Overview of SQL vs. NoSQL vs. NewSQL
In this blog, we will show you the basic difference between SQL vs. NoSQL vs. NewSQL. These are the types of databases, and in the upcoming paragraph, you will understand each of them.
SQL Databases can also be termed as Relational Database management Systems, also known as RDBMS, a classical approach to storing and operating historical data. In such a system, the information follows a structured approach utilizing tables or relations.
With the advent of Big Data, the structured approach fails miserably to cater to the needs of the humongous information processing that tends to be unstructured in nature.
Over time SQL has undergone many iterations to support the vast quantity of data processing and pipelines. However, still, it is not the efficient answer to Big Data systems expecting a quick response and supreme scalability.
A new approach known NoSQL was introduced was devised to work around the limitations imposed by the former. NoSQL system was designed to provide fast scalability when dealing with unstructured data platforms or handle Big Data applications.
NoSQL databases use a key-value pair, Documents, graph databases, or wide–column stores without a typical schema. It is also horizontally scalable as opposed to only vertical scaling in RDBMS.
NewSQL is a term that’s used to describe product offerings that support the relational data model while delivering the same scalable performance of NoSQL database systems.
NoSQL showed a great promise to be an ideal database system for Big Data applications, but like anything else, it fell short due to some major drawbacks discussed below.
This is where NewSQL came alive. NewSQL is the latest development in the world of database systems. NewSQL is a relational database with the scalable properties of NoSQL.
What is SQL Relational Database?
The term SQL is both a language and the type of database. SQL stood for structured query language and was the pioneer of database design philosophy. Since the mid-’80s, SQL has been a standard for managing and querying relational datasets; however, the early beginnings of the relational model can be dated back to the ’60s and ’70s where the urgent need to distinguish between the application data and application code emerged, allowing the developers to focus on other aspects of the program development such as access to and manipulation of data at hand. IBM’s IMS was the first fully functional relational database, albeit designed for a different purpose, to organize data for the Apollo space exploration program.
The relational database is a collection of time-varying, normalized relations of assorted degrees. The following intuitive correspondence can be made.
- A relation is a file.
- Each file contains only one record type
- The records have no particular order
- Every field has single-value
- The records have a unique identifying field or composite field, called the primary key field.
Concepts of SQL Relational Database
Atomicity, Consistency, Isolation, Durability to maintain the reliability of transactions.
Atomicity – completion of the transaction as a whole or none at all
Consistency – assures the stable state of the database with or without changes
Isolation – multiple transactions do not interfere with each other
Durability – permanent effect on the database by the changes
A process of designing efficient databases
1NF – Split the table by separating repeating and nonrepeating attributes. All domains are simple, and all elements are atomic
2NF – Remove partial dependency between attributes. No attribute should be functionally dependent on one part of an aggregated primary key.
3NF – Remove transitive dependency between table attributes. No prime attribute is functionally dependent on the non-prime attribute.
The capability of the database to handle growing amounts of data. Vertical scaling helps to enhance the existing capacity of the database server. Most SQL databases support vertical scaling. They can, however, scale-up, not scale-out.
A domain is a named set of scalar values, all of the same type. They help to impose semantic constraints.
Rely on traditional features and utilizes a defined data schema.
Support JOIN functionality, engineered for data integrity
Drawbacks of SQL Relational Database
Although RDBMS provides exclusive features, they suffer greatly from some major drawbacks.
The Rigidity of Data Modeling
One of the relational database’s biggest limitations is the rigidity of organizing the data into the particular structure in tables and relations. Since all the data cannot be fitted into tables conveniently, hence this approach can’t be applied to all-natural data and be store as trees and graphs
But, RDBMS work around this limitation by modeling this data in a normalized manner with parent-child relations, which is still not enough
The complexity of the data also creates a limitation in the relational database. These databases are made to organize the data by common characteristics. Complex numbers, images, and multimedia data are hard to store, access and process.
Inefficient Usage of Space
When we define the schema of the relation, we define the size of all the attributes. Not all the records have data that use the full space. Some have a short length. Every record needn’t necessarily fit into the given data type again, resulting in space wastage.
Heavy Weight Changes
Any changes required for one record are needed to be applied to all of the records. Thus resulting in a heavyweight change. Depending on the size and number of records present then, the changes can be expensive and infeasible. Thus it was challenging to change the schema of a database which is already existing.
Inefficient for Big Data
SQL is not suitable for volume, velocity, and variety data, rendering it highly inefficient for a cloud-based application.
Click to know more about NoSQL Databases
What is NoSQL?
These problems became the origin of the impetus for the NoSQL movement in the mid to late 2000s. The key working strategy being that they forgo strong transactional guarantees and relational model of DBMS in favor of eventual consistency and alternative data models like key-value pair, graphs. This was done following the belief that these aspects of existing DBMS inhibit their ability to scale out and achieve the high availability needed to support web apps on the go. The two most well-known systems that followed the creed were Google’s BigTable and Amazon’s Dynamo and were restricted to use inside their own organization’s only, leading to organizations creating their own open-source clones like Facebook’s Cassandra and powerset’s HBase. By the end of the 2000s, there was a diverse set of scalable and affordable DBMS.
Concepts of NoSQL
Lack of schema
Support for structured, semi-structured, and unstructured data. No specific schema require to define before entering data into NoSQL databases. New fields can be added and also supports nested data implementation and retrieval. Developers can use data type and query options requisite for the specific application resulting in faster development. Faster Development time is considerably reduced due to no complex SQL queries or join statements.
Division of data among multiple servers automatically, with no assistance required from applications.
NoSQL database cache data in system memory to increase data throughput and increase the performance in advance.
High scalability, reliability with a simple data model and simple query language.
The BASE Principle For Transaction
The base is to NoSql as what ACID is to SQL. It ensures that NoSQL databases ensure their reliability despite the loss of consistency. The base stands for Basically Available Soft-state Eventually consistent.
Eventually consistent – System can become eventually consistent, information is updated wherever necessary.
Drawbacks of NoSQL
Lack of Consistency
Since these systems prefer availability over consistency, they fail miserably when consistency is the most important thing in financial transactions. There is a risk of system failure due to the non-synchronization of data nodes.
Lack of Analytics
For analytics, you require a relational model to process the data, as a result of which the whole database needs to converted using some relational model. This leads to increased cost overhead.
Lack of Standardization
No specific language
Doesn’t provide security at the elemental level of data.
It is important to facilitate fraud detection before completing the transaction and check for balance whilst on a call. NoSQL fails when the database needs to compete against high-volume transactions per day as they require a highly scalable, consistent database.
Read more about Database Managed Service and Solutions.
What is NewSQL?
The early counter-measures to the above approaches were a powerful single node machine that can handle all the transactions, a custom-built middleware system to distribute queries over traditional DBMS nodes. But both of them are prohibitively expensive to carry out.
As a result, there was a need for an intermediate database system that combines NoSQL systems’ distributed architectures with multiple node concurrency and a whole new storage mechanism. Thus Newsql can be defined as a class of modern relational DBMSs that seek to provide the same scalable performance of NoSQL for OLTP workloads and simultaneously to guarantee ACID compliance for transactions as in RDBMS. In other words, these systems want to achieve the scalability of NoSQL without having to discard the relational model with SQL and transaction support of the legacy DBMS.
Concepts of NewSQL
- Main Memory storage of OLTP databases enables in-memory computations of databases.
- Scaling out by splitting a database into disjoint subsets called either partitions or shards, leading to the execution of a query into multiple partitions and then combining them into a single result.
- NewSQL systems preserve the ACID properties of databases.
- Enhanced concurrency control system benefits traditional ones.
- Presence of a secondary index allowing NewSQL to support faster query processing times.
- High availability and Strong data durability only possible with the use of replication mechanisms.
- Configure NewSQL systems to provide synchronous updates of data over the WAN.
- Minimizes Downtime, provides fault tolerance with its crash recovery mechanism.
Difference Between SQL, NoSQL, and NewSQL?
|Relational Property||Yes, it follows relational modeling to a large extent.||No, it doesn’t follow a relational model. It was designed to be entirely different from that.||Yes, since the relational model is equally essential for real-time analytics.|
|ACID||Yes, ACID properties are fundamental to their application||No, rather provides for CAP support||Yes, Acid properties are taken care of.|
|SQL||Support for SQL||No support for old SQL||Yes, proper support and even enhanced functionalities for Old SQL|
|OLTP||Inefficient for OLTP databases.||It supports such databases, but it is not the best suited.||Fully functionally supports OLTP databases and is highly efficient|
|Scaling||Vertical scaling||Only Vertical scaling||Vertical + Horizontal scaling|
|Query Handling||Can handle simple queries with ease and fails when they get complex in nature||Better than SQL for processing complex queries||Highly efficient to process complex queries and smaller queries.|
SQL, NoSQL, or NewSQL – Which is the Best Solution to Big Data?
SQL complies with ACID properties and does well with vertical scalability, while NoSQL offers its own horizontal scaling and provides for BASE properties. However, NoSQL does not play by ACID rules necessary to maintain a reliable and consistent database. The fast-paced enterprises and organizations generate terabytes of transactional data daily while working in an OLTP system, NewSQL is the ideal choice. NewSQL improves upon SQL by providing horizontal scalability whilst maintaining ACID properties. This facilitates working with Big Data by implementing concurrency. It also does well with ACID compliance. NewSQL thus seems to have found the sweet spot between speed, scalability, consistency, and availability. Even though it is still in its nascent stage, NewSQL ticks all the right boxes to be an ideal database for Big Data and OLTP applications. You can also explore the difference between Virlet and Kubevirt in this blog.
How Can XenonStack Help You?
With XenonStack Database Managed Services, you get 24×7 access to our DBAs, architects, and engineers to handle day-to-day activities. XenonStack Database Managed Services offers Database Monitoring, Incident Management, DB Performance Tuning, Backup/Recovery Management, Database Security Management, Database Security Audits, and more. Get in Touch with us.