DEV Community: Oghenerukevwe Emeni

FRAMEWORKS IN GOLANG.

Oghenerukevwe Emeni — Wed, 02 Nov 2022 00:12:59 +0000

Golang (or Go) is an open-source compiled programming language that is used to create software that is simple, systematic, and secure. It was created by Google in 2007 and has been widely adopted by developers worldwide due to features such as memory safety, structural typing, garbage collection, and similarity to C-language.

Hello World in Golang.
Hello, World! is the first basic program in any programming language. Let’s write the first program in the Go Language using the following steps:

First of all open Go compiler. In Go language, the program is saved with .go extension and it is a UTF-8 text file.
Now, add the package main in your program. Each program must begin with a package declaration. Packages are used in the Go programming language to organize and reuse code. There are two types of programs available in Go: executable programs and libraries. Executable programs are those that can be run directly from the terminal, whereas libraries are collections of programs that can be reused in our program. The package main instructs the compiler that the package should be compiled as an executable program rather than a shared library. It is the program's starting point and also contains the main function.
After adding main package import “fmt” package in your program: The import keyword is used in this case to import packages into your program, and the fmt package is used to implement formatted Input/Output with functions.
Now write the code in the main function to print hello world in Go language.

Result

Reasons to choose Golang for your next project.
1. Simplified code.
Golang code is simpler and incorporates ideas from various other programming languages. This has many advantages because it saves developers a ton of time by having them navigate fewer lines of code. This is significant because reading code takes longer than writing it.

2. Powerful performance.
The performance of Go is strong despite its simplicity. It has shorter software development lifecycles because it runs and compiles more quickly, is simple to support and maintain, and runs quickly.

3. Easy to learn.
Golang is frequently compared by programmers to C but without the annoying flaws. If you are familiar with the C concepts, learning it will be easier because its syntax seems familiar to developers.

4. Easy maintenance.
Businesses using Go have cited its abundance of tools for automatic code maintenance as one advantage. The outcomes are nearly exact replicas of what would be obtained from human programmers.

5. Uses open source.
Golang has a large following among programmers and a vibrant community because it is open-source. Go promotes the use of innovative solutions while the community quickly identifies and fixes coding errors through forums, tutorials, and open-source projects.

6. Created with multi-core processors in mind.
Go was created for the cloud computing era of today and the parallel processing of today's processors. Before multi-core computers became widely used, other well-known programming languages such as Java, JavaScript, Python, Ruby, or C, C ++ were developed. Golang makes it simpler to use every CPU core without adding extra complexity to development.

Golang is used by various companies across all industries. Some of them are:
Uber
Dropbox
Alibaba
Capital One
SoundCloud
American Express

Frameworks.
You don't have to start from square one if you're a programmer. There are resources available to assist you with your projects. Developers create frameworks, which are software tools they use to create applications.
A framework, or software framework, is a platform for developing software applications.
Software development is a difficult process. Numerous tasks, such as coding, designing, and testing, are required. Programmers had to deal with the syntax, declarations, garbage collection, statements, exceptions, and more just for the coding portion. By enabling developers to control the majority of the software development process from a single platform, software frameworks make their lives easier.

Benefits Of Using A Framework.

It helps establish better programming standards and appropriate design pattern usage.
Code is more secure.
It makes it possible to avoid redundant and duplicate code.
It helps develop consistently bug-free code.
It facilitates working on advanced technologies.

Golang Frameworks.
Golang web frameworks are used to write application programming interfaces (APIs) and web services straight away.
1. gin.
Due to its performance and minimalist design, the gin framework is the most popular Go framework. If a programmer wants to create a single-page application using a frontend framework, it is generally used to build a REST API for the backend. With its comprehensive documentation available on GitHub, this framework is perfect for beginners and uses HTTP routers to handle Golang traffic. It is the best Golang framework for creating high-performance REST APIs because it supports the most crucial libraries and features.
The biggest drawback is that, despite being appropriate for smaller applications, it is not appropriate for the development of large backend applications or complex enterprise-level server functions.
Documentation Link: https://github.com/gin-gonic/gin#gin-web-framework

2. echo.
Golang's echo framework is a high-performance, extensible, and simple web framework. It intelligently prioritizes routes and has a highly optimized HTTP router with no dynamic memory allocation. It is used to create dependable, scalable, and easily segmentable REST APIs. It supports HTTP/2, which increases speed and offers a better user experience, and automatically installs TLS certificates from Let's Encrypt. Additionally, there are numerous built-in middlewares available for use, and programmers can even create their own custom middlewares that can be set at the root, group, or route level.
The echo framework's disadvantage is that there is just one developer who maintains it, and there aren't many updates to the code.
Documentation Link: https://github.com/labstack/echo

3. beego.
The beego framework is used for the rapid development of REST APIs, web applications, and backend services in Golang. It has specific Golang characteristics like interfaces and struct embedding and is frequently compared to the Python Django web framework. No third-party installations are necessary. It is an entire Model-View-Controller (MVC) framework with its own libraries and the Bee Tool, an integrated tool that aids in locating code changes.
The only major downside to beego is that because of its high functionality and numerous features, it is not the best choice for beginners.
Documentation Link: https://github.com/astaxie/beego

4. fasthttp.
The fasthttp framework provides a fast HTTP server and client API which was made as an alternative to net/http due to its limits on optimization opportunities. On current hardware, it can easily handle over 100K qps and over 1M concurrent keep-alive connections thanks to its speed optimization. Additionally, it is designed to use little memory and offers RequestCtx for simple connection upgrade support. Hijack.
The Fasthttp API was created with the flexibility to create new client and server implementations from scratch or to extend existing client and server implementations.
Documentation Link: https://github.com/valyala/fasthttp

5. kit.
The kit framework is a programming toolkit for building robust, reliable, and maintainable microservices in Golang. It is a collection of packages and best practices that offer businesses of all sizes a thorough, reliable, and trustworthy way to create microservices. Go is a fantastic general-purpose language, but microservices need some specialized assistance.
As a result, the kit framework offers infrastructure integration, system observability, and Remote Procedure Call (RPC) safety. Golang is a first-class language for creating microservices in any organization thanks to its composition of numerous closely related packages that together form an opinionated framework for building substantial Service-Oriented Architectures (SOAs).It was created with interoperability in mind, and developers are free to select the platforms, databases, components, and architectural styles that best suit their needs.
The disadvantage of using go-kit is that it has a high overhead for adding API to the service because of how heavily it relies on interfaces.
Documentation Link: https://github.com/go-kit/kit

Make sure you fully understand the necessary requirements for each framework listed here before implementing it because each one of them has a unique set of features to try out. Not only limited to these, Golang also has a set of numerous other frameworks to offer like Buffalo, Martini, Goji, net/http, Revel, etc. Feel free to check them out.

Product Scalability In Startups And How To Choose A Database As A Lead Engineer.

Oghenerukevwe Emeni — Mon, 24 Oct 2022 18:42:37 +0000

As a startup you need to make the most of your data to make sure you grow your business as fast as possible. Most online businesses are built around databases, they lie at the heart of online businesses quietly doing their job which is often unknown to the less tech-minded. Building and choosing a database for a startup can be complicated if you don't know much about the subject. This article intends to explain how to go about the process, which you would find is not as hard as it seems.
Startups often start out by launching a Minimum Viable Product (MVP). This is done to test waters and find the right audience for the product. No business wants to spend money on a product only to discover that it has no market. It is a common launch method that has been extremely successful for household companies like Figma, Dropbox and Uber. A minimum viable product is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. It is a basic, launchable version of the product that supports minimal but essential features. The “least effort” does not, however, mean that an MVP is the “minimum” functionality to be developed. It must also be "viable," which means that when designing it, keep in mind that the MVP needs to be usable, reliable and considerate of user needs.
However, just because an MVP is the most basic version of your product, it doesn't mean it shouldn't be scalable. You should still aim to create something that scales easily and works really well to aid your transition from the MVP stage to the fully functional business stage or Full-Scale Product. A database is necessary, whether it's for the Minimum Viable Product or the Full-Scale Product.

What is a Database?
A database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. A database is usually controlled by a database management system (DBMS). A database system, frequently abbreviated to just "database", is the collective term for the data, the DBMS, and the applications that are associated to it. The most popular types of databases in use today typically model their data in rows and columns in a series of tables to facilitate processing and data querying. The data can then be easily organized, updated, accessed, modified, managed and controlled.

Database Scalability.
Database scalability is the ability of a database to handle changing demands by adding/removing resources. Scalability is the ability to handle increased workloads without compromising significant performance. In this context, scalability means that a database can handle an increasing amount of requests from the application to store, modify, and retrieve data.

How do you scale a database?
There are two types of database scaling; Vertical Scaling and Horizontal Scaling. Databases are scaled either vertically (by adding more resources to existing machines) or horizontally (by adding more machines, distributing data, and processing across those machines).

Vertical Scaling.
Vertical scaling (also known as scaling up) refers to the addition of new resources to a system in order to meet demand. Vertical scaling refers to increasing the power of your existing machines. If your server requires more processing power, for example, in vertical scaling you would simply upgrade the CPUs. You can also scale memory, storage, and network speed vertically. Vertical scaling can also refer to completely replacing a server or moving a server's workload to a more powerful one.

Horizontal scaling.
Horizontal scaling (also known as scaling out) refers to adding new nodes or machines to your infrastructure to meet increased demand. If you host an application on a server and discover that it no longer has the capacity or capabilities to handle traffic, adding a server may be the solution. It's similar to splitting the workload among several employees rather than just one. However, the added complexity of your operation may be a disadvantage. You must determine which machine does what and how your new machines will interact with your old ones.
So which option is preferable? Neither is better. Choosing a type of scalability depends on the operation. Vertical scaling is typically easier but more limited. Horizontal scaling is more complex but handles larger loads in terms of number of requests served as well as total data storage.

Why do you need a scalable database?
1.You can accommodate for changes in demand
A scalable database, as its name suggests, allows your database to expand at the same rate as your business. This ensures that you never find yourself in a position where you are unable to perform to your best capacity. Many people overlook the fact that you can scale down as well as up. In order to lower operating costs, a company may seek to scale back if demand declines or if they require fewer resources.

2.You will understand bottleneck issues.
It's probable that there is a data bottleneck issue if your database starts to operate slowly. This is typically brought on by heavy CPU utilization, and it can mean that your server needs to be upgraded. High CPU consumption may also indicate that your database's software needs to be addressed since the operating system is using more CPU power than it should. An alternative explanation for poor CPU performance is that the CPU is waiting on the input/output.

Types of Database Systems.
1‌. Relational Database Management System. (RDMS) / Structured Database.
‌2. Non-Relational Database Management System. (NON-RDMS) / Unstructured Database.

Relational Database Management System.
In RDBMS, data is stored in a tabular format. For example, a store could store details of their customers’ names and addresses in one table and details of their orders in another. This form of data storage is often called structured data. See the image below:

Structured Query Language is used by relational databases (SQL). The database in relational database design typically has tables with rows and columns. New entries are either added to already-existing tables or new tables are created when new data is provided. Then, relationships between two or more tables can be created.
When accuracy is vital and the data they hold doesn't change frequently, relational databases perform well. For instance, relational databases are frequently used in financial applications. Examples of relational database include MySQL, PostgreSQL, IBM Db2, Oracle Database etc.

Non-Relational Database Management System.
In comparison to the conventional relational database architecture built on SQL, a non-relational database(often called NoSQL databases) stores data in a non-tabular format. It doesn't use the relational model that traditional relational database management systems give. Non-relational databases, on the other hand, might be built using data structures like documents. Despite carrying a variety of different types of information in various formats, a document can be extremely detailed. Non-relational databases are much more flexible than relational databases due to their capacity to process and organize numerous types of information simultaneously. See the image below:

When organizing large amounts of complex and diverse data, non-relational databases are frequently used. For instance, a large store might have a database where each customer has a personal record with all of their information, including name, address, order history, and credit card details. Each of these bits of information can be stored in the same document despite their various formats.
Because a query doesn't need to view multiple tables to provide an answer, as it frequently does with relational databases, non-relational databases often operate faster. Therefore, non-relational databases are perfect for applications that manage a wide variety of data types or for storing data that may be modified often. They can support rapidly developing applications that need a dynamic database with the flexibility to adapt and support huge quantities of complex, unstructured data.

Real World Applications.
Relational Database Example.
Healthcare Database.
The storage of patient healthcare data is one typical application for relational databases. One healthcare provider, for instance, might see patients at many locations. A table called "Office" would be created as a result, containing data like the office's name and address. There could be numerous doctors working out of one office, indicating a one-to-many interaction between offices and physicians. Offices and patients have a similar one-to-many interaction. One-to-many relationship entities are often stored in different tables. Separate tables would be needed for "Doctor" and "Patient," in this case. These tables would have columns for their individual ID, address, and other relevant data.
Shared columns would define the connection between the tables. For instance, to reflect the location of the doctor's office, a column in the "Doctor" database would hold the officeId value from the "Office" field. A patient's doctor and the office they visit for appointments would both be connected to them via the attributes officeId and doctorId, respectively.

Non-Relational Database Example.
Graph Database Example
A graph database uses nodes to hold data, and edges define relationships between them.
Movie Database
Information about movies and the people who make them is one type of data you can store in a graph database. The nodes provide details on items like a person or movie. The links between them, such as an individual acting in, directing, or producing a movie, are defined by the margins.
This is beneficial for creating queries for movie recommendations. After watching a movie with a particular actor, a viewer may be recommended further movies with that actor or movies with that actor as the director.

Both Relational and Non-Relational Database Example.
Financial Payment Application.
Financial payment applications like PayPal where you can chat with who you send or receive money from are great examples of where relational and non-relational databases are both incorporated. The chat platform requires a non-relational or unstructured database while the financial aspect of the application requires a relational or structured database.

Advantages of Relational Databases.
1 - Simplicity of Model
The relational database model is substantially less complex than other kinds of database models. Simple SQL queries are sufficient to handle the data because there is no query processing or structuring required.
2 - Usability
Users don't have to deal with the database's complexity in order to quickly access or retrieve the data they need. Complex queries are executed using SQL (Structured Query Language).
3 - Precision
Relational databases are distinguished by their strict definition and excellent organization, which prevents data duplication. Relational databases' structure, which prevents data duplication, contributes to their correctness.
4 - Data Integrity
Since RDBMS databases offer consistency across all tables, they are also frequently employed for data integrity. Accuracy and usability are features that are guaranteed by data integrity.
5 - Normalization
Database normalization guarantees that a relational database's structure is uniform and consistent and that it can be accurately changed. This guarantees that the integrity of the data used in this database for your business choices is upheld.
6 – Collaboration
At the same time as data is being updated, multiple users can access the database to retrieve information.
7 – Security
Data is secure because the Relational Database Management System restricts direct access to only authorized users. The information is not accessible to unauthorized users.

Disadvantages of Relational Databases.
1 - Maintenance Issue
Due to the increase in data, maintaining a relational database becomes difficult over time. Developers and programmers must devote a significant amount of time to database maintenance.
2 – Cost
Setting up and maintaining a relational database system is expensive. The cost of the software alone can be prohibitively expensive for small businesses, but it gets worse when you consider hiring a professional technician who must also be knowledgeable about that particular program.
3 - Physical Storage
A relational database is made up of rows and columns, which necessitates a large amount of physical memory because each operation requires its own storage. Physical memory requirements may rise in tandem with data growth.
4 - Inadequate Scalability
When a relational database is used across multiple servers, its structure changes and becomes difficult to manage, especially when the amount of data is large. As a result, the data is not scalable across physical storage servers.
5 - Structure Complexity
Relational databases can only store data in tabular form, making complex relationships between objects difficult to represent. This is a problem because many applications require multiple tables to store all of the data required by their application logic.
6 - Performance decline over time
The relational database can become slower due to factors other than its reliance on multiple tables. When there are a large number of tables and data in the system, the system becomes more complex. Depending on how many people are logged into the server at any given time, this can result in slow query response times or even complete failure.

Advantages of Non-Relational Databases.
1 - Flexible Scalability
They are highly scalable and can be tailored to your company's specific scaling requirements. They can be scaled horizontally rather than vertically, giving them a clear advantage over SQL databases. The lack of data structure allows NoSQL databases to scale horizontally.
2 - Flexible Data Types
They enable you to store and retrieve data with minimal or no reliance on a predefined schema. This means that your application can quickly adapt as new types of information are added without having to change table structures or indexes.
3 - Large data storage capacities
Because they can handle large datasets, they are ideal for big data applications and other real-time analytics.
4 - Simplicity and less code
They require only a few lines of code, which is ideal for developers who want to get started quickly.
5 - Less Ongoing Database Maintenance
They don't require the same level of ongoing database administration as traditional relational databases because they can automatically partition and replicate information across nodes.

Disadvantages of Non-Relational Databases.
1 - Queries are less flexible.
They are more flexible when storing a wide variety of data structures, but they lack the complex query functionality found in SQL.
2 - It is Relatively New.
Since it hasn't been around for long, it may be more challenging to find solutions to problems.
3 - It isn't designed to scale by itself.
While there are ways to scale out your application using some NoSQL database management systems like BigTable or MongoDB replica sets, their design limits the amount of traffic they can accommodate by themselves