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Why Use MongoDB? Advantages & Use Cases

Database selection plays a significant role in overall product development. How seamlessly you can edit, update, retrieve or delete depends on the database you choose. Of the two database types – non-relational and relational databases, you must choose the best fit based on your individual needs. You would have probably heard about the most in-demand database MongoDB, which is a NoSQL and a very popular document database. In this article, we attempt to touch upon the reasons for the increased popularity of MongoDB.MongoDB, a document-based NoSQL database, is a schema-less database with compelling characteristics and salient features that allows users to query data in the most straightforward and tech-savvy way. The database supported with JSON-style storage enables users to manipulate and access data with no hassles.With more than 15 million downloads, MongoDB has become the most preferred database and is used by programmers globally. Keep on reading to know more about MongoDB, its advantages, why to use it, and where it can be used.Advantages of MongoDB over RDBMSMongoDB NoSQL databases and relational databases differ in many ways. Not only is MongoDB easy-to-use, but it also supports excellent scaling options. Moreover, the performance capabilities of MongoDB are unbeatable compared to other databases.Sounds awe-inspiring? There are other unique and unparalleled features and built-in functionalities that make MongoDB the most preferred choice among developers. Let us take a look at some of the advantages of using MongoDB over RDBMS.MongoDB is schema-less: In relational databases, we must create tables, schemas, and relations to identify and organize the data. On the other hand, MongoDB is a schema-less database that does not require the creation of tables and other rigid, pre-defined schema. As a document database, MongoDB stores all the records in a single collection.MongoDB has no joins: In RDBMS, connecting two or more tables is challenging as it requires using joins. MongoDB has no complex joins, and changing the document structure in MongoDB is easy, allowing us to connect other documents with no difficulty. No primary key set-up: When using RDBMS, setting up a primary key is necessary. In MongoDB, there is no need to explicitly create a primary key. The NoSQL database offers an _id field, created by default, with every document. This created field acts as a primary key. The reserved _id field serves as the primary key in MongoDB, and it must be a unique value. It is important to note that if there is no set up to the _id field, MongoDB fills it with "MongoDB Id Object" automatically. MongoDB Uses CAP Theorem: RDBMS focuses on ACID properties via Atomicity, Consistency, Isolation, and Durability. On the other hand, MongoDB uses CAP theorem Consistency, Availability, and Partition tolerance for database distributions. What makes MongoDB so much in-demand?Do you know why MongoDB is becoming a favourite choice among developers? Its excellent capabilities are listed below:Flexible document schemasUsing Structured Query Language databases, every time there is an attempt to insert information, declaration and confirmation of a table schema are necessary. MongoDB, being a document database, holds different documents in a single collection. Simply put, MongoDB allows the storage of multiple objects in a unified way, with a different set of fields. Flexible document schemas offer great advantages when working on complex data or handling real-time data.Code-native data accessGetting the data in the Object is often not easy. In most of the databases, you will probably have to leverage Object Relational Mappers to get this work done. MongoDB saves you from using heavy wrappers and allows you to store and access data in the most simple way. Simply put, you can have code-native data access from any programming language like dictionaries in Python, associative arrays in JavaScript, and Maps in Java.Change-friendly designMany programmers have the habit of bringing down the site or application for customizing data.  With MongoDB, there is no need to bring the site down because the changes or customizations offered using MongoDB are impressive. Any time you need to change the schema, you will not have to lose valuable downtime. MongoDB allows users to add new data anytime, anywhere – without any disturbance in its operational processes.Powerful querying and analyticsThe fact that there are no complex joins in MongoDB also adds up to a great advantage. That means MongoDB allows data accessibility seamlessly without the need to make joins. MongoDB knows how to reach into documents when performing queries. The MongoDB Query Language (MQL) supports powerful dynamic query on documents facilitating deep queries. Above all, the document database allows accessing complex data simply using one-line of JSON-like code.Easy horizontal scale-outMongoDB facilitates horizontal scaling with the help of database sharding. Since the data is structured horizontally, it becomes easy to spread it across different servers and access it in a simplified way. You can create clusters using real-time replications and shard high-volume data to sustain performance.Harness the potential of database sharding to distribute the database into several clusters. With database sharding, you will get an increased storage capacity and also quicken the query response rate.Warning Bell: When sharding a database, keep in mind that once you have completed the sharding, you cannot unshard it at any cost.Why Use MongoDB?Now that you have spotted the main differences between MongoDB and NoSQL databases, you might have understood the perks of using MongoDB. Here are a few compelling reasons to use MongoDB over other databases.Highly versatile – Being a Non-Structured Query Language, there is no need to create tables when working with MongoDB. As a result, there is an appreciable degree of versatility in storing, managing, and accessing data. Versatility adds a great advantage when storing big and uncategorized data.Impressive Speed – One of the reasons for the high demand for MongoDB is its speed. As there is no need to create a table or schema, the database speed is impressive. Using MongoDB, the CRUD (Creating, Reading, Updating, Deleting) speed is faster than other databases. A MongoDB query is 100 times quicker, allowing users to index their search in the speediest time. Easily Accessible – Another reason for using MongoDB is that it supports almost all the major programming languages C, C++, C#, Java, Node.js, Perl, PHP, Python, Ruby, Scala, and many more. Also, MongoDB has excellent community-supported drivers for low popular programming languages too. You can also host MongoDB on its cloud service, MongoDB Atlas, which offers both a community-driven open source and a premium Enterprise Edition. Easy-to-UseIf you happen to be a JavaScript developer, you will fall in love with the document database; MongoDB. Compared to other databases, MongoDB is easy to use. Even a newbie can understand the database and use it efficiently with no difficulty. As MongoDB stores each record to the Binary JSON, it becomes super easy to use the database, especially if you are using JavaScript libraries like Node.js, React, or Express.js in the backend.Where to Use MongoDB?MongoDB is an excellent choice for web applications where there is little to no user interaction. Unlike a relational database, where there is a need to store information across several tables or even create joins, MongoDB saves the extra tasks and does it implicitly. Using MongoDB, you can store user information in the most unified way. As a result, there will be a single query to a single collection, and the front-end can deal with editing the data.Integrating Big-Data – If your business involves a pool of incoming data from different sources, MongoDB will prove to be very helpful. When other databases have failed, MongoDB comes up with unique capabilities to store and integrate big data seamlessly. The one-document database provides robust capabilities to store a large amount of diverse data in the most simplified way. Defining Complex-Data – MongoDB allows embedded documents (also called Nested Documents) to define nested structures. Nested documents are documents where a document is present inside a document. It is helpful when a one-to-many relationship exists between documents. Best of all, MongoDB supports specialized data formats like geospatial format, that result in a resilient repository that remains unbroken even after edits. In a nutshell, you can use MongoDB for the following:Blogs and content managementE-commerce product catalogUser data managementFor real-time analytics and high-speed logging, and high scalabilityConfiguration managementTo maintain Geospatial dataMobile and social networking sitesAttention: It is important to note that MongoDB is not the right choice for a robust transactional system or places where the data model is upfront. Also, it is a poor decision to leverage MongoDB for tightly coupled systems. Perhaps, Structured Query Language will be the right fit!ConclusionMongoDB is a robust database with excellent capabilities and stands out in-built functions. Today, IT sectors, e-commerce, banking, logistics, and many others are managing their data flow using MongoDB. Multi-national companies like Bosch, Uber, Accenture, Barclays, to name a few, use MongoDB for storing the uncategorized data in the most sophisticated way.If you are looking for the most efficient database to store and access data seamlessly, there is no better option than MongoDB. Although the performance analysis of MongoDB is exceptionally superb, perhaps there is no transaction support, and indeed the database uses very high memory for storage. However, the striking features you get for using MongoDB should not be sacrificed for the high-memory cost.

Why Use MongoDB? Advantages & Use Cases

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Why Use MongoDB? Advantages & Use Cases

Database selection plays a significant role in overall product development. How seamlessly you can edit, update, retrieve or delete depends on the database you choose. Of the two database types – non-relational and relational databases, you must choose the best fit based on your individual needs. You would have probably heard about the most in-demand database MongoDB, which is a NoSQL and a very popular document database. In this article, we attempt to touch upon the reasons for the increased popularity of MongoDB.

MongoDB, a document-based NoSQL database, is a schema-less database with compelling characteristics and salient features that allows users to query data in the most straightforward and tech-savvy way. The database supported with JSON-style storage enables users to manipulate and access data with no hassles.

With more than 15 million downloads, MongoDB has become the most preferred database and is used by programmers globally. Keep on reading to know more about MongoDB, its advantages, why to use it, and where it can be used.

Advantages of MongoDB over RDBMS

MongoDB NoSQL databases and relational databases differ in many ways. Not only is MongoDB easy-to-use, but it also supports excellent scaling options. Moreover, the performance capabilities of MongoDB are unbeatable compared to other databases.

Sounds awe-inspiring? There are other unique and unparalleled features and built-in functionalities that make MongoDB the most preferred choice among developers. Let us take a look at some of the advantages of using MongoDB over RDBMS.

Advantages of MongoDB over RDBMS

MongoDB is schema-less: In relational databases, we must create tables, schemas, and relations to identify and organize the data. On the other hand, MongoDB is a schema-less database that does not require the creation of tables and other rigid, pre-defined schema. As a document database, MongoDB stores all the records in a single collection.

MongoDB has no joins: In RDBMS, connecting two or more tables is challenging as it requires using joins. MongoDB has no complex joins, and changing the document structure in MongoDB is easy, allowing us to connect other documents with no difficulty. 

No primary key set-up: When using RDBMS, setting up a primary key is necessary. In MongoDB, there is no need to explicitly create a primary key. The NoSQL database offers an _id field, created by default, with every document. This created field acts as a primary key. The reserved _id field serves as the primary key in MongoDB, and it must be a unique value. It is important to note that if there is no set up to the _id field, MongoDB fills it with "MongoDB Id Object" automatically. 

MongoDB Uses CAP Theorem: RDBMS focuses on ACID properties via Atomicity, Consistency, Isolation, and Durability. On the other hand, MongoDB uses CAP theorem Consistency, Availability, and Partition tolerance for database distributions. 

What makes MongoDB so much in-demand?

Do you know why MongoDB is becoming a favourite choice among developers? Its excellent capabilities are listed below:

Flexible document schemas

Using Structured Query Language databases, every time there is an attempt to insert information, declaration and confirmation of a table schema are necessary. MongoDB, being a document database, holds different documents in a single collection. Simply put, MongoDB allows the storage of multiple objects in a unified way, with a different set of fields. Flexible document schemas offer great advantages when working on complex data or handling real-time data.

Code-native data access

Getting the data in the Object is often not easy. In most of the databases, you will probably have to leverage Object Relational Mappers to get this work done. MongoDB saves you from using heavy wrappers and allows you to store and access data in the most simple way. Simply put, you can have code-native data access from any programming language like dictionaries in Python, associative arrays in JavaScript, and Maps in Java.

Change-friendly design

Many programmers have the habit of bringing down the site or application for customizing data.  With MongoDB, there is no need to bring the site down because the changes or customizations offered using MongoDB are impressive. Any time you need to change the schema, you will not have to lose valuable downtime. MongoDB allows users to add new data anytime, anywhere – without any disturbance in its operational processes.

Powerful querying and analytics

The fact that there are no complex joins in MongoDB also adds up to a great advantage. That means MongoDB allows data accessibility seamlessly without the need to make joins. MongoDB knows how to reach into documents when performing queries. The MongoDB Query Language (MQL) supports powerful dynamic query on documents facilitating deep queries. Above all, the document database allows accessing complex data simply using one-line of JSON-like code.

Easy horizontal scale-out

MongoDB facilitates horizontal scaling with the help of database sharding. Since the data is structured horizontally, it becomes easy to spread it across different servers and access it in a simplified way. You can create clusters using real-time replications and shard high-volume data to sustain performance.

Harness the potential of database sharding to distribute the database into several clusters. With database sharding, you will get an increased storage capacity and also quicken the query response rate.

Warning Bell:  When sharding a database, keep in mind that once you have completed the sharding, you cannot unshard it at any cost.

Why Use MongoDB?

Now that you have spotted the main differences between MongoDB and NoSQL databases, you might have understood the perks of using MongoDB. Here are a few compelling reasons to use MongoDB over other databases.

Highly versatile – Being a Non-Structured Query Language, there is no need to create tables when working with MongoDB. As a result, there is an appreciable degree of versatility in storing, managing, and accessing data. Versatility adds a great advantage when storing big and uncategorized data.

Impressive Speed – One of the reasons for the high demand for MongoDB is its speed. As there is no need to create a table or schema, the database speed is impressive. Using MongoDB, the CRUD (Creating, Reading, Updating, Deleting) speed is faster than other databases. A MongoDB query is 100 times quicker, allowing users to index their search in the speediest time. 

Easily Accessible – Another reason for using MongoDB is that it supports almost all the major programming languages C, C++, C#, Java, Node.js, Perl, PHP, Python, Ruby, Scala, and many more. Also, MongoDB has excellent community-supported drivers for low popular programming languages too. You can also host MongoDB on its cloud service, MongoDB Atlas, which offers both a community-driven open source and a premium Enterprise Edition. 

Easy-to-Use

If you happen to be a JavaScript developer, you will fall in love with the document database; MongoDB. Compared to other databases, MongoDB is easy to use. Even a newbie can understand the database and use it efficiently with no difficulty. As MongoDB stores each record to the Binary JSON, it becomes super easy to use the database, especially if you are using JavaScript libraries like Node.js, React, or Express.js in the backend.

Where to Use MongoDB?

MongoDB is an excellent choice for web applications where there is little to no user interaction. Unlike a relational database, where there is a need to store information across several tables or even create joins, MongoDB saves the extra tasks and does it implicitly. Using MongoDB, you can store user information in the most unified way. As a result, there will be a single query to a single collection, and the front-end can deal with editing the data.

Integrating Big-Data – If your business involves a pool of incoming data from different sources, MongoDB will prove to be very helpful. When other databases have failed, MongoDB comes up with unique capabilities to store and integrate big data seamlessly. The one-document database provides robust capabilities to store a large amount of diverse data in the most simplified way. 

Defining Complex-Data – MongoDB allows embedded documents (also called Nested Documents) to define nested structures. Nested documents are documents where a document is present inside a document. It is helpful when a one-to-many relationship exists between documents. Best of all, MongoDB supports specialized data formats like geospatial format, that result in a resilient repository that remains unbroken even after edits. 

In a nutshell, you can use MongoDB for the following:

  • Blogs and content management
  • E-commerce product catalog
  • User data management
  • For real-time analytics and high-speed logging, and high scalability
  • Configuration management
  • To maintain Geospatial data
  • Mobile and social networking sites

Attention: It is important to note that MongoDB is not the right choice for a robust transactional system or places where the data model is upfront. Also, it is a poor decision to leverage MongoDB for tightly coupled systems. Perhaps, Structured Query Language will be the right fit!

Conclusion

MongoDB is a robust database with excellent capabilities and stands out in-built functions. Today, IT sectors, e-commerce, banking, logistics, and many others are managing their data flow using MongoDB. Multi-national companies like Bosch, Uber, Accenture, Barclays, to name a few, use MongoDB for storing the uncategorized data in the most sophisticated way.

If you are looking for the most efficient database to store and access data seamlessly, there is no better option than MongoDB. Although the performance analysis of MongoDB is exceptionally superb, perhaps there is no transaction support, and indeed the database uses very high memory for storage. However, the striking features you get for using MongoDB should not be sacrificed for the high-memory cost.

Abhresh

Abhresh Sugandhi

Author

Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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Handling events with react is  very similar to handling events in DOM elements. Below are some general events that you would see in and out when dealing with react based websites:  Clicking an element  Submitting a form Scrolling page Hovering an element  Loading a webpage Input field change User stroking a key Image loading Naming Events in React Handling events with react is very similar to handling events in DOM elements, although there are some syntactic differences.   React events are written in camelCase.   A function is passed as the event handler rather than string. The way to write events in html / DOM is below:        click me onclick is written in lower case in html as shown above and what action to take when this onclick event triggers is taken care of by handleClick.In React, events are named using camel case and you pass a function as event handler as shown below:  Like in a functional component, event is written like below:       click me   In class based component ,event is written like below        click me Defining Events:Events are normally used in combination with functions, and the function is not executed until the event occurs, and the combination of event, HTML element, and javascript function is called binding which means to map all three. Generic syntax is:      Example:  Create a button element and what happens when onClick event triggered is driven by the function which is func() shown below     click me Let’s see some of the event attributes:   onmouseover : The mouse is moved over an element onmouseup : The mouse button is released onmouseout : The mouse  is moved off an element onmousemove: The mouse is moved Onmousedown: mouse button is pressed  onload : A image is done loading onunload: Existing the page  onblur : Losing Focus  on element  onchange : Content of a field changes onclick: Clicking an object  ondblclick: double clicking an object  onfocus element getting a focus  Onkeydown: pushing a keyboard key Onkeyup: keyboard key is released Onkeypress: keyboard key is pressed  Onselect: text is selected These are some examples of events:                                         Events                               function testApp (){                        alert((“Hello Event”);                                                   test Clicked                  test double Clicked                     Synthetic Events When you specify an event in JSX, you are not directly dealing with regular DOM events, you are dealing with a react event type called a synthetic event.It's a simple wrapper for native event instances and every synthetic event created needs to be garbage-collected which can be resource intensive in terms of CPU. 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For example, instead of using onClick, use onClickCapture to handle the click event.  Capture event example:                  Click me    Additional ExamplesExample1                       Import React from ‘...react’                         function clickAppHandler() {                                function clickHandler() {                                        console.log(‘clicked’)                                         }                                  return (                                                                                  Click                                                                          )                         }                       export default clickAppHandler   Example2       This example is along with HTML in a single file                                                            Events                               function testApp (){                        alert((“Hello Event”);                                                   test Clicked                  test double Clicked                     Adding Events: Below example is how you add an event. Highlighted in bold                      Import React from ‘...react’                         function clickAppHandler() {                                function clickHandler() {                                        console.log(‘clicked’)                                         }                                  return (                                                                                  Click                                                                          )                         }                       export default clickAppHandler  Passing Arguments to Event HandlerThere are two ways arguments are passed to event handler  Arrow function                    this.handleClick(id,e)}>Click                onClick is the event                e is the event object                 id can be state or props or some data Bind method      Click  In this case event object is automatically passed In both methods e represents the react event and its passed after the ID as second argument,With an arrow function this event e is passed explicitly but with bind method its automatically passed.                                     Import React,{ Component } from “react”;                                         class TestApp extends Component {                                           state = {                                                       id: 2,                                                      Name: “TestApp Dummy”                                                };                                                             //arrow function                                                 handleClick = (id,e) => {                                                       console.log(id);                                                       console.log(e);                                                  };                                               handleArg = (e) => { this.handleClick(this.state.id,e);}                                                          render() {     return (                    TestApp,{this.state.name}            onClick={this.handleArg}>Display            );   }  }  The react event is an object and obtained from react. Instead of creating a separate function for passing argument, you can directly pass the anonymous arrow function as shown in the render function below:     render() {        return (                                                                                                       TestApp,{this.state.name}                                                {                           this.handleClick(this.state.id,e);                                                               }}>Display                                                                                                         );                                                 }                                            }    Output:   click on button  “TestApp Dummy “                   Let’s see only how bind method looks like in the render function    render() {                                         return (                                                                                                 TestApp,{this.state.name}                                                   Display                                                                                                       );                                                  }                                              } Output: this will display the h1 tag and when you click the button handleClick function gets invoked and the console will display id of the state object as shown above. Building a Practice to Thoroughly Understand Events This blog focuses on event handling, which in turn teaches about event handlers declared in JSX markup.This approach helps in tracking down the element mapped with events in an easy way.  We also learned how to handle multiple event handlers in a single element by using JSX attributes.we also learned about ways to bind event handler and  parameter values. Then we learned about synthetic events which are abstractions around native events. The best way you can retain this learning is by practicing more and tackling the complexities that may arise as you practice. You can find several tutorials on the internet or share your questions with us here. Happy learning! 
5355
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. 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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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 
7344
Implementing MongoDb Map Reduce using Aggregation

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