Efficient code can be defined by numerous factors, in addition to what others deem necessary based on their experiences. Per this article, we will define it as:
Measuring and comparing your code on its design, optimization, and performance; while considering its use cases, compute resources, quality, scalability, and structure.
Along with our definition, it is important to understand some key concepts before we dive further into some best practices and additional support to contextualize our definition.
Key Concepts For Efficient Code Design
Key concepts may vary within the industry but you should familiarize yourself with the following as a starting point:
- Consistency – A baseline for best practices and standard guidelines across your team’s code. The fuel to establish good coding habits and efficient operations.
- Maintainability – The ability to understand, predict, improve, scale, and debug your code without major rewrites or reworks, and implementing them without disruptions.
- Readability – Code that is easily understood by those who review it. Ranging from format, code flow and control, security, simplicity, structure, and organization.
- Testing – Measuring your code, and understanding what it is expected to do, along with its operational abilities — to include load, speed, stress, and environmental tests.
- Performance – The ability to understand your code as it operates daily — including threshold limits, resource utilization, traffic loads, use and edge cases, stability, and speed.
Best Practices For Comments, Structure, Reusable, And Repeatable Code
When thinking of ways to improve the efficiency and quality of your code consider implementing comments, structure, and ways to make it reusable and repeatable.
They provide organization, enhance, and enable effectiveness on all ends of your service. Giving you the ability to enable consistency and quality to stand out in your service, among your organization and consumers. So let’s talk about them!
Commenting your code
Comments in your code should be descriptive, yet simple. They should convey purpose and nuances that prove useful for yourself and others to understand.
When implemented properly, comments can assist in development, code reviews, deployments, maintainability, and troubleshooting processes. Keep in mind that comments should not be used in place of bad code but complement thoughtfully designed code. Refer to the examples below:
A comment to save debugging hours or preventing someone from switching an API call within the code, compared to what is normally used:
// Code uses 'X' API because 'Y' API breaks the code sometimes.
Good comment:
/* This function utilizes 'X' API and not the usual 'Y' API due to a recurring bug that occurs when 'Y' API updates on a weekly basis. This update causes malformed data that breaks the code. Until 'Y' API no longer has this issue, we must use 'X' API to prevent service disruption.*/
A comment to explain why float value types are being used:
Bad comment:
// This returns float values because the code performs better.
Good comment:
// Values must return float type to calculate KPI results accurately for 'X' metrics.
Adding structure to your code
Much like comments, structure in your code and packages should enhance the quality of your code. It should empower efficiency and enable seamless implementation, to scale your applications and manage version control. Understanding the differences between bad and good structure is important to achieve this.
Bad structure can lead to hours of tedious code-digging to find out what something is, where it’s located, what is outdated, or if there is a single-point of failure. Worse case, it can lead to refactoring your entire code base when it isn’t necessary.
Some examples of bad structure include:
- Creating a program and placing your code all into one file;
- Not having any dependency tracking;
- Not organizing and keeping modules separated;
- Improper formatting or bad labeling within your packages.
Good structure should save time in developing, maintaining, and debugging your code and packages.
Examples include:
- Proper formatting guidelines;
- Applicable algorithm designs and inheritance integrations;
- File and folder organization for components, backend, and frontend applications;
- Settings file for dependency management and version control;
- Reference documentation for security protocols, additional notes, design documents, runbooks, etc.
Reusable and repeatable code
By far two of the most helpful practices you can do for your code.
These two approaches work hand-in-hand, promoting additional best practices for your code’s Software Development Lifecycle (SDLC). They can also enhance the quality, security, and reliability of your code and service.
To assist, ask yourself:
- Can I reuse this in other parts of my code?
- Is this being used somewhere else?
- Are you seeing the same algorithms being used?
- Is there a library or built-in function for this?
While putting these into practice is easier said than done, the good news is that there are ways to help you.
Consider the following:
- Applying structure to your code and packages
- Developing templates
- Applying dynamic values
- Using Object-Oriented Programming (OOP) practices
- Ensure you’re advocating for reusable or repeatable code, when applicable
Compute Resources, Use Cases, And Language Considerations
Compute resources
Compute resources such as memory, storage, and CPU are critical components to consider in your code design and development processes. You may want to create efficient code with blazing-fast speed, but it may not be the most optimal or cost-effective solution.
Understanding what costs you can sacrifice and what resources you can utilize are critical to your design and development process. Additionally, it is necessary to make notes correlating with your approach when it comes to this as it saves time by helping others understand.
Use cases
It is important to consider why and what you’re developing your code for. You can always apply the same code for similar situations.
However, your use cases may not be suited for them. This means you need to consider various factors that contribute to your code. Some examples include infrastructure, traffic, APIs, workloads, operational settings, compute, and operational resources.
For example, you may not be able to implement a piece of your code that you usually use because it doesn’t support the security protocols you need to abide by.
Or maybe you don’t receive the necessary data you need from a specific endpoint you usually use. So you need to use another, because it’s the only one that produces that data. Even if it causes a slower workflow, it still saves hours of time compared to manual execution.
Language considerations
Not all languages are created equal. It is important to be aware of and understand the distinctions, data types, function structure, syntax, and inheritance between each. Additional considerations to keep in mind include language interactions, environment components, parallel programming, and multi-threading.
For instance, multi-threading in Java could speed up your program but will be hindered in Python because of the default Global Interpreter Lock (GIL). Being aware of how data is stored, garbage collection processes, comment interpretation, API communications, security key and value retrieval, and other schema processing are things to consider.
Bottlenecks, Benchmarks, And Performance Testing
Efficient code can refer to the way you design, develop, and maintain it. Another factor that contributes to efficient code is the way you deploy and run it. This includes having clear and concise procedures that outline the contributions you need from others.
Ranging from company-wide schedules, code review processes, benchmarks, and performance measurements.
Bottlenecks
Bottlenecks can be a variety of things. Ranging from a specific component or resource, throughput scenario or edge case, even external factors – such as unclear processes or procedures, code reviews, and external teams or individuals. However, there are ways to improve and eliminate bottlenecks. Some examples include:
- Resource utilization metrics
- One or more SDLC practices, pending the use case to create clear and concise processes
- Improve existing code review guidelines by establishing approval requirements
- Automating tests and deployments to check for security implementations, required reviewers, dependencies, versioning, etc.
Benchmarks and performance testing
Benchmark and performance testing are other efficient approaches for your code.
They can help you identify bottlenecks and improve other issues that may occur due to interactions.
For instance, many languages have libraries that simplify benchmark implementations. There are also ways you can log, test, and measure code performance that correlates with input and output operations and profiles.
This includes space and time complexity tests that assist in identifying if the algorithms and data types being utilized are ideal for your code, while accounting for factors that interact with it – such as your infrastructure and specific use cases.
Being Consistent In Practice
There are several ways to build and maintain efficient code. Advocating, practicing, and implementing them consistently will help you grow as a developer. Which allows you to hone your craft, identify root problems, implement fixes, and scale your service.
It can be a complete game changer that sets an example that inspires and motivates others. Furthermore, it enables efficient and effective solutions for ourselves, our customers, and our services.
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