示。
注意
可以使用聚合管道操作符(如$group、$merge等)重写各种map-reduce表达式,而不需要自定义函数。
例如,请参见map-reduce示例。

Map-Reduce到聚合管道转换表

这张表只是粗略的翻译。例如,该表显示了使用$projectmapFunction的近似转换。
    然而,mapFunction逻辑可能需要额外的阶段,例如,如果逻辑包括对数组的迭代:
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    function() {
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    this.items.forEach(function(item){ emit(item.sku, 1); });
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    }
    Copied!
    然后,聚合管道包括一个$unwind和一个$project:
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    { $unwind: "$items "},
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    { $project: { emits: { key: { "$items.sku" }, value: 1 } } },
    Copied!
    $project中的emit字段可以被命名为其他名称。为了进行可视化比较,选择了字段名称emit。
Map-Reduce
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: { 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” }} } }, { $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: { 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: [ { $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” }} } } ] )

例子

可以使用聚合管道操作符(如$group$merge等)重写各种map-reduce表达式,而不需要自定义函数。但是,为了说明目的,下面的例子提供了两种选择。

示例1

通过cust_id对订单集合组执行以下map-reduce操作,并计算每个cust_id的价格总和:
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var mapFunction1 = function() {
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emit(this.cust_id, this.price);
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};
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var reduceFunction1 = function(keyCustId, valuesPrices) {
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return Array.sum(valuesPrices);
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};
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db.orders.mapReduce(
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mapFunction1,
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reduceFunction1,
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{ out: "map_reduce_example" }
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)
Copied!
备选方案1:(推荐)您可以重写操作到聚合管道,而不将map-reduce函数转换为等效的管道阶段:
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db.orders.aggregate([
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{ $group: { _id: "$cust_id", value: { $sum: "$price" } } },
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{ $out: "agg_alternative_1" }
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])
Copied!
备选方案2:(仅为说明目的)下面的聚合管道提供了各种map-reduce函数的转换,使用$accumulator定义自定义函数:
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db.orders.aggregate( [
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{ $project: { emit: { key: "$cust_id", value: "$price" } } }, // equivalent to the map function
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{ $group: { // equivalent to the reduce function
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_id: "$emit.key",
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valuesPrices: { $accumulator: {
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init: function() { return 0; },
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initArgs: [],
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accumulate: function(state, value) { return state + value; },
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accumulateArgs: [ "$emit.value" ],
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merge: function(state1, state2) { return state1 + state2; },
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lang: "js"
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} }
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} },
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{ $out: "agg_alternative_2" }
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] )
Copied!
    1.
    首先,$project阶段输出带有emit字段的文档。emit字段是一个包含以下字段的文档:
      key包含cust_id文档的值
      value包含price文档的值
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    { "_id" : 1, "emit" : { "key" : "Ant O. Knee", "value" : 25 } }
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    { "_id" : 2, "emit" : { "key" : "Ant O. Knee", "value" : 70 } }
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    { "_id" : 3, "emit" : { "key" : "Busby Bee", "value" : 50 } }
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    { "_id" : 4, "emit" : { "key" : "Busby Bee", "value" : 25 } }
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    { "_id" : 5, "emit" : { "key" : "Busby Bee", "value" : 50 } }
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    { "_id" : 6, "emit" : { "key" : "Cam Elot", "value" : 35 } }
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    { "_id" : 7, "emit" : { "key" : "Cam Elot", "value" : 25 } }
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    { "_id" : 8, "emit" : { "key" : "Don Quis", "value" : 75 } }
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    { "_id" : 9, "emit" : { "key" : "Don Quis", "value" : 55 } }
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    { "_id" : 10, "emit" : { "key" : "Don Quis", "value" : 25 } }
    Copied!
    2.
    然后,$group使用$accumulator操作符来添加发出的值:
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    { "_id" : "Don Quis", "valuesPrices" : 155 }
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    { "_id" : "Cam Elot", "valuesPrices" : 60 }
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    { "_id" : "Ant O. Knee", "valuesPrices" : 95 }
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    { "_id" : "Busby Bee", "valuesPrices" : 125 }
    Copied!
    3.
    最后,$out将输出写入集合agg_alternative_2。或者,您可以使用$merge而不是$out

示例2

以下字段对orders集合组的map-reduce操作,item.sku并计算每个sku的订单数量和总订购量。然后,该操作将为每个sku值计算每个订单的平均数量,并将结果合并到输出集合中。
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var mapFunction2 = function() {
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for (var idx = 0; idx < this.items.length; idx++) {
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var key = this.items[idx].sku;
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var value = { count: 1, qty: this.items[idx].qty };
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emit(key, value);
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}
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};
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var reduceFunction2 = function(keySKU, countObjVals) {
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reducedVal = { count: 0, qty: 0 };
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for (var idx = 0; idx < countObjVals.length; idx++) {
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reducedVal.count += countObjVals[idx].count;
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reducedVal.qty += countObjVals[idx].qty;
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}
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return reducedVal;
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};
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var finalizeFunction2 = function (key, reducedVal) {
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reducedVal.avg = reducedVal.qty/reducedVal.count;
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return reducedVal;
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};
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db.orders.mapReduce(
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mapFunction2,
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reduceFunction2,
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{
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out: { merge: "map_reduce_example2" },
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query: { ord_date: { $gte: new Date("2020-03-01") } },
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finalize: finalizeFunction2
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}
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);
Copied!
备选方案1:(推荐)您可以重写操作到聚合管道,而不将map-reduce函数转换为等效的管道阶段:
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db.orders.aggregate( [
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{ $match: { ord_date: { $gte: new Date("2020-03-01") } } },
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{ $unwind: "$items" },
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{ $group: { _id: "$items.sku", qty: { $sum: "$items.qty" }, orders_ids: { $addToSet: "$_id" } } },
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{ $project: { value: { count: { $size: "$orders_ids" }, qty: "$qty", avg: { $divide: [ "$qty", { $size: "$orders_ids" } ] } } } },
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{ $merge: { into: "agg_alternative_3", on: "_id", whenMatched: "replace", whenNotMatched: "insert" } }
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] )
Copied!
备选方案2:(仅为说明目的)下面的聚合管道提供了各种map-reduce函数的转换,使用$accumulator定义自定义函数:
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db.orders.aggregate( [
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{ $match: { ord_date: {$gte: new Date("2020-03-01") } } },
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{ $unwind: "$items" },
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{ $project: { emit: { key: "$items.sku", value: { count: { $literal: 1 }, qty: "$items.qty" } } } },
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{ $group: {
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_id: "$emit.key",
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value: { $accumulator: {
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init: function() { return { count: 0, qty: 0 }; },
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initArgs: [],
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accumulate: function(state, value) {
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state.count += value.count;
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state.qty += value.qty;
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return state;
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},
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accumulateArgs: [ "$emit.value" ],
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merge: function(state1, state2) {
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return { count: state1.count + state2.count, qty: state1.qty + state2.qty };
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},
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finalize: function(state) {
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state.avg = state.qty / state.count;
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return state;
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},
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lang: "js"}
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}
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} },
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{ $merge: {
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into: "agg_alternative_4",
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on: "_id",
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whenMatched: "replace",
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whenNotMatched: "insert"
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} }
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] )
Copied!
    1.
    $match阶段只选择那些ord_date大于或等于new Date("2020-03-01")的文档。
    2.
    $unwinds阶段按items数组字段分解文档,为每个数组元素输出一个文档。例如:
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    { "_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" }
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    { "_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" }
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    { "_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" }
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    { "_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" }
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    { "_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" }
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    { "_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" }
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    { "_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" }
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    { "_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" }
    9
    ...
    Copied!
    3.
    $project阶段输出带有emit字段的文档。emit字段是一个包含以下字段的文档:
      key包含items.sku
      value包含具有qty值和count值的文档
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    { "_id" : 1, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 5 } } }
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    { "_id" : 1, "emit" : { "key" : "apples", "value" : { "count" : 1, "qty" : 5 } } }
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    { "_id" : 2, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 8 } } }
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    { "_id" : 2, "emit" : { "key" : "chocolates", "value" : { "count" : 1, "qty" : 5 } } }
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    { "_id" : 3, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 10 } } }
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    { "_id" : 3, "emit" : { "key" : "pears", "value" : { "count" : 1, "qty" : 10 } } }
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    { "_id" : 4, "emit" : { "key" : "oranges", "value" : { "count" : 1, "qty" : 10 } } }
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    { "_id" : 5, "emit" : { "key" : "chocolates", "value" : { "count" : 1, "qty" : 5 } } }
    9
    ...
    Copied!
    4.
    $group使用$accumulator操作符来添加发出的计数和数量,并计算avg字段:
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    { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
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    { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
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    { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
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    { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
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    { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }
    Copied!
    5.
    最后,$merge将输出写入集合agg_alternative_4。如果现有文档具有与新结果相同的键_id,则操作将覆盖现有文档。如果没有具有相同密钥的现有文档,操作将插入该文档。
也可以看看 聚合命令比较
译者:李冠飞
校对:
最近更新 1yr ago