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How to Create MongoDB Collections

In MongoDB, data is stored in the form of documents. These documents are stored inside collections and collections are placed inside database. A database in MongoDB can have several collections. Collections in MongoDB can be created implicitly while we are importing the data or explicitly by specifying the ‘createCollection’ command. Usually the implicit creation is the most common method but for instances where we need to create a capped collection or collection with schema validation rules explicit method is used. In this article, we will explore both the implicit and explicit methods to create acollection.  Implicit Creation In this method, we do not specify the create collection command. The collection is created automatically when data is imported from the application. This is the most common method used for creating collection in MongoDB. We can insert the data from the mongo shell or directly from the application. Example 1: Create a collection named implicit through the mongo shell.In the above example we have inserted one row inside the implicit collection which was not present before the document insertion. As the row is inserted we can see a new collection is created. This collection can now be used for future data insertion and is persistent. Example 2: Create a collection named application through python application. Steps:  First,We need to establish connection to themongodb server.  conn = MongoClient(‘host’, port-number)  Here, host and port will be of the server hosting mongodb.  Switch to the database where we want to import data.  db = conn.databasename  Create a collection object  Collection = db.application  Insert data  Collection.insert_one({“name” : “implicit”}) This will create a collection named application and insert data inside it.Now we need to verify if the data inserted is present inside the collection.  We can see that the application collection is created inside the implicitdb database and the rows we inserted are present.Explicit Creation The explicit method to create collection is mainly used in case there is need to create special collections like capped collections or collections with document validation rules.  Syntax: db.createCollections(name,options) Below is the description of the arguments passed in the createCollection() method.ParameterTypeDescriptionnameStringName that we want to give to collection.optionsdocumentThis is optional, depending on the type of collection we want to create we can specify different options.The ‘name’ parameter is of type string while the ‘option’ parameter is document. Let’s explore the ‘option’ parameter document to see some of major options while creating collection explicitly.FieldDescriptioncappedSpecify value ‘true’ if want to create capped collectionsizeMaximum size in bytes for capped collectionmaxMaximum number of documents allowed in capped collection.StorageEngineTo specify storage engine options. Valid only for WiredTigerwriteConcernThe write concern that will be used for the collection for write operations.ValidatorAllows to specify validation rules for collectionExample: Creating a collection named ‘explicit’ explicitlyWe can see ‘explicit’ collection is created using the create eCollection command. Capped Collection Capped collections are fixed size collections that start to overwrite the oldest log entries when the size reaches the maximum limit. The oplog that is used for MongoDB replication are capped collections.  Example: Create a collection named ‘cc’ which is capped collection.In above example we have create capped collection ‘cc’ explicitly using the createCollection command. While creating the capped collection we need to specify the collection size and maximum number of documents it can have. Creating Collection with Document validation Documents with validation in MongoDB compare each insert and updates against the validation rules set for the collection. These rules are specified using validator option in the createCollection command. The row is inserted or updated if the validation is passed and rejected if the validation fails. Example: create a collection named ‘validate’ with validation rules We will create a collection named ‘validate’ with validation rules and try to insert data that does not meet the validation requirement.We can see document insertion failed as it did not pass the validation of schema defined by the validator. Access Needed to Create Collections To read and write data inside MongoDB we should have proper authentication/authorization. Authentication defines who can access the system and Authorization controls what activity can be performed by whom. MongoDB uses RBAC [Role Based Access Control] where access is provided to set of roles and then these roles are assigned to users.  Below is the access matrix for createCollection.User with built-in role readWrite can also create collection inside the database. Locking Behaviour of createCollection() command From MongoDB version 4.2 onwards db.createCollection() obtains an exclusive lock on the specified collection or view for the period of the operation. This lock is being held typically for a very short time but during this operation all other operations must wait for the lock to be released. If we are creating view additional lock will be placed on collection system.views and will block all other view creation until this command completes. In summaryIn this blog, we have seen how collection can be created in MongoDB both implicitly and explicitly. The implicit way to create collection inside the MongoDB database is the most common method, widely used by developers.  The explicit method is needed when special kinds of collections need to be created such as capped collection or if we want to place validation rules for our schema. We have also pored over the access matrix which helps developers to be provided with adequate access so that they can create collections implicitly. Finally, we saw the behaviour of locks when the collection is created. To advance your career in the much sought after MongoDB, explore the MongoDB developer course. You will learn critical aspects of MongoDB and use it to perform real-time analytics, data management, and other activities with a unique blended learning experience.  In the MongoDB administrator course, you will learn about architecture and installation, CRUD operations, schema design, data modelling and how replication works. You will also learn to use various GUI tools required to perform DBA tasks.Find easy solutions to Database management. Explore our MongoDB online workshops here.

How to Create MongoDB Collections

8K
How to Create MongoDB Collections

In MongoDB, data is stored in the form of documents. These documents are stored inside collections and collections are placed inside database. A database in MongoDB can have several collections. 

Collections in MongoDB can be created implicitly while we are importing the data or explicitly by specifying the ‘createCollection’ command. Usually the implicit creation is the most common method but for instances where we need to create a capped collection or collection with schema validation rules explicit method is used. 

In this article, we will explore both the implicit and explicit methods to create acollection.  

Implicit Creation 

In this method, we do not specify the create collection command. The collection is created automatically when data is imported from the application. This is the most common method used for creating collection in MongoDB. We can insert the data from the mongo shell or directly from the application. 

Example 1: Create a collection named implicit through the mongo shell.

In the abovehow to Create a collection named implicit through the mongo shell example we have inserted one row inside the implicit collection which was not present before the document insertion. As the row is inserted we can see a new collection is created. This collection can now be used for future data insertion and is persistent. 

Example 2: Create a collection named application through python application. 

Steps:  

First,We need to establish connection to themongodb server. 
conn = MongoClient(‘host’, port-number) 
Here, host and port will be of the server hosting mongodb. 
Switch to the database where we want to import data. 
db = conn.databasename 
Create a collection object 
Collection = db.application 
Insert data 
Collection.insert_one({“name” : “implicit”}) 

This will create a collection named application and insert data inside it.

how to Create a collection named application through python application

Now we need to verify if the data inserted is present inside the collection.  

how to Create a collection named application through python application

We can see that the application collection is created inside the implicitdb database and the rows we inserted are present.

Explicit Creation 

The explicit method to create collection is mainly used in case there is need to create special collections like capped collections or collections with document validation rules.  

Syntax

db.createCollections(name,options) 

Below is the description of the arguments passed in the createCollection() method.

ParameterTypeDescription
nameStringName that we want to give to collection.
optionsdocumentThis is optional, depending on the type of collection we want to create we can specify different options.

The ‘name’ parameter is of type string while the ‘option’ parameter is document. Let’s explore the ‘option’ parameter document to see some of major options while creating collection explicitly.

FieldDescription
cappedSpecify value ‘true’ if want to create capped collection
sizeMaximum size in bytes for capped collection
maxMaximum number of documents allowed in capped collection.
StorageEngineTo specify storage engine options. Valid only for WiredTiger
writeConcernThe write concern that will be used for the collection for write operations.
ValidatorAllows to specify validation rules for collection

Example: Creating a collection named ‘explicit’ explicitly

Creating a collection named ‘explicit’ explicitly

We can see ‘explicit’ collection is created using the create eCollection command. 

Capped Collection 

Capped collections are fixed size collections that start to overwrite the oldest log entries when the size reaches the maximum limit. The oplog that is used for MongoDB replication are capped collections.  

Example: Create a collection named ‘cc’ which is capped collection.

Create a collection named ‘cc’ which is capped collection

In above example we have create capped collection ‘cc’ explicitly using the createCollection command. While creating the capped collection we need to specify the collection size and maximum number of documents it can have. 

Creating Collection with Document validation 

Documents with validation in MongoDB compare each insert and updates against the validation rules set for the collection. These rules are specified using validator option in the createCollection command. The row is inserted or updated if the validation is passed and rejected if the validation fails. 

Example: create a collection named ‘validate’ with validation rules 

We will create a collection named ‘validate’ with validation rules and try to insert data that does not meet the validation requirement.

create a collection named ‘validate’ with validation rules

We can see document insertion failed as it did not pass the validation of schema defined by the validator. 

Access Needed to Create Collections 

To read and write data inside MongoDB we should have proper authentication/authorization. Authentication defines who can access the system and Authorization controls what activity can be performed by whom. MongoDB uses RBAC [Role Based Access Control] where access is provided to set of roles and then these roles are assigned to users.  

Below is the access matrix for createCollection.

Access matrix for createCollection

User with built-in role readWrite can also create collection inside the database. 

Locking Behaviour of createCollection() command 

From MongoDB version 4.2 onwards db.createCollection() obtains an exclusive lock on the specified collection or view for the period of the operation. This lock is being held typically for a very short time but during this operation all other operations must wait for the lock to be released. If we are creating view additional lock will be placed on collection system.views and will block all other view creation until this command completes. 

In summary

In this blog, we have seen how collection can be created in MongoDB both implicitly and explicitly. The implicit way to create collection inside the MongoDB database is the most common method, widely used by developers.  

The explicit method is needed when special kinds of collections need to be created such as capped collection or if we want to place validation rules for our schema. We have also pored over the access matrix which helps developers to be provided with adequate access so that they can create collections implicitly. Finally, we saw the behaviour of locks when the collection is created. 

To advance your career in the much sought after MongoDB, explore the MongoDB developer course. You will learn critical aspects of MongoDB and use it to perform real-time analytics, data management, and other activities with a unique blended learning experience.  

In the MongoDB administrator course, you will learn about architecture and installation, CRUD operations, schema design, data modelling and how replication works. You will also learn to use various GUI tools required to perform DBA tasks.


Find easy solutions to Database management. Explore our MongoDB online workshops here.

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Handling React Events - A Detailed Guide

Event handling essentially allows the user to inte... Read More

MongoDB Query Document Using Find() With Example

MongoDB's find() method selects documents from a collection or view and returns a cursor to those documents. There are two parameters in this formula: query and projection.Query – This is an optional parameter that specifies the criteria for selection. In simple terms, a query is what you want to search for within a collection.Projection – This is an optional parameter that specifies what should be returned if the query criteria are satisfied. In simple terms, it is a type of decision-making that is based on a set of criteria.MongoDB's Flexible SchemaA NoSQL database, which stands for "not only SQL," is a way of storing and retrieving data that is different from relational databases' traditional table structures (RDBMS).When storing large amounts of unstructured data with changing schemas, NoSQL databases are indeed a better option than RDBMS. 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MongoDB can be run on a variety of platforms, including developer laptops, private clouds, and public clouds.Querying documents using find()MongoDB queries are used to retrieve or fetch data from a MongoDB database. When running a query, you can use criteria or conditions to retrieve specific data from the database.The function db.collection is provided by MongoDB. find() is a function that retrieves documents from a MongoDB database.In MongoDB, the find method is used to retrieve a specific document from the MongoDB collection. In Mongo DB, there are a total of six methods for retrieving specific records.find()findAndModify()findOne()findOneAndDelete()findOneAndReplace()findOneAndUpdate()Syntax:find(query, projection)We can fetch a specific record using the Find method, which has two parameters. If these two parameters are omitted, the find method will return all of the documents in the MongoDB collection.Example:Consider an example of employees with the database of employee_id and employee_name and we will fetch the documents using find() method.First, create a database with the name “employees” with the following code:use employeesNow, create a collection “employee” with:db.createCollection("employee")In the next step we will insert the documents in the database:db.employee.insert([{employee_id: 101, employee_name: "Ishan"}, {employee_id: 102, employee_name: "Bhavesh"}, {employee_id: 103, employee_name: "Madan"}])Find all Documents:To get all the records in a collection, we need to use the find method with an empty parameter. In other words, when we need all the records, we will not use any parameters.db.employee.find()Output in Mongo ShellThe pretty() method can be used to display the results in a formatted manner.Syntax:db.COLLECTION_NAME.find().pretty()Let’s check our documents with pretty() method:Query FiltersWe will see examples of query operations using the db.collection.find() method in mongosh.We will use the employee collection in the employees database.db.employee.insert([{employee_id: 101, employee_name: "Ishan", age: 21, email_id: "ishanjain@gmail.com"}, {employee_id: 102, employee_name: "Bhavesh", age: 22, email_id: "bhaveshg@gmail.com"}, {employee_id: 103, employee_name: "Madan", age: 23, email_id: "madan@gmail.com"}])As we have seen earlier that to select all the documents in the database we pass an empty document as the query filter parameter to the find method.db.employee.find().pretty()Find the first document in a collection:db.employee.findOne()Find a document by ID:db.employee.findOne({_id : ObjectId("61d1ae0b56b92c20b423a5a7")})Find Documents that Match Query Criteriadb.employee.find({“age”: “22”})db.employee.find({"employee_name": "Madan"}).pretty()Sort Results by a Field:db.employee.find().sort({age: 1}).pretty()order by age, in ascending orderdb.employee.find().sort({age: -1}).pretty()order by age, in descending orderAND Conditions:A compound query can specify conditions for multiple fields in the documents in a collection. A logical AND conjunction connects the clauses of a compound query indirectly, allowing the query to select all documents in the collection that meet the specified conditions.In the following example, we will consider all the documents in the employee collection where employee_id equals 101 and age equals 21.db.employee.find({"employee_id": 101, "age": "21" }).pretty()Querying nested fieldsThe embedded or nested document feature in MongoDB is a useful feature. Embedded documents, also known as nested documents, are documents that contain other documents.You can simply embed a document inside another document in MongoDB. Documents are defined in the mongo shell using curly braces (), and field-value pairs are contained within these curly braces.Using curly braces, we can now embed or set another document inside these fields, which can include field-value pairs or another sub-document.Syntax:{ field: { field1: value1, field2: value2 } }Example:We have a database “nested” and in this database we have collection “nesteddoc”.The following documents will insert into the nesteddoc collection.db.nesteddoc.insertMany([ { "_id" : 1, "dept" : "A", "item" : { "sku" : "101", "color" : "red" }, "sizes" : [ "S", "M" ] }, { "_id" : 2, "dept" : "A", "item" : { "sku" : "102", "color" : "blue" }, "sizes" : [ "M", "L" ] }, { "_id" : 3, "dept" : "B", "item" : { "sku" : "103", "color" : "blue" }, "sizes" : "S" }, { "_id" : 4, "dept" : "A", "item" : { "sku" : "104", "color" : "black" }, "sizes" : [ "S" ] } ])Place the documents in the collection now. Also, take a look at the results:As a result, the nesteddoc collection contains four documents, each of which contains nested documents. The find() method can be used to access the collection's documents.db.nesteddoc.find()Specify Equality Condition:In this example, we will select the document from the nesteddoc query where dept equals “A”.db.nesteddoc.find({dept: "A"})Querying ArraysUse the query document {: } to specify an equality condition on an array, where is the exact array to match, including the order of the elements.The following query looks for all documents where the field tags value is an array with exactly two elements, "S" and "M," in the order specified:db.nesteddoc.find( { sizes: ["S", "M"] } )Use the $all operator to find an array that contains both the elements "S" and "M," regardless of order or other elements in the array:db.nested.find( { sizes: { $all: ["S", "M"] } } )Query an Array for an Element:The following example queries for all documents where size is an array that contains the string “S” as one of its elements:db.nesteddoc.find( { sizes: "S" } )Filter conditionsTo discuss the filter conditions, we will consider a situation that elaborates this. We will start by creating a collection with the name “products” and then add the documents to it.db.products.insertMany([ { _id: 1, item: { name: "ab", code: "123" }, qty: 15, tags: [ "A", "B", "C" ] }, { _id: 2, item: { name: "cd", code: "123" }, qty: 20, tags: [ "B" ] }, { _id: 3, item: { name: "ij", code: "456" }, qty: 25, tags: [ "A", "B" ] }, { _id: 4, item: { name: "xy", code: "456" }, qty: 30, tags: [ "B", "A" ] }, { _id: 5, item: { name: "mn", code: "000" }, qty: 20, tags: [ [ "A", "B" ], "C" ] }])To check the documents, use db.products.find():$gt$gt selects documents with a field value greater than (or equal to) the specified value.db.products.find( { qty: { $gt: “20” } } )$gte:$gte finds documents in which a field's value is greater than or equal to (i.e. >=) a specified value (e.g. value.)db.products.find( { qty: { $gte: 20 } } )$lt:$lt selects documents whose field value is less than (or equal to) the specified value.db.products.find( { qty: { $lt: 25 } } )$lte:$lte selects documents in which the field's value is less than or equal to (i.e. =) the specified value.db.products.find( { qty: { $lte: 20 } } )Query an Array by Array Length:To find arrays with a specific number of elements, use the $size operator. For example, the following selects documents with two elements in the array.db.products.find( { "tags": {$size: 2} } )ProjectionIn MongoDB, projection refers to selecting only the data that is required rather than the entire document's data. If a document has five fields and you only want to show three of them, select only three of them.The find() method in MongoDB accepts a second optional parameter, which is a list of fields to retrieve, as explained in MongoDB Query Document. When you use the find() method in MongoDB, it displays all of a document's fields. To prevent this, create a list of fields with the values 1 or 0. The value 1 indicates that the field should be visible, while 0 indicates that it should be hidden.Syntax:db.COLLECTION_NAME.find({},{KEY:1})Example:We will consider the previous example of products collection. Run the below command on mongoshell to learn how projection works:db.products.find({},{"tags":1, _id:0})Keep in mind that the _id field is always displayed while executing the find() method; if you do not want this field to be displayed, set it to 0.Optimized FindingsTo retrieve a document from a MongoDB collection, use the Find method.Using the Find method, we can retrieve specific documents as well as the fields that we require. Other find methods can also be used to retrieve specific documents based on our needs.By inserting array elements into the query, we can retrieve specific elements or documents. To retrieve data for array elements from the collection in MongoDB, we can use multiple query operators.
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MongoDB Query Document Using Find() With Example

MongoDB's find() method selects documents from a c... Read More

Implementing MongoDb Map Reduce using Aggregation

Algorithms and applications in today's data-driven market collect data about people, processes, systems, and organisations 24 hours a day, seven days a week, resulting in massive amounts of data. The problem is figuring out how to process this massive amount of data efficiently without sacrificing valuable insights.What is Map Reduce? The MapReduce programming model comes to the rescue here. MapReduce, which was first used by Google to analyse its search results, has grown in popularity due to its ability to split and process terabytes of data in parallel, generating results faster. A (Key,value) pair is the basic unit of information in MapReduce. Before feeding the data to the MapReduce model, all types of structured and unstructured data must be translated to this basic unit. The MapReduce model, as the name implies, consists of two distinct routines: the Map-function and the Reduce-function.  MapReduce is a framework for handling parallelizable problems across huge files using a huge number of devices (nodes), which are collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogeneous hardware).  When data stored in a filesystem (unstructured) or a database(structured) is processed, MapReduce can take advantage of data's locality, processing it close to where it's stored to reduce communication costs. Typically, a MapReduce framework (or system) consists of three operations: Map: Each worker node applies the map function to local data and saves the result to a temporary storage. Only one copy of the redundant input data is processed by a master node. Shuffle: worker nodes redistribute data based on output keys (produced by the map function), ensuring that all data associated with a single key is stored on the same worker node. Reduce: each group of output data is now processed in parallel by worker nodes, per key. This article will walk you through the Map-Reduce model's functionality step by step. Map Reduce in MongoDB The map-reduce operation has been deprecated since MongoDB 5.0. An aggregation pipeline outperforms a map-reduce operation in terms of performance and usability. Aggregation pipeline operators like $group, $merge, and others can be used to rewrite map-reduce operations. Starting with version 4.4, MongoDB provides the $accumulator and $function aggregation operators for map-reduce operations that require custom functionality. In JavaScript, use these operators to create custom aggregation expressions. The map and reduce functions are the two main functions here. As a result, the data is independently mapped and reduced in different spaces before being combined in the function and saved to the specified new collection. This mapReduce() function was designed to work with large data sets only. You can perform aggregation operations like max and avg on data using Map Reduce, which is similar to groupBy in SQL. It works independently and in parallel on data. Implementing Map Reduce with Mongosh (MongoDB Shell)  The db.collection.mapReduce() method in mongosh is a wrapper for the mapReduce command. The examples that follow make use of the db.collection.mapReduce(). Example: Create a collection ‘orders’ with these documents: db.orders.insertMany([     { _id: 1, cust_id: "Ishan Jain", ord_date: new Date("2021-11-01"), price: 25, items: [ { sku: "oranges", qty: 5, price: 2.5 }, { sku: "apples", qty: 5, price: 2.5 } ], status: "A" },     { _id: 2, cust_id: "Ishan Jain", ord_date: new Date("2021-11-08"), price: 70, items: [ { sku: "oranges", qty: 8, price: 2.5 }, { sku: "chocolates", qty: 5, price: 10 } ], status: "A" },     { _id: 3, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-08"), price: 50, items: [ { sku: "oranges", qty: 10, price: 2.5 }, { sku: "pears", qty: 10, price: 2.5 } ], status: "A" },     { _id: 4, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-18"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 5, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-19"), price: 50, items: [ { sku: "chocolates", qty: 5, price: 10 } ], status: "A"},     { _id: 6, cust_id: "Madan Parmar", ord_date: new Date("2021-11-19"), price: 35, items: [ { sku: "carrots", qty: 10, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },     { _id: 7, cust_id: "Madan Parmar", ord_date: new Date("2021-11-20"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 8, cust_id: "Abhresh", ord_date: new Date("2021-11-20"), price: 75, items: [ { sku: "chocolates", qty: 5, price: 10 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },     { _id: 9, cust_id: "Abhresh", ord_date: new Date("2021-11-20"), price: 55, items: [ { sku: "carrots", qty: 5, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 }, { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 10, cust_id: "Abhresh", ord_date: new Date("2021-11-23"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" }  ]) Apply a map-reduce operation to the orders collection to group them by cust_id, then add the prices for each cust_id: To process each input document, define the map function: this refers the document that the map-reduce operation is processing in the function. For each document, the function maps the price to the cust_id and outputs the cust_id and price. var mapFunction1 = function() {emit(this.cust_id, this.price);}; With the two arguments keyCustId and valuesPrices, define the corresponding reduce function: The elements of the valuesPrices array are the price values emitted by the map function, grouped by keyCustId. The valuesPrice array is reduced to the sum of its elements by this function. var reduceFunction1 = function(keyCustId, valuesPrices) {return Array.sum(valuesPrices);};Apply the mapFunction1 map function and the reduceFunction1 reduce function to all documents in the orders collection: db.orders.mapReduce(mapFunction1,reduceFunction1,{ out: "map_reduce_example" }) The results of this operation are saved in the map_reduce_example collection. If the map_reduce_example collection already exists, the operation will overwrite its contents with the map-reduce operation's results. Check the map_reduce_example collection to verify: db.map_reduce_example.find().sort( { _id: 1 } ) Aggregation Alternative:You can rewrite the map-reduce operation without defining custom functions by using the available aggregation pipeline operators: db.orders.aggregate([{$group: { _id:"$cust_id",value:{$sum: "$price" } } },{ $out: "agg_alternative_1" }]) Check the agg_alternative_1 collection to verify: db.agg_alternative_1.find().sort( { _id: 1 } )Implementing Map Reduce with Java Consider the collection car and insert the following documents in it. db.car.insert( [ {car_id:"c1",name:"Audi",color:"Black",cno:"H110",mfdcountry:"Germany",speed:72,price:11.25}, {car_id:"c2",name:"Polo",color:"White",cno:"H111",mfdcountry:"Japan",speed:65,price:8.5}, {car_id:"c3",name:"Alto",color:"Silver",cno:"H112",mfdcountry:"India",speed:53,price:4.5}, {car_id:"c4",name:"Santro",color:"Grey",cno:"H113",mfdcountry:"Sweden",speed:89,price:3.5} , {car_id:"c5",name:"Zen",color:"Blue",cno:"H114",mfdcountry:"Denmark",speed:94,price:6.5} ] ) You will get an output like this:  Let's now write the map reduce function on a collection of cars, grouping them by speed and classifying them as overspeed cars.  var speedmap = function (){  var criteria;  if ( this.speed > 70 ) {criteria = 'overspeed';emit(criteria,this.speed);}}; Based on the speed, this function classifies the vehicle as an overspeed vehicle. The term "this" refers to the current document that requires map reduction. var avgspeed_reducemap = function(key, speed) {       var total =0;       for (var i = 0; i 
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Implementing MongoDb Map Reduce using Aggregation

Algorithms and applications in today's data-driven... Read More