Welcome to the first chapter of the MongoDB tutorial (part of the MongoDB Developer and Administrator Course). This tutorial provides an introduction to the NoSQL and NoSQL database. Let us explore the objectives of this lesson in the next section.
After completing this NoSQL tutorial, you will be able to:
Let us begin with understating what NoSQL is in the next section of this tutorial.
Traditionally, the software industries use relational databases to store and manage data persistently. Not only SQL or NoSQL is a new set of a database that has emerged in the recent past as an alternative solution to relational databases.
Carl Strozzi (Pronounce as Stro-jee ) introduced the term NoSQL to name his file-based database in 1998.
NoSQL refers to all databases and data stores that are not based on the Relational Database Management Systems or RDBMS principles. It relates to large data sets accessed and manipulated on a Web scale. NoSQL does not represent single product or technology. It represents a group of products and a various related data concepts for storage and management. NoSQL was a hashtag that was chosen for a tech meetup to discuss the new databases.
We will learn about various database features of NoSQL in the next section of the tutorial.
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NoSQL Databases can have a common set of features such as:
We will discuss the purpose of using NoSQL in the next section of this tutorial.
With the explosion of social media, user-driven content has grown rapidly and has increased the volume and type of data that is produced, managed, analyzed, and archived. In addition, new sources of data, such as sensors, Global Positioning Systems or GPS, automated trackers, and other monitoring systems generate huge volumes of data on a regular basis.
These large volumes of data sets, also called big data, have introduced new challenges and opportunities for data storage, management, analysis, and archival. In addition, data is becoming increasingly semi-structured and sparse. This means that RDBMS databases which require upfront schema definition and relational references are examined.
To resolve the problems related to large-volume and semi-structured data, a class of new database products has emerged. These new classes of database products consist of column-based data stores, key/value pair databases, and document databases. Together, these are called NoSQL. The NoSQL database consists of diverse products with each product having unique sets of features and value propositions.
In the next section of this NoSQL tutorial, we will identify the differences between NoSQL and RDBMS.
Given below is a table that explains the difference between RDBMS (Relationship Database Management Systems) and NoSQL.
RDBMS |
NoSQL |
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In the next section, we will discuss the various benefits of NoSQL.
The desired technical characteristics of an enterprise-class NoSQL solution are as follows.
Primary and Analytic Data Source Capability
The first criterion of an enterprise-class NoSQL solution is it must serve as a primary or active data source that receives data from different business applications. It also must act as a secondary data source or analytic database that enhances business intelligence applications. From business perspective, the NoSQL database must be capable of quickly integrating all types of data viz structured, semi-structured, or unstructured. In addition, it must be able to execute high-performance queries.
Once all the required data is collected in the database, data administrators may want to perform an analysis in real time and in map-reduce form. An enterprise-class NoSQL database can easily handle such requests using the same database. It does not require loading the data into a separate analytic database for analysis.
Big Data Capability
NoSQL databases are not restricted to working with big data. However, an enterprise-class NoSQL solution can scale to manage large volumes of data from terabytes to petabytes. In addition to storing large volumes of data, it delivers high performance for data velocity, variety, and complexity.
Continuous Availability or No Single Point of Failure
To be considered enterprise-class, a NoSQL database must offer continuous availability, with no single point of failure. Moreover, rather than providing the continuous availability feature outside the software, the NoSQL solution delivers inherent continuous availability.
The NoSQL databases must include the following key features:
Multi-Data Center Capability
Typically, business enterprises own highly distributed databases that are spread across multiple data centers and geographic locales. Data replication is a feature offered by all legacy RDBMS. However, none can offer a simple model of data distribution between various data centers without any performance issue.
A simple method includes the ability to handle multiple data centers without concerning the occurrences of the read and write operations. A good NoSQL enterprise solution must support multi data-center deployment and must provide configurable option to maintain a balance between performance and consistency.
Easy Replication for Distributed Location-Independent Capabilities
To avoid data loss affecting an application, a good NoSQL solution provides strong replication abilities. These include a read-anywhere and write-anywhere capability with full location-independence support.
This means you can write data to any node in a cluster, have it replicated on other nodes, and make it available to all users irrespective of their location. In addition, the write capability on any node must ensure that the data is safe in the event of a power failure or any other incident.
No Need for Separate Caching Layer
A good NoSQL solution is capable of using multiple nodes and distributing data among all the participating nodes.
Thus, it does not require a specific caching layer to store data. The memory caches of all participating nodes can store data for quick input and output or I/O access. NoSQL database eliminates the problem of synchronizing cache data with the persistent database. Thus, it supports simple scalability with fewer management issues.
Cloud-Ready
As an adaptation of cloud infrastructure is increasing day by day, an enterprise-class NoSQL solution must be cloud-ready.
A NoSQL database cluster must be able to function in a cloud setting, such as Amazon EC2, and also must be able to expand and contract a cluster when necessary. It also must support a hybrid solution where part of the database is hosted within the enterprise premise and another part is hosted in a cloud setting.
High Performance with Linear Scalability
An enterprise-class NoSQL database can enhance performance by adding nodes to a cluster. Typically, the performance of database systems may go down when additional nodes are added to a cluster. However, a good NoSQL solution increase performance for both read and writes operations when additional nodes are added. These performance gains are linear in nature.
Flexible Schema Support
An enterprise-class NoSQL database offers a flexible or dynamic schema design to manage all types of data—structured, semi-structured, and non-structured. Therefore, the need to have different vendors to support the different data types does not arise.
NoSQL databases may support various schema formats, such as columnar/Bigtable and document.
Therefore, choosing an appropriate database based on application requirement is a key design decision.
The flexible or dynamic schema support ensures that you can make schema changes to a structure without making the structure offline. This support is critical considering the near-zero downtime and round-the-clock availability for business applications.
Support Key Developer Languages and Platforms
Ideally, an enterprise-class NoSQL solution must support all major operating systems. In addition, it must run on a product hardware that does not require any tweaks or other proprietary add-ons.
The NoSQL database must provide client interfaces and drivers for all common developer languages. It must offer a structured query language or SQL or a similar language that helps store and access data in a NoSQL database.
Easy to Implement, Maintain, and Grow
A NoSQL database must be simple but robust. In other words, it must be easy to implement and use and must offer sturdy functionality to handle various enterprise applications.
In addition, the NoSQL vendor must supply good management tools that assist data professionals to perform various administrative tasks, such as adding capacity to a cluster, running utility tasks, and so on.
The NoSQL database must allow easy growth without making any change to the front-end of the business application.
Thriving Open Source Community
For an open source NoSQL database, having a vibrant community is essential to make a regular contribution to enhance the core software.
Moreover, open source communities generally provide excellent quality assurance or QA testing. This sometimes eliminates the need for software companies to hire, train, and retain a QA team.
To encourage a thriving open source community, including activities on mailing lists and technical forums, initiate technical discussions, and participate in conferences.
In the next section of this tutorial, we will discuss the various types of NoSQL databases.
There are four basic types of NoSQL databases.
Key-Value database
Key-Value database has a big hash table of keys and values. Riak (Pronounce as REE-awk), Tokyo Cabinet, Redis server, Memcached ((Pronounce as mem-cached), and Scalaris are examples of a key-value store.
Document-based database
Document-based database stores documents made up of tagged elements. Examples: MongoDB, CouchDB, OrientDB, and RavenDB .
Column-based database
Each storage block contains data from only one column, Examples: BigTable, Cassandra, Hbase, and Hypertable.
Graph-based database
A graph-based database is a network database that uses nodes to represent and store data. Examples are Neo4J, InfoGrid, Infinite Graph, and FlockDB. The availability of choices in NoSQL databases has its own advantages and disadvantages.
The advantage is, it allows you to choose a design according to your system requirements. However, because you have to make a choice based on requirements, there is always a chance that the same database product may not be used properly.
We will learn about the key-value database in the next section of this tutorial.
From an Application Program Interface or API perspective, a key-value database is the simplest NoSQL database.
This database stores every single item as a key with a value. You can get the value for a key, add a value for a key, or delete a key. The value is a blob that the database stores without knowing its content. The responsibility lies with the application to understand what is stored. Typically, key-value databases use primary-key access.
Therefore, they generally offer enhanced performance and scalability. All key-value databases may not have the same features.
For example, data is not persistent in Memcached while it is in Riak.
These features are important when implementing certain solutions.
For example, you need to implement caching of user preferences.
If you implement them in Memcached, you may lose all the data when the node goes down and may need to get them from the source database. If you store the same data in Riak, you may not lose data but must consider how to update the stale data. It is important to select a key-value database based on your requirements.
We will continue with the discussion on a key-value database in the next section.
The key value store does not have a defined schema. It contains client defined semantics for understanding what the values are. A key value store is simple to build and easy to scale. It also tends to have great performance because the access pattern can be optimized to suit your requirement.
The advantages & disadvantages of the key-value store include the following.
Advantages
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Disadvantages |
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In the next section of the NoSQL tutorial, we will discuss Document database.
This NoSQL database type is based on the concept of documents.
In the next section, we will focus on examples of a document database.
Examples of Document databases are:
MongoDB: Provides a rich query language and many useful features such as built-in support for MapReduce-style aggregation and geospatial indexes.
Apache CouchDB: Uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API.
Example: (MongoDB) document
{Name:“SimpliLearn",
Address:" 10685 Hazelhurst Dr, Houston, TX 77043, United States”,
Courses: [“Big Data”,”Python”,”Android”,”PMP”,”ITIL”],
Offices: [ “NYK”,”Dubai”,”BLR”],
}
In the next section, we will learn about the Column-based database.
Column-based databases store data in column families as rows. These rows contain multiple columns associated with a row key.
Column families are groups of related data that is accessed together.
For example, you may access customer profile information at the same time, but not their order history.
We will continue our discussion on the Column-based database in the next section of the tutorial.
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The goal of a Column-based database is to efficiently read and write data to and from hard disk storage to quickly return a query. In this database, all column one values are physically together, followed by all the column two values.
The data is stored in record order so that the 100th entry for column one and the column two are from the same input record. This allows you to access individual data elements, such as customer name, as a group in columns, rather than individually row-by-row.
The compression permits columnar operations like MIN, MAX, SUM, COUNT, and AVG— to be performed very rapidly. A column-based database management system or DBMS is self-indexing, therefore it uses less disk space than an RDBMS containing the same data.
We will continue our discussion column-based database in the next section of this tutorial.
The diagram provided in the section depicts that data is getting stored in a column rather than row format. It shows columns for the same column family are stored together in one file on the hard disk.
Therefore, these data can be retrieved fast in an efficient manner. In the next section, we will focus on an example of a column-based database.
Cassandra is a column-based database.
Cassandra is fast and easily scalable with write operations spread across the cluster.
The cluster does not have a master node, hence, any node can handle the read and write operations.
In the next section of this tutorial, we will discuss Graph database.
A graph database lets you store data and its relationships with other data in the form of nodes and edges. Each relation can have a set of properties. Edges have a direction which has its own significance and enables you to explore the relationship in both the direction.
All the nodes in the graph are organized by relationships that help explore interesting and hidden patterns between the nodes.
We will continue our discussion on Graph database in the next section.
In the graph database, the labeled property graph model is used for modeling the data. It is same as the entity relationships or ER model used in RDBMS. The property graph contains connected entities, such as the nodes which can hold any number of attributes or key-value-pairs.
In the next section, we will discuss Consistency, Availability, and Partition tolerance or CAP theorem.
In a distributed system, the following three properties are important.
We will continue with the CAP Theorem in the next section.
The CAP theorem was proposed by Eric Brewer.
According to this theorem, in any distributed system, you can use only two of the three properties—consistency, availability, or partition tolerance simultaneously.
Many NoSQL databases provide options for a developer to choose to adjust the database as per requirement. For this, understanding the following requirements is important:
We will focus on consistency in the next section of this tutorial.
Consistency in CAP theorem refers to atomicity and isolation. Consistency means consistent read and write operations for the same sets of data so that concurrent operations see the same valid and consistent data state, without any stale data.
Consistency in ACID means if the data does not satisfy predefined constraints, it is not persisted. Consistency in CAP theorem is different. In a single-machine database, consistency is achieved using the ACID semantics.
However, in the case of NoSQL databases which are scaled out and distributed providing consistency gets complicated.
We will discuss availability in the next section of this tutorial.
According to the CAP theorem, availability means:
In the next section, we will discuss partition tolerance.
Partition tolerance or fault-tolerance is the third element of the CAP theorem. Partition tolerance measures the ability of a system to continue its service when some of its clusters become unavailable.
In the next section, we will learn about MongoDB in terms of the CAP theorem.
By default, MongoDB offers strong consistency. This means after you perform a write operation, you cannot read the same data until the write operation is successful. MongoDB is a single-master system and by default, all reads go to the primary node.
Optionally, if you enable reading from the secondary node, MongoDB becomes eventually consistent and allows reading of out-of-date results. In addition, MongoDB handles network partition very well by keeping same data on multiple nodes or replica set.
Therefore, MongoDB is a consistent and partition tolerant database which comprises the availability aspect.
Here is a quick recap of what was covered in this lesson:
This concludes the lesson on Introduction to NoSQL databases. In the next chapter, we will discuss databases for the modern Web.
Name | Date | Place | |
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MongoDB Developer and Administrator | 2 Mar -24 Mar 2019, Weekend batch | Your City | View Details |
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