MongoDB - A Database for the Modern Web Tutorial

2.1 MongoDB-A Database for the Modern Web

Hello and welcome to Lesson 2 of the Mongo DB Developer course offered by Simplilearn. This lesson provides an introduction to the MongoDB database and its key features. Let us explore the objectives of this lesson in the next screen.

2.2 Objectives

After completing this lesson, you will be able to: • Describe what MongoDB is • Identify the key features of MongoDB • Explain MongoDB’s core server and tools • Explain how to install MongoDB on Windows and Linux computers • Identify the steps to start the MongoDB server • Identify the data types available in MongoDB and • Identify the schema design and data modeling techniques in MongoDB In the next screen, we will discuss MongoDB.

2.3 What is MongoDB?

MongoDB is a document-based database. The basic idea behind shifting from relational data model to a new data model is to replace the concept of a ‘row’ with a flexible model, the ‘document’. The document-based approach allows embedded documents, arrays, and represents a complex hierarchical relationship using a single record. This is how developers using object-oriented languages want to represent their data. MongoDB is schema-free, the keys used in documents are not predefined or fixed. Without a fixed schema, massive data migrations have become unnecessary. While migrating data to MongoDB, any issues of new or missing keys can be resolved at the application level, rather than changing the schema in the database. This offers developers the flexibility of working with evolving data models. In the next screen, we will discuss JSON, a data format used in MongoDB.

2.4 JSON

JavaScript Object Notation or JSON is an open data interchange format that can be read by both, human and computers. JSON is language independent. However, it uses conventions of the C-family of languages, such as C, C++ (Pronounce as C plus plus), and C# (Pronounce as C hash), Java, JavaScript, Perl, and Python. Because of these properties, JSON makes an ideal data-interchange language. The main format used on the modern Web is XML. Typically, JSON supports the basic data types, such as numbers, strings, boolean values, arrays, and hashes. In the next screen, we will discuss JSON structures.

2.5 JSON Structure

JSON is built on the following two structures: • A collection of name/value pairs, such as an object, record, struct, dictionary, hash table, keyed list, or associative array. • An ordered list of values, such as an array, vector, list, or sequence. Typically, all modern programming languages support these universal data structures. In JSON, an object is considered as a set of name/value pairs which does not follow any order. Typically, an object is presented between a left brace and right brace. Each name in the object is followed by a colon and the name/value pairs are separated by a comma. In the example of JSON given on the screen, the document has the ID field which contains unique numbers to identify the document name field as ‘MongoDB’ string, ‘customers’ field as an array to represent customers of MongoDB. It also has an embedded document called ‘application’ that contains information about different kind of application built using MongoDB. In the next screen, we will discuss BSON.

2.6 BSON

Binary JSON or BSON is a binary serialization format which is used for storing documents and making remote procedure calls in MongoDB. Similar to JSON, BSON allows embedding of doc¬u-ments and ar¬rays with¬in oth¬er doc¬u¬ments and ar¬rays. In addition, BSON supports ex¬ten¬sions that represent data types that are not part of JSON. For ex¬ample, BSON has a Date type and a BinData type. BSON has the following three char¬ac¬ter¬ist¬ics: Lightweight BSON is lightweight. When used over the network, BSON manages to keep the overhead involved in processing extra header data to a minimum. Traversable BSON is de¬signed to tra¬verse eas¬ily across network. This is a vi¬tal prop¬erty in its role as the primary data rep¬res¬ent¬a¬tion for Mon¬goDB. Efficient BSON uses C data types that allows easy and quick en¬cod¬ing and de¬cod¬ing of data. MongoDB can build indexes inside BSON objects and match objects against query expressions on both top-level and nested BSON keys. We will discuss the structure of MongoDB in the next screen.

2.7 MongoDB Structure

The table given on the screen depicts the various SQL (pronounce as sequel) terminology and concepts and the corresponding MongoDB terminology and concepts. In MongoDB, the table and view structure is known as collection. Typically, these collections group documents that are structurally or conceptually similar. MongoDB allows embedding of related document to one another. These documents are used to query related data. The data for a table in MongoDB are distributed among different shards which is similar to the concept of partition in Relational Database Management Systems or RDBMS. After grouping structurally similar documents into collection, MongoDB groups different collections into databases. For instance, a single instance of MongoDB is capable of hosting multiple independent databases. Ideally, you should store data related to a single application at one database. You need separate databases when storing multiple application or users data on the same MongoDB server. In the next screen, we will examine a document store example.

2.8 Document Store Example

An example of an embedded document is given on the screen. In this example, a document is stored in the collection user for a person called Mark Tailor, who is 35 years old and has interest in long distance running and mountain biking. This document also contains an embedded document called favorites that displays the data about Mark Tailor’s favorite color and sports. In the next screen, we will discuss MongoDB as a document database.

2.9 MongoDB as a Document Database

The data model in MongoDB is document-based. Although documents provide rich structure, they need not conform to any pre- specified schema. In a relational database, rows are stored in a table and each table has a strictly defined schema that specifies the permitted types and columns. If a row in a table requires an extra field, the entire tabled needs to be altered. MongoDB on the other hand groups documents into collections that do not follow any schema. Each individual document in a collection can have a completely independent structure. In practice, documents in a collection are relatively uniform. For example, if you are creating a collection ‘review_comments’ for storing review comments given by the customer, then every document in this collection will have fields such as title, tags, comments, and so on. The lack of schema offers many advantages. First, since structure is defined by the application code not the database, this speed up initial application development when the schema tends to change frequently. Second? a schema less model lets you represent data with variable properties. In the next slide, we will discuss Transaction Management in MongoDB.

2.10 Transaction Management in MongoDB

Compared to RDBMS, such as MySQL (pronounce as My Sequel) which supports two phase commit, MongoDB only supports a single phase commit at each document level. A write operation in a single document is considered atomic in MongoDB even if the operation alters multiple embedded documents. However, the entire operation is not atomic and other operations may interleave. We will discuss Easy Scaling in the next screen.

2.11 Easy Scaling

With the rapid growth of technology, data set sizes are growing at a remarkable pace. Advances in sensor technology, the popularity of internet connected handheld devices have created a demand for more data storage with more databases capabilities. Even small scale applications generate more data than many databases are capable of handling. As the amount of data that needs to be stored are growing, developers face the challenge of scaling their databases. Scaling is the core feature of MongoDB. You can add a new machine to the existing MongoDB cluster to expand its capacity. In the next screen, we will discuss how to scale up or scale out a database.

2.12 Scaling Up vs. Scaling Out

Scaling a database involves two choices: • Scaling up or getting a bigger database and • Scaling out or partitioning data across multiple databases. Scaling up is an easy but costly affair. Large databases are very expensive and eventually a time may come when the purchased database too will be unable to handle the growing volumes of data and a more powerful database is required. On the other hand, it is both extensible and cost-effective to scale out. You can buy commodity servers and add them to your cluster, thus add storage space and increase performance. In the next screen, we will discuss scale up or vertical scaling.

2.13 Vertical Scaling

You can easily scale a database by upgrading the hardware. If your application is running on a single node, you can add some combination of disk input output per second or IOPS (Pronounce as I-O-P-S), memory, and CPU to enhance the database performance. The technique of enhancing a single node hardware is called vertical scaling or scaling up. Vertical scaling is simple, reliable, and cost-effective to some extent. If you are running on a physical hardware, you may face a situation where the cost of a more powerful server is not permitted. In such cases, you need to consider horizontal scaling, or scaling out. In the next screen, we will learn about horizontal scaling.

2.14 Horizontal Scaling

In horizontal scaling, database is distributed across multiple machines. You can use a commodity hardware for a horizontally scaled architecture. Thus, the costs for hosting the total data is reduced. In addition, the distribution of data across multiple machines mitigates the risk of failure. When you scale vertically, you need to deal with one machine when failure occurs. The failure may not impact your business if a copy of the data is saved on a replicated slave. However, failure of a single server may bring down the entire system. On the other hand, the failure in a horizontally scaled architecture is less disastrous because a single machine is only a small part of the entire system. MongoDB supports horizontal scaling. It uses a range-based partitioning mechanism called ‘auto-sharding’ which automatically manages data distribution across multiple nodes. In the sharding system, you can easily add new nodes. When a failure of the master node occurs, it does the failover to another replica-set node. This whole sharding system is transparent to the client. The client does need to be aware whether it is single shard or a set of shards In the next screen, we will discuss the features of MongoDB.

2.15 Features of MongoDB

Some of the key features of MongoDB are as follows: Ad hoc queries: MongoDB allows performing of search functions by field, range queries, and regular expression searches. Queries can return specific document fields and may include user-defined JavaScript functions. Querying: For document retrieval, MongoDB uses rich query language. It also allows you to write any complex condition to retrieve documents. Fast In-Place Updates: MongoDB allows you to choose write semantics, enable journaling, and thus control the speed and durability. All writes are sent across a Transmission Control Protocol or TCP socket and do not require a database response. To get a response, use the special safe mode of the drivers and perform a write. This generates a response acknowledging the receipt of the write with no errors. Server-side JavaScript execution: MongoDB uses JavaScript in queries and aggregation functions such as MapReduce, which are sent to the database for execution. Capped collections: Capped collections are fixed sized collections supported by MongoDB. These collections maintain insertion order and once the specified size has been reached, behaves like a circular queue. In the next screen, we will discuss secondary indexes.

2.16 Secondary Indexes

MongoDB allows implementation of multiple secondary indexes as B-trees. These B-tree indexes can be optimized for range scan queries and queries with sort clauses. MongoDB lets you create up to 64 indexes per collection. It supports indexes, such as ascending, descending, unique, compound-key, and geospatial. MongoDB uses the same data structure for indexes as most RDBMSs. In the next screen, we will learn about replication.

2.17 Replication

MongoDB creates database replication using a topology called replica set. A replica set distributes data across various MongoDB nodes called shards for redundancy and also automates failover when server or network outages occur. In addition, replication is used to scale database reads. For example, if your application is read intensive, you can spread database reads across various nodes in the replica set cluster. Typically, a replica set contains one primary node and one or more secondary nodes. Similar to the master-slave replication, the primary node of a replica set supports both reads and writes. However, the secondary nodes support read-only. We will continue with Replication in the next screen.

2.18 Replication (contd.)

The image on the screen depicts a working replica set. When the primary node fails, the cluster picks a secondary node and automatically converts it to primary. When the failed primary is restored, it acts as a secondary. We will discuss Memory management in the next screen. ?

2.19 Memory Management

MongoDB store the data in memory mapped files. By default, MongoDB uses all the system memory for these mapped files. This is the reason why MongoDB operations are fast. MongoDB allows its operating system to manage its memory. This design impacts its performances and operations. All extents are mapped to memory using mmap (Pronounce as m- map) methods. In the image given on the screen, this mapping is depicted by the solid black lines connecting the mapped disk blocks to the virtual memory pages. In the next screen, we will discuss Replica Set.

2.20 Replica Set

The replica set feature in MongoDB offers benefits, such as redundancy and failover. When one node fails, it does not impact the entire database. If the failed node is a Master node of the replica set, then MonoDB automatically converts another member of replica set to the Master. When replicating the data between master and slave nodes, choose whether you want a strong or delayed consistency. The replication process will keep replicated data on the nodes belonging to different data centres to protect the data in the case of natural disaster. In the next screen, we will discuss Auto Sharding.

2.21 Auto Sharding

MongoDB uses sharding for horizontal scaling. A shard is a master node with one or more slaves. You need to choose a shard key that determines the distribution of the collection data. The shard key splits the data into ranges and distributes them across multiple shards. MongoDB is spread over multiple servers and performs load balancing and/or data duplicating to keep the system up and running in case of hardware failure. Automatic configuration can be easily deployed and new machines can be added to a running database. The diagram given on the screen represents documents distributed to different shards of the MongoDB cluster. In the next screen, we will discuss Aggregation and Mapreduce.

2.22 Aggregation and MapReduce

Aggregation are operations used to analyze data sets and return calculated results. In MongoDB, you have rich set of operations to perform aggregate calculation by analyzing the data sets. MapReduce is typically used for operations, such as batch processing of data and aggregation. MongoDB uses mapreduce operations to perform data aggregation. Typically, a mapreduce operation consists of two phases. In the map stage, documents are processed and one or more objects are produced for each input document. In the reduce stage, the outputs of the map operation are combined. Optionally, there can be an additional stage to make final modifications to the result. Similar to other aggregation operations, mapreduce can define a query condition to select the input documents, and sort and limit the results. In the next screen, we will discuss collection and databases.

2.23 Collection and Database

A collection is a group of documents. If you compare a document in MongoDB to a row in a relational database, then a collection is like a table. Collections are not restricted by any schema. Therefore, documents within a single collection can have any shape. MongoDB groups collections into databases and a single instance of MongoDB can host several databases, each a completely independent database. Having different types of documents in the same collection can be cumbersome for developers and administrators. Developers need to ensure that their queries retrieve specific documents or their application codes handle documents of different structure. A good practice is to store data of a single application in the same database. In the next screen, we will discuss Schema Design and modeling.

2.24 Schema Design and Modeling

The collections in MongoDB allows flexibility in document structure. This enables you to easily map a document to an entity or an object. Typically, all documents in a collection share a similar structure. The key challenge in data modeling involves maintaining a balance between the needs of the application, the performance capability of the database engine, and its data retrieval patterns. When designing a data model, you need to consider the application usage of the data, such as queries, updates, and data processing along with its inherent structure. In the next screen, we will discuss reference data models.

2.25 Reference Data Model

When designing data models for MongoDB applications, two things are important? the structure of documents and representation of the data relationships. References and embedded documents are the two tools that allow applications to represent data relationship. References use links from other documents to store relationships between data. Applications use these references to access the related data. These are called normalized data models. You can use normalized data models: • When embedding document results in data duplication does not give sufficient performance benefit. • To represent complex many-to-many relationships. • To model large hierarchical data sets. References provide more flexibility than embedding. However, client-side applications must trigger follow-up queries to resolve references. In other words, normalized data models can send more database queries from client to the database server. We will review and example of Reference data model in the next screen.

2.26 Reference Data Model Example

The diagram provided on the screen is an example of a reference data model, which consists three different collections named Offeredcourses (Pronounce as a single word), course_detail (Pronounce as course underscore detail), and instructor. Each collection is dependent on the other collections. For example, if offeredcourses is dependent on course_detail and instructor, then you need to create three different collections and provide the reference of course_detail and instructor in the offerdcourses collection by storing their respective id field. In the next screen, we will discuss embedded data model.

2.27 Embedded Data Model

Embedded documents store related data in a single document structure and thus capture relationships between data. MongoDB allows embedding of document structures in a field or array within a document. Thus, these denormalized data models permit retrieval and manipulation of related data in a single database operation. You can use embedded data models when you have the following relationships between entities: • ‘contains’ and • One-to-many In these relationships, the ‘many’ or child documents are viewed in the context of the parent documents. Typically, embedding provides good read performance. It also allows request and retrieval of related data in a single database operation. Embedded data models allow updating of related data in a single atomic write operation. In the next screen we will focus on an example of embedded data model.

2.28 Embedded Data Model Example

The diagram given on the screen is an example of an embedded data model. In this diagram, the outermost document offeredcourse contains two embedded document coursedetail and instructor. Coursedetail has the fields, Topic and duration while instructor has the fields name and year of experience or YOE. In the next screen, we will focus on data types.

2.29 Data Types

MongoDB supports other data types while retaining JSON’s essential key/value pair characteristic. How values of each type are represented depends on the language used. Following is a list of the commonly supported data types: Null: is used to represent both a null value and a nonexistent field. For example, {"x”: null} (read as field X has null value) Undefined is used in documents. JavaScript has distinct types for null and undefined. For example, {"x" : undefined} (read as field X is undefined) Boolean is used to represent 'true' and 'false' values. For example, {"x" : true} (read as field X is a Boolean variable whose value is true) 32-bit integer cannot be represented on the shell. JavaScript supports only 64-bit floating point numbers. Hence, 32-bit integers will be converted to 64-bit. 64-bit integer The shell cannot represent these and will display them using a special embedded document. 64-bit floating point number All numbers in the shell must be of this type. We will continue with data types in the next screen.

2.30 Data Types (contd.)

Here are few more data types supported by MongoDB. maximum value: BSON contains a special data type that represent the largest possible value. The shell does not support this type. minimum value: BSON contains a special data type that represent the smallest possible value. The shell does not support this type. ObjectId ObjectId data type is unique, fast to generate and ordered. These consists of 12 bytes where the first four bytes represent the ObjectId creation time. String: BSON strings are UTF-8 compliant. Typically, when serializing and deserializing BSON, programming languages convert language strings to UTF-8 format. This allows easy storing of international characters in BSON strings. Symbol: Symbols are not supported by the shell. When the shell gets a symbol from the database, it converts it into a string. Timestamps: BSON offers a special timestamp for internal MongoDB use that is not associated with the regular Date type. Note that within a single mongod (pronounce as mongo d) instance, timestamp values are always unique. Date: Date in BSON is a 64-bit integer that denotes the number of milliseconds since the UNIX epoch. It can represent a date range of about 290 million years into the past and future. Regular expression: Documents contain JavaScript’s regular expression syntax. For example, {"x" : /simplilearn/i} (Read as “X is the filed whose value contain sub string ‘simplilearn’) We will discuss few more data types in the next screen.

2.31 Data Types (contd.)

Following are some more data types supported by MongoDB. Code Documents can contain JavaScript code. For example, {"x" : function() { /* ... */ }} (read as logic inside function) binary data It is a string of arbitrary bytes. It cannot be manipulated from the shell. Array Sets or lists of values can be represented as arrays. For example, {"courses" : ["PMP", "Cloud", " MongoDB "]} (read as Courses is the field whose value is an array. This arrays holds three elements “PMP”,”Cloud” and “MongoDB”) Embedded document Documents can contain entire documents, embedded as values in a parent document. For example, {"course_duration" : {"MongoDB " : "24 Hrs"}} (read as Course_duation is the embedded document which has field name “MongoDB”, the value for this field is 24 hours) In the next screen, we will discuss the core servers of MongoDB.

2.32 Core Servers of MongoDB

The core database server of MongoDB can be run an executable process called mongod (pronounce as mongo d) or mongodb.exe (pronounce as mongo db dot e-x-e) on Windows. The mongod process receives a command to run the MongoDB server over a network socket through a custom binary protocol. The data files for a mongod process are stored by default in the directory /data/db (read as slash data slash D-B). You can run a mongod process in several modes. The most common mode is the replica set. Typically, replica set configurations comprise two replicas and an arbiter process that reside on a third server. The auto-sharding architecture of MongoDB consist of mongod processes configured as per-shard replica sets. It also consist special metadata servers called config servers. In addition, mongos, a separate routing server is used to send requests to the appropriate shard. Mongos queries from the application layer and locates the data in the sharded cluster to complete these operations. A mongos instance is identical to any MongoDB instance. In the next screen, we will discuss the MongoDB tools.

2.33 MongoDB's Tools

MongoDB Tools consists of the following: JavaScript shell, database drivers, and command-line tools. The JavaScript shell: The command shell in MongoDB is a JavaScript-based tool. It is used to administer the database and manipulate data. A mongo executable loads the shell and connects it to a specified mongod process. In addition to inserting and querying data, the shell allows you to run administrative commands. Database drivers The MongoDB drivers are easy to use. It provides an Application Program Interface or API that matches the syntax of the language used while maintaining uniform interfaces across languages. 10gen (pronounce as ten gen), a company behind MongoDB supports drivers for C, C++, C#, (pronounce as C, C plus plus C hash) Erlang, Haskell, Java, Perl, PHP, Python, Scala, and Ruby. Command-line tools MongoDB contains the following command-line utilities: mongodump and mongorestore (Pronounce as Mongo dump and mongo store) are standard utilities that helps backup and restore a database. mongodump can save the data and the BSON format of MongoDB and thus, is used for backups only. This tool is used for hot backups and can be restored with mongorestore easily. mongoexport and mongoimport are used to export and import JSON, comma separated value or CSV, and Tab separated Value or TSV data. These tools help you get data in widely supported formats. You can use mongoimport for initial imports of large data sets. However, you need to adjust the data models for best results. In such a case, you can use a custom script to easily import the data through one of the drivers. mongosniff is a wire-sniffing tool used for viewing operations sent to the database. This tool translates the BSON that is transmitted to human-readable shell statements. mongostat is similar to iostat (pronounce as I-O-stat). mongostat provides helpful statistics, including the number of operations per second, for example, inserts, queries, updates, deletes, and so on. It also provides information, such as the amount of virtual memory allocated and the number of connections to the server. In the subsequent screens, we will learn how to install and start MongoDB on Linux and Windows.

2.34 Installing MongoDB on Linux

This demo will show the steps to install MongoDB on Linux. Click the demo icon to view the demo.

2.36 Installing MongoDB on Windows

This demo will show the steps to start MongoDB on Windows. Click the demo icon to view the demo.

2.38 Starting MongoDB On Linux

This demo will show the steps to start MongoDB on Linux. Click the demo icon to view the demo.

2.40 Starting MongoDB On Windows

This demo will show the steps to start MongoDB on Windows. Click the demo icon to view the demo.

2.42 Use Cases

Let us discuss some use cares of MongoDB. Personalization: For any business, you need to have customer details, such as names, address, their online interactions, likes and dislikes. Based on these information, you can predict the wants and needs of customers. MongoDB allows you to personalize customer experience in real time. It helps search through multiple lines of business from holiday destinations to flights with different attributes, such as the time and number of flight connects, the size and model of a rental car and so on. Customers can either log on or choose to surf through as a guest, view different products, pictures, and reviews. They may enter text notes about things they find, put up a comment. All these online activities amount to a large deal of unstructured data. A relational database would not be able to manage these online activities and normalize all customers, sessions and product data through an Extraction, transformation and loading or ETL process. Mobile: Modern day customers want everything on their smartphone. An RDBMS would not be able to handle these demands. MongoDB allows you to scale mobile applications to cater to millions of online users. Internet of things or IOT: The IOT is a world where all your physical assets and devices are connected for instant information sharing. Sensor-enabled devices generate huge volumes of data regularly which cannot be managed by the traditional relational databases. MongoDB allows building of your own IOT suite. This helps you manage big data on your own. Real time Analytics: Most companies performs data analysis. Although many can act on data from months, weeks, or even days ago, few can respond to changes minute by minute, or second by second because: • They cannot analyze semi-structured, unstructured, and geospatial data. • The data changes faster than their systems can cope with. Mongo DB allows data analysis in real time. In the next screen, we will review few more use cases of MongoDB.

2.43 Use Cases (contd.)

Some more use cases of MongoDB are: Web application: MongoDB is a primary data store for Web applications. A simple web application may require numerous data models for managing users, sessions, application specific data, and uploads. The collection and document model of MongoDB helps these Web application manage data efficiently. As documents represent rich data structures, less collections can represent data. The same data will require more tables in a fully normalized relational model. In addition, dynamic queries and secondary indexes allow easy implementation of queries familiar to SQL developers. Finally, as a Web application grows, MongoDB provides a clear path for scaling. Content management: Back in the year 2000, Websites mostly contained static text content. With the ever growing population of Internet users, you need an array of text, audio, video, images and social media to attract user attention. Adding new content, features or attributes to a relational database is not easy. You need to take the database offline to include any of these. With MongoDB, you can store and present any type of content, build new features, incorporate all kinds of data in a single database. Catalog: You use catalogs to organize Stock Keeping Units or SKUs, Forex trades, equipment, and everything else. However, you cannot easily add new items, such as new attributes or features without impacting the database performance or making it offline. With MongoDB, you can do this easily. Online retailers have access to lot of customer data. Smart retailers access these data to provide online shoppers a highly personalized and smooth shopping experience. Single View is the real-time views of your business that integrates all siloed data and display them in a single view. MongoDB allows you to build a single view all your business data.

2.44 Quiz

With this, we come to the end of this lesson. Following are few questions to test your understanding of the concepts discussed here.

2.45 Summary

Here is a quick recap of what was covered in this lesson: • MongoDB is a document-based database that represents a complex hierarchical data relationship using embedded document model or using reference model. • MongoDB uses JSON as the data format, which is based on: o A collection of name/value pairs o An ordered list of values • A collection in MongoDB is a table and view structure that groups structurally or conceptually similar documents. • MongoDB supports auto-sharding to manage data distribution across multiple nodes. • MongoDB uses replica sets to create redundancy and automates failover when server or network outages occur.

2.46 Conclusion

This concludes the lesson MongoDB: A database for the modern web. In the next lesson, we will discuss CRUD Operations in MongoDB.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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