NoSQL vs SQL: What, Where, and How
This comprehensive guide will help beginners understand the differences between SQL and NoSQL, their use cases, and performance scenarios.
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As a beginner, it is essential to understand the two most commonly used types of databases: SQL and NoSQL. In this article, I have tried my best to provide a comprehensive guide that will help beginners to understand the differences between SQL and NoSQL, their use cases, and the scenarios in which they perform better than the other. The information here will provide you with an overview of SQL and NoSQL databases and highlight the advantages and disadvantages of each. By the end of this article, you will be able to make an informed decision on which type of database to use for your project. Whether you are a software developer, a data analyst, or a business owner looking to store and manage your data, this information is valuable and relevant to you.
So, let's dive in and explore the world of SQL and NoSQL databases.
Facts About SQL and NoSQL
- SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Edgar F. Codd in the early 1970s.
- The term NoSQL was used by Carlo Strozzi in 1998.
- Oracle brought the first commercial relational database to market in 1979 followed by DB2, SAP Sybase ASE, and Informix.
- NoSQL databases are not a replacement for relational databases but rather offer an alternative solution for certain use cases.
- SQL databases offer a high degree of data consistency and transactional support, making them a popular choice for applications that require data integrity and reliability.
- NoSQL databases are often horizontally scalable, meaning they can easily distribute data across multiple servers, allowing for greater scalability.
- The CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that any distributed data store can provide only two of the three guarantees:
There are no hard and fast rules for when to use SQL or NoSQL, and the best choice for a particular project will depend on the specific needs and constraints of the project.
SQL databases are generally more widely used than NoSQL databases. According to a survey by DB-Engines, the top five most popular databases in terms of popularity and usage are all SQL databases (Oracle, MySQL, Microsoft SQL Server, PostgreSQL, and SQLite).
Real-World Applications That Use SQL or NoSQL
- Twitter uses a NoSQL database (Cassandra) to store and manage the massive amount of data generated by its users. They say, "Our geo team uses it to store and query their database of places of interest. The research team uses it to store the results of data mining done over our entire user base."
- Netflix uses a combination of SQL and NoSQL databases to store and manage data related to its streaming service. The company uses a SQL database (MySQL) to store structured, transactional data such as subscriber information and billing records, and a NoSQL database (Cassandra) to store data related to user interactions and recommendations.
- LinkedIn uses a combination of SQL and NoSQL databases to store and manage data related to its professional networking platform. Espresso is LinkedIn's online, distributed, fault-tolerant NoSQL database that currently powers approximately 30 LinkedIn applications including Member Profile, InMail (LinkedIn's member-to-member messaging system), portions of the Homepage, and mobile applications.
- Facebook uses MySQL as a primary database, an open-source database developed by Oracle, that powers some of Facebook’s most important workloads. They introduced MyRocks, a new MySQL database engine, with the goal of improving space and write efficiency beyond what was possible with compressed InnoDB.
- Stack Overflow uses SQL Server. Nick Craver wrote in one of his blogs that Stack Overflow is using SQL Server as single source of truth. All data in Elastic and Redis comes from SQL Server. They run two SQL Server clusters with AlwaysOn Availability Groups.
Use Cases for SQL and NoSQL in Different Businesses
- Financial systems
- Customer relationship management (CRM) systems
- Inventory management systems
- Human resources (HR) systems
- Data warehousing and business intelligence (BI) systems
- Social media networks
- E-commerce websites
- Real-time analytics systems
- Mobile app backends
- Content management systems (CMS)
These are just a few examples, and there are many other use cases for both SQL and NoSQL.
The best technology for a particular project will depend on the specific needs and constraints of the project.
Database in the Cloud
Most major cloud providers offer a variety of SQL and NoSQL databases as a service. Here are a few examples of the types of databases offered by some of the major cloud providers:
- Amazon Web Services (AWS) offers a range of SQL and NoSQL databases, including:
- Microsoft Azure offers a range of SQL and NoSQL databases, including:
- SQL: Azure SQL Database (relational), Azure Database for MySQL, Azure Database for PostgreSQL
- NoSQL: Azure Cosmos DB (multi-model), Azure Table Storage (key-value)
- Google Cloud Platform offers a range of SQL and NoSQL databases, including:
Best Practices on Selecting Between SQL and No SQL
When choosing between SQL and NoSQL for a particular project, there are a few best practices to keep in mind (this is not the final list):
- Understand the specific needs and constraints of your project. This will help you determine which technology is the best fit.
- Consider the type and structure of the data you are working with. SQL is well-suited for structured, transactional data with well-defined relationships, while NoSQL is better for handling unstructured, high-volume data with less-defined relationships. (Again, your project and use case will decide this.)
- Evaluate the scalability and performance requirements of your application. You must have heard NoSQL databases are generally more scalable and performant than SQL databases, but this may not always be the case.
- Consider the level of consistency and reliability you need. SQL databases are generally more predictable and consistent, but NoSQL databases offer more flexibility.
- Test different technologies to see which one performs best in your particular use case. This will help you make an informed decision.
- Both SQL and NoSQL databases can offer high availability and durability, depending on the specific implementation and the use of techniques such as replication and sharding.
- Everyone is using NoSQL and so doing this is not always the right strategy.
Tools To Help Decide
To help decide between SQL and NoSQL for an enterprise application, you might consider using tools such as database performance benchmarking tools, database design and modeling tools, and database management and monitoring tools. Some examples of these types of tools include:
- MySQL Workbench
- MongoDB Compass
- Redis Desktop Manager
Causes of Database Implementation Failures
- Poorly designed data models or schemas that do not meet the needs of the application
- Inadequate performance testing or optimization, resulting in poor database performance
- Lack of robust backup and recovery processes, leading to data loss or corruption
- Insufficient planning or resources for database maintenance and support
Common Failure and Exception
- Connection failures - When there is a problem establishing a connection to the database, such as when the database server is not running or the connection details are incorrect
- Resolution: Establishing robust connection management and retry strategies to handle connection failures
- Query failures - A problem while executing a query, something like when the query syntax is invalid or the query is taking too long to execute
- Resolution: Debugging and optimizing queries to improve performance
- Transaction failures -If there is a problem with a database transaction like a transaction is canceled or rolled back due to a deadlock or a constraint violation
- Resolution: Implementing proper transaction management to minimize the risk of transaction failures
- Data corruption - This can occur when there is a problem with the data stored in the database, such as when data becomes corrupted or is lost due to a hardware failure or a software bug.
- Resolution: Implementing backup and recovery strategies to mitigate the risk of data loss or corruption
- Performance issues: An underperformance in database queries like slowness or the database consuming too many resources
- Resolution: Monitoring and tuning the database to identify and address performance issues
Deployment Architectures for Databases
- Standalone server: In this architecture, the database is installed on a single server and accessed directly by the application. This is the simplest and easiest deployment option, but it is not suitable for high-scale or high-availability applications.
- Replication: Here, the database is deployed on multiple servers, with each server hosting a copy of the data. The servers are configured in a replica set, and one of the servers is designated as the primary. Applications write to the primary and the data is automatically replicated to the other servers. This provides improved availability and fault tolerance but does not offer horizontal scalability.
- Sharding: This is the same as with replication where the database is deployed on multiple servers, and the data is partitioned across the servers. Here partitions are called shards, and the servers are organized into a sharded cluster. Applications write to the cluster and the data is automatically routed to the appropriate shard. This style provides improved scalability and performance while requiring additional configuration and management.
- Cloud-managed service: A cloud provider manages the database and is accessed by APIs. This may be the easiest way to deploy and manage. On the other hand, it might be expensive, and control and customization will be less as compared to others.
What Can Cause the Performance Issues in Database
- Insufficient resources
- Poorly designed queries
- Indexing issues
- Schema are not optimized
- Sharding issues
- Network latency or bandwidth
My Personal Experience Using SQL and NOSQL
I was part of an enterprise API development team where initially, we started using SQL database. Later when our organization adopted NoSQL, we moved there considering the fact that we will scale and everything else will be smooth.
However, we started getting challenges like scale, performance, indexing, etc. One of the challenges of using NoSQL databases is that they often lack the strong data consistency guarantees that are provided by relational databases. You need to remember the "eventual consistency" in a distributed environment. This means that it is possible for data to become inconsistent or outdated in certain scenarios, such as when multiple clients are updating the same data simultaneously.
So as beginners, we never thought of this scenario and gradually started learning and redesigning the database architecture to move from records to documents. NoSQL databases are designed to handle large amounts of data and high read and write throughput, but optimizing their performance requires a deep understanding of the database's architecture and configuration settings.
A shift is required from a relational-only mindset. A database is a place to store data and a specific data structure is followed. Think of moving from a store procedure full of business logic to app-only business logic: there will be no logic inside the database. One has to be better at data modeling and designing indexes while using NoSQL to its full potential.
Where To Go From Here
Both NoSQL and SQL databases have their unique advantages and limitations, and the choice between them depends on the specific requirements and use cases of a particular project. It is important to carefully consider the trade-offs and benefits of each option before making a decision.
Note: The information and examples provided in these topics are for educational purposes only. The specific implementation and deployment database may vary depending on the specific requirements and constraints of the application. It is important to carefully plan and design the database deployment and architecture to ensure optimal performance and scalability.
I hope you've learned as much as I have. Until next time: sharing is caring.
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