Introduction
A web app can offer a great user experience with an intuitive design and valuable features, but experience can quickly become frustrating if pages take too long to load or actions seem to take forever. Performance problems often appear gradually as an application gains users, features, integrations, and more data.
Performance problems can be caused by many factors, broken down into components such as user queries to the database, back-end coding, number of API calls, large JavaScript files, and inadequate server resources. Randomly optimizing different elements of a web application may not resolve performance issues.
For teams working on Web Development, identifying the source of the bottleneck before making changes is therefore essential. Performance optimization should begin with measurement and analysis rather than assumptions.
What Is a Performance Bottleneck?
A performance bottleneck is the component of a system that will slow everything else down. Examples of performance bottlenecks are inefficient code, too many API calls, slow server response time, slow database queries, and large images or JS bundles. In this article, we will discuss how to find the component slowing down your web application.
Common bottlenecks include:
- Long access times on databases
- High server response times
- Inefficient application code
- Excessive API requests
- Large images or JavaScript bundles
- Memory or CPU limitations
- Poor application design
- External providers
The first step is figuring out which component is actually responsible.
1. Start by Measuring the Problem
Measuring performance issues is the only way to avoid guessing which performance component may be slowing things down. Once we have measurable performance issues, we explore external factors. Oftentimes it's assumed the database is slow, but external services or large frontend JavaScript may be the problem.
Analyzing performance metrics related to application response time, CPU utilization, memory usage, database calls, and network requests is made easier with performance monitoring tools.
If you are asking, “what should we optimize?” start by asking, “what part of the application takes the longest time?”
Setting performance benchmarks before optimization gives developers a standard against which to compare results.
2. Check Database Queries
Databases are usually the biggest performance bottlenecks in applications that work heavily with data. A query that might appear to be fast in a small data set can become very slow in a big data set.
Slow queries also need to be assessed in terms of how data is being retrieved. Many performance issues can result from missing indexes, unnecessary joins, unnecessarily repeating queries, and retrieving more than required.
Some useful approaches include:
- Adding the appropriate indexes
- Optimizing complex queries
- Selecting only required fields
- Reducing unrequired database calls
- Using pagination on large data sets
Optimizing the database can reduce backend response times with no change to the application’s user interface.
3. Analyze Backend Processing
The database may not be the issue. It may be the application itself that takes a great deal of time to process the request.
Servers spend a lot of time processing a request when there are unnecessary calculations, inefficient loops, or repeated operations. There are many performance profiling tools to find functions that use an excessive amount of CPU.
Operations that take a long time should be outside the main request processing cycle; this includes using background jobs and asynchronous processing to provide a quick response to the user while the operation continues in the background.
4. Reduce Unnecessary API Requests
Applications are required to make API calls to both internal and external services and systems. Unnecessary API calls may result in increased response time for the application and the services.
For example, the same information is provided by making multiple API calls that a single consolidated API call could replace.
Some strategies to reduce unnecessary overhead caused by API requests are:
- Combining requests
- Caching
- Loading data only when needed
- Avoid duplicate requests
- Use asynchronous requests
Indeed, reducing network calls independently optimizes networking applications.
5. Optimize Frontend Assets
Front-end resources and web applications that are slow to respond are not always the result of a back-end delay. Large JavaScript Bundles, large unoptimized images, deep CSS resource trees, and unneeded third-party resources can all increase the perceived slow response times of a web application when the back end is responsive.
Many optimizations can be employed to reduce the resources required to process front-end requests, including code splitting, lazy loading, image optimization, and asset minification.
Developers also need to ensure that the third-party resources that they are using are also optimized, including chat widgets and analytical resources, as they may further delay the application.
6. Use Caching Strategically
Many expensive processes within an application can be cached to prevent repeated access. Data that is frequently accessed can be permanently or temporarily stored to prevent the application system from executing the same logic and querying the database.
Caching can be implemented at multiple levels including:
- Browser caching
- Database caching
- Server caching
- Optimization of API responses
- Implementation of Content delivery networks (CDNs)
However, caching can be problematic if data contained within the cache is not consistently updated.
7. Monitor Server Resources
Applications may produce unpredictable demands on CPU, memory, storage, and network resources. Consistently operating at the limit may cause delays and errors experienced by users.
In order to decide whether to optimize the application or add additional resources, developers must know the current usage of resources.
Adding capacity to the server may be one way to address the problem, but developers should still identify and resolve issues related to inefficient code and database queries to avoid the same problem as traffic grows.
8. Test After Every Optimization
Any change in an application’s code should be measurable. An optimization should be tested after implementation, and the new results should be compared to the initial benchmarks.
This test determines if the optimization successfully improved the performance of the application or if the bottleneck has simply shifted.
Teams developing Web and Mobile Apps must test performance with varying conditions across different networks and devices. Keep in mind, users may access the application’s backend through websites, mobile applications, or other platforms.
When Professional Support Can Help
Performance issues can be very difficult to identify when they span multiple components of the system. A slow database query may result in a slow server response, which then results in a slow-loading page, so delays experienced by the end user may be very long.
A Web App Development Service can do a complete performance audit and identify and address problem areas based on the actual operational performance of the application.
When performance issues begin to adversely affect operational performance, professional optimization can design performance monitoring and scalable systems.
Conclusion
Designing an optimized system means more than just making an application “faster.” Determining which component is limiting the system's performance and addressing its underlying concern is the goal.
Optimizers should begin by monitoring and profiling, evaluating the efficiency of the backend compared to the frontend, inspecting caching systems, API requests, database queries, and processing. However, it is also important to test for optimization.
Whether building a new platform or improving a current product, treating performance as a continuous development process should be of main concern. An optimization approach should allow for orderly growth of an application’s performance on features, users, and data.
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