How to Install MongoDB on Windows 10

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Conventional relational database management systems make it tough to scale data warehousing, grid, web 2.0, and cloud applications. Their non-linear question execution time, unstable question plans, and static schemas are a huge disadvantage and make data management a strenuous task. As a result, the servers fail to update and retrieve data as per expectations. MongoDB, a document-oriented database server, solves all these problems by offering unbeatable service at speeds that modern technologies demand.

MongoDB is a popular distributed database that supports replication, horizontal partitioning (sharding), a flexible document schema, and ACID guaranteed on the document level. Moreover, the support for Ad-Hoc queries, flexibilities, auto sharding and auto-failover, schema-free migration, effective horizontal scalability, and access to professional technical support makes the database the most favourite among programmers worldwide.

Prerequisites

The hardware and the software requirements you need for installing MongoDB are stated below:

Hardware requirements

1. RAM 4GB
2. CPU Intel Core i3TM i3 HQ CPU @2.50 GHz
3. ROM 256 GB

Software requirements

1. Any browser like Google Chrome, Mozilla Firefox, or Microsoft Edge.

Installation Procedure

Let us look at the step-by-step approach on how to install, set-up, and configure MongoDB in Windows 10.

Now go to the official MongoDB site and click on Community Server as shown:

In the Version dropdown, select the version of MongoDB to download as shown:

a. In the Platform dropdown, select Windows.
b. In the Package dropdown, select msi. (Mark it as the most crucial step.)

Once you have successfully downloaded MongoDB on your system, it's time to specify where you want to store the set-up code. Specify the location you want to keep the MongoDB Windows installer package. For example, here we are storing the package in the Local Disc (F:) in our system.

Step-2: Run the MongoDB installer.

Now, its time to run the application in your system. Run the application by following the simple steps:

b. Double-click the .msi file.

Step-3: Click on Next

You will get a welcome message from the MongoDB Community Edition installation wizard. The set-up wizard will guide you through the installation of MongoDB in your system. To continue with the installation process, click on Next.

Adhere to the terms of using the software. Read the end-user license agreement carefully before proceeding further.

Step-5: Accept the terms

Once you have read the terms and conditions mentioned by the MongoDB community, click on the square box as shown below. Further, click on the Next button to continue.

Step-6: Choose Setup Type – Complete

Once you land on the choose set-up type page, choose the option as Complete and then click the Next button.

Attention: MongoDB allows you to choose either the Complete (recommended for most users) or Custom (to customize the set-up type you desire) buttons.

Step-7: Keep Service Configuration as default.

• N.B.: Don’t make any changes here and keep all these parameters as default. Click on the Next button.
• Data directory: Select the data directory, which leads you to the --dbpath. In case this directory does not exist, the installer will create the directory automatically and set the directory access to the service user.
• Log Directory: Select the Log directory, which is similar to the --log path. If the directory does not exist, the installer will create the directory automatically and set the directory access to the service user. All this is done automatically.

Install MongoDB as a Service.

• Service Name: MongoDB
• Data Directory: C:\Program Files\MongoDB\Server\4.4\data\
• Log Directory: C:\Program Files\MongoDB\Server\4.4\log
• Attention: Note that if you already have a customized name service, you must choose another name.

Run the service as a local or domain user.

For an existing local user account, specify a period (i.e..) for the Account Domain and specify the Account Name and the Account Password for the user.

For an existing domain user, specify the Account Domain, the Account Name, and the Account Password for that user.

Step-9: Click on Install

Once you land on the Ready to Install MongoDB page, click on Install as shown below. Once done, the MongoDB installation begins.

The installation now starts. Based on your system speed, the installation may take a few minutes, and you are expected to get an image as shown:

Step-10: Click the Finish button.

The finish button will exit the Setup Wizard. On clicking the finish button, the installation gets completed.

Congratulations! You have successfully installed MongoDB and set up MongoDB in your local system.

What’s Next?

Go to file location of MongoDB and copy the path of MongoDB bin directory as shown below:

• Open Windows Command Prompt

Now type cd to change the directory path to the desired path as shown below:
cd c:\Program Files\MongoDB\Server\4.4

After typing mongo, press enter. You will get to see the image below:

To check the databases, type show databases as shown below:

If you want to know the admin, type use admin as shown below:

To check the collection in the database, type show collections as shown below:

Checking the Version

Attention: You may have noticed that we have not yet specified the path for our environment variable, which is why we have to manually type C:\Program Files\MongoDB\Server\4.4 to run MongoDB in our system.

Setting the path to Environmental Variable

Step-1: Go to my computer, right-click and choose the option as properties.

Step-2: Choose the advanced system settings option in the top left as shown:

Step-3: Now go to Environment variables as shown below:

Step-4: Go to system variables and choose the option path as shown below:

Step-5: Add the C:\Program Files\MongoDB\Server\4.4 and press OK.

Now you can see, we are able to run MongoDB without bringing the C:\Program Files\MongoDB\Server\4.4

Creating a demo database:

Till now we have seen how to set up and install MongoDB in our system. Now let us see a demo on how to create a database in MongoDB.

Step-1: Go to the command prompt and type mongo.

Step-2: After opening MongoDB prompt, create a new database with the name KnowledgeHut Global using the following command:

Use KnowledgeHut Global and add data into the collection.

Step-3: Showing database in the collection

show collections;

Uninstallation Procedure

Let us see how to uninstall MongoDB from the system. To uninstall MongoDB from our system, we need to follow the following steps as discussed below:
Step-1: Go to the control panel.
Step-2: Choose the option: Uninstall a program.

Step-3: Select MongoDB from the list.

Step-4: Right Click on MongoDB and you will find three options as shown below:

Step-5: Click on uninstall and complete the uninstallation process.

That’s all! You will have successfully uninstalled MongoDB from Windows 10.

Let’s Verify:

Go to your Windows command prompt and run the command to check the version of MongoDB in your system.

mongod --version

You will get an error message because you have uninstalled MongoDB from the system.

Conclusion

MongoDB has unparalleled and unique advantages over SQL-based databases because it allows data replication to mirror servers with full flexibility and reliability. Plus, the installation process is not complicated and can be completed with ease by following the step-by-step instructions mentioned above.

Just keep in mind that the MongoDB installers come in both 32-bit and 64-bit format. Based on your work requirement and your system configuration, choose the format to cater to your database needs. If your work involves more testing and development, then a 32-bit installer is definitely recommended. However, the 32-bit installer is not a perfect choice for production environments as this type of format limits the amount of data stored in the database. Perhaps, you must opt for a 64-bit installer in your system to get the most of MongoDB.

KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

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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. Horizontal scaling properties of NoSQL databases allow them to store and process large amounts of data.These are intended for storing, retrieving, and managing document-oriented data, which is frequently stored in JSON format (JavaScript Object Notation). Document databases, unlike RDBMSs, have a flexible schema that is defined by the contents of the documents.MongoDB is one of the most widely used open-source NoSQL document databases. MongoDB is known as a 'schemaless' database because it does not impose a specific structure on documents in a collection.MongoDB is compatible with a number of popular programming languages. It also offers a high level of operational flexibility because it scales well horizontally, allowing data to be spread or 'sharded' across multiple commodity servers with the ability to add more servers as needed. 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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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
7344
Implementing MongoDb Map Reduce using Aggregation

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