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Map-Reduce to Aggregation Pipeline

An aggregation pipeline provides better performance and usability than a map-reduce operation.

Map-reduce operations can be rewritten using aggregation pipeline operators, such as $group, $merge, and others.

For map-reduce operations that require custom functionality, MongoDB provides the $accumulator and $function aggregation operators starting in version 4.4. Use these operators to define custom aggregation expressions in JavaScript.

Map-reduce expressions can be re-written as shown in the following sections.

The table is only an approximate translation. For instance, the table shows an approximate translation of mapFunction using the $project.

  • However, the mapFunction logic may require additional stages, such as if the logic includes iteration over an array:

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

    Then, the aggregation pipeline includes an $unwind and a $project:

    { $unwind: "$items "},
    { $project: { emits: { key: { "$items.sku" }, value: 1 } } },
  • The emits field in $project may be named something else. For visual comparison, the field name emits was chosen.
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: { 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" }}
} }
] )

Various map-reduce expressions can be rewritten using aggregation pipeline operators, such as $group, $merge, and others, without requiring custom functions. However, for illustrative purposes, the following examples provide both alternatives.

The following map-reduce operation on the orders collection groups by the cust_id, and calculates the sum of the price for each cust_id:

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" }
)

Alternative 1: (Recommended) You can rewrite the operation into an aggregation pipeline without translating the map-reduce function to equivalent pipeline stages:

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

Alternative 2: (For illustrative purposes only) The following aggregation pipeline provides a translation of the various map-reduce functions, using $accumulator to define custom functions:

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. First, the $project stage outputs documents with an emit field. The emit field is a document with the fields:

    • key that contains the cust_id value for the document
    • value that contains the price value for the document
    { "_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. Then, the $group uses the $accumulator operator to add the emitted values:

    { "_id" : "Don Quis", "valuesPrices" : 155 }
    { "_id" : "Cam Elot", "valuesPrices" : 60 }
    { "_id" : "Ant O. Knee", "valuesPrices" : 95 }
    { "_id" : "Busby Bee", "valuesPrices" : 125 }
  3. Finally, the $out writes the output to the collection agg_alternative_2. Alternatively, you could use $merge instead of $out.

The following map-reduce operation on the orders collection groups by the item.sku field and calculates the number of orders and the total quantity ordered for each sku. The operation then calculates the average quantity per order for each sku value and merges the results into the output collection.

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
}
);

Alternative 1: (Recommended) You can rewrite the operation into an aggregation pipeline without translating the map-reduce function to equivalent pipeline stages:

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" } }
] )

Alternative 2: (For illustrative purposes only) The following aggregation pipeline provides a translation of the various map-reduce functions, using $accumulator to define custom functions:

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. The $match stage selects only those documents with ord_date greater than or equal to new Date("2020-03-01").
  2. The $unwind stage breaks down the document by the items array field to output a document for each array element. For example:

    { "_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. The $project stage outputs documents with an emit field. The emit field is a document with the fields:

    • key that contains the items.sku value
    • value that contains a document with the qty value and a count value
    { "_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. The $group uses the $accumulator operator to add the emitted count and qty and calculate the avg field:

    { "_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. Finally, the $merge writes the output to the collection agg_alternative_4. If an existing document has the same key _id as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.
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