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:

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

    然后,聚合管道包括一个$unwind和一个$project:

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

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" }
)

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:

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

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:

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

    { "_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,$group使用$accumulator操作符来添加发出的值:

    { "_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。

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
}
);

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:

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" } }
] )

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:

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:

    { "_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

    { "_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操作符来添加发出的计数和数量,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

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