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Introduction
MapReduce is a data processing technique that condenses vast amounts of data into meaningful aggregated output. MongoDB includes the mapReduce database command for MapReduce operations. This article clearly describes the operations behind MongoDB MapReduce.
Head over to the article below to gain insights on MapReduce in Mongodb and its complete example implementation.
Advantages of MongoDB
Some of the benefits of MongoDB in Computer Science are given below:
MongoDB is substantially faster because it mostly stores working temporary datasets in internal memory.
MongoDB is a database that stores documents without schema. Scaling up and down is extremely simple.
For searching the stored data, MongoDB supports field, range-based queries, regular expressions, or regex, among other options.
By employing Sharding, we can balance the load in MongoDB. Sharding is used to horizontally grow the database.
MapReduce, the aggregation pipeline, and single-goal aggregation commands are just a few of the techniques that MongoDB offers to perform aggregate operations on the data.
What is MongoDB MapReduce?
In MongoDB, the Map-Reduce technique of programming enables you to handle big data sets. Map-reduce operations are carried out using the MapReduce() function. The two primary functions of this function are map and reduce. The map function can be used to group all the data based on a single key value, and the reduce function can be used to conduct actions on this grouped data.
The MapReduce() method performs well on large data collections. Data can be aggregated with MapReduce utilizing key-based operations like max, average, and group in SQL. In order to produce a new collection, each data set is individually mapped, reduced, and linked in a function.
Syntax and Parameter Description in MongoDB MapReduce
Simplified Syntax:
collection_name.mapReduce (function (Map function), function (Reduce function))
Descriptive Syntax:
collection_name.mapReduce (
function () {emit(key(Key value of map function), value);},
function (key, values) {return reduceFunction (Reduce function for MapReduce command)}, {
out: <collection>,
query: <document>,
sort: <document>,
limit: <number>,
finalize: <function>,
scope: < document>,
jsMode: <boolean>,
verbose: <boolean>,
bypassDocumentValidation: <boolean>
})
A description of the various parameters used is given below:
S.No.
Parameter Name
Description
1
collection_name
Documents retrieved from the collection using the MapReduce command are defined as the collection name. The MapReduce method in MongoDB allows us to process huge quantities of data.
2
MapReduce
Documents retrieved from the collection using the MapReduce command are defined as the collection name. The MapReduce method in MongoDB allows us to process huge quantities of data.
3
options
Options are listed as an extra parameter that will be used with this MapReduce command.
4
out
Out specifies where the MapReduce operation's results will be stored in MongoDB. We can set output as a primary member, but we can only set an inline output for secondary members.
5
query
In MongoDB, a query is defined as the selection criterion for a document. We must specify the MapReduce choose criterion in MongoDB using the query.
6
sort
This is used to sort the documents from collections. This option is primarily helpful for MongoDB optimization when utilizing the MapReduce approach.
7
limit
Limit is a designated technique that uses the MapReduce method to restrict the number of documents in the input.
8
finalize
It is a method in MongoDB with an optional parameter. It uses the reduction method and modifies the output.
9
scope
The MapReduce method's scope parameter is used to specify which global variables were available from the map.
10
jsMode
It will state whether or not the data will be converted into BSON format when the functions are executed.
11
verbose
Verbose in the MapReduce command has a default setting of false. The timing details will be disclosed.
12
collation
In MongoDB, the MapReduce method's optional parameter is the collation. The collation to be used for MapReduce operations will be specified.
Steps for Implementing MapReduce in Mongo Shell
The following steps are necessary to implement MapReduce in the Mongo shell:
Define the map function in order to process each statement from the collection.
Create a reduce function to remove a single item from the values returned by the MongoDB MapReduce process.
After developing a map and reduce function, we used it to carry out the map-reduce process.
Use the necessary command to check the result of the MapReduce command.
Example of MapReduce
Build a MongoDB database and collection using the given command.
The map-reduce function is first used to query the collection, and the output documents are then mapped to create the key-value pairs. We will now count the number of blogs that an author has.
db.runCommand({
mapReduce: "articles",
map: function () {
for (let i = 0; i < this.authors.length; ++i) {
let author = this.authors[i];
emit(author.firstName + " " + author.lastName, 1);
}
},
reduce: function (author, counters) {
count = 0;
for (let i = 0; i < counters.length; ++i) {
count += counters[i];
}
return count;
},
out: { inline: 1 },
});
Thus, we can use MongoDB's MapReduce function in this manner to process enormous amounts of data.
Frequently Asked Questions
Does MongoDB use MapReduce?
Yes, MongoDB supports MapReduce for data aggregation and transformation, but its aggregation framework is generally preferred for efficiency and simplicity.
What is the difference between MapReduce and aggregation in MongoDB?
MapReduce is JavaScript-based and flexible but slower, while aggregation is a pipeline-based framework offering better performance and simpler syntax for common tasks.
What is MapReduce in a database?
MapReduce is a programming model for processing and aggregating large datasets by mapping data to key-value pairs and reducing them into results.
Conclusion
We have learned how to use the MongoDB MapReduce function in Mongo Shell. We have seen the syntax and parameter description of the MapReduce function, along with a simple and illustrative example.
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