Developing a web app requires it to be able to handle a large number of users. It’s important to anticipate this and ensure the system is prepared. If the system isn’t flexible enough, it may struggle to handle heavy traffic.
To avoid this, scalability should be addressed early on in the development process.
This article provides a guide on creating web applications that can handle large data streams without issues.
Let’s explore the details of this process and delve into building a scalable web application.
Why Is Scalability Important?
What are scalable web applications and how they work
People today expect instant access to data and have no patience for slow-loading web pages or forms.
If your application can’t handle increased users and workload, it will be left behind. Scalability is crucial for web applications to perform well.
Unfortunately, many web and app development companies fail to understand and implement scalable design principles.
There are several key factors to consider for a web application to be scalable.
- Performance is important, and load balancing determines how well an application can handle increased users.
- Recoverability is also crucial, as downtime can cost companies millions in revenue.
- Easy management and updates are important for maintaining and scaling a web application efficiently.
Scalable development solves performance and code maintenance issues.
It’s not a new language or framework, but a methodology and set of principles that businesses should adopt.
When building a scalable web application, consider the user experience, page loading times, flexibility to make code changes, and the cost of updates.
Key Strategies for Scalable Web Application Development
1. Modular Application Design and Development
A modular application consists of units called modules. These modules can be linked together to form a larger application.
Each module has a specific function and can communicate with other modules easily.
This modular approach keeps code clean and organized, making coding, testing, deployment, and maintenance easier.
Modular development saves time and money by allowing complete modules to be reused for other applications.
This makes the management and scaling of modularized apps easier. On the other hand, monolithic programming makes reusing existing code difficult.
Modular code is easier to debug than monolithic code.
Problems are isolated to specific modules, making it easier to identify and fix issues.
Adding new modules to an existing application doesn’t affect other modules.
Modules operate independently, making it easier to identify code breaks.
If a function isn’t working, it’s likely an issue with the responsible module. Code length increases the risks associated with vulnerabilities and unpredictability.
2. Building Scalable Web Applications Using Microservice Architecture
Monolithic architecture becomes difficult to scale, upgrade, or maintain over time.
The growing complexity hinders modifications to the architecture. To address this challenge, enterprises can turn to the microservices architecture.
Implementing microservices architecture is an excellent approach to building scalable web applications.
It involves breaking down large applications into smaller, specialized components.
This segmentation allows for better scalability, especially when downsizing is desired.
In contrast to monolithic architecture, microservices architecture distributes complex tasks into independent, API-driven processes.
The decentralization results in loosely coupled and reusable components.
This approach empowers organizations to upgrade, maintain, test, and develop individual web services without impacting the entire application.
As a result, businesses can achieve greater agility.
The benefits of using microservices architecture for scalable web apps are numerous. It enables real-time scaling with minimal disruption for the end-user.
Time to market is accelerated, and various scalability issues are overcome.
Launch processes become faster, and security is enhanced. Additionally, shorter throughput and load times are achieved, app resilience is boosted, and downtime is reduced.
3. If You Want To Scale Your Application, Learn How to Async
When a user creates a profile on your website, you need to send a confirmation email. But the server has to wait for the SMTP server to respond, send the message, and wait for acknowledgement, causing unnecessary idle time.
To speed up the process, we use asynchronous functions to allow the server to execute other tasks while waiting.
Asynchronous programming is useful in scenarios where tasks are blocking the execution of further code.
Some languages have built-in asynchronous capabilities, like JavaScript with AJAX calls, but in others like Python, you need to code async operations deliberately.
There are multiple scenarios where async operations are valuable, such as when importing CSV records to create profiles or making HTTP requests to multiple sources.
Each language has its own way of implementing async programming, such as using async/await in JavaScript or threads in Java.
Python offers options like the Celery project for function delegation or grequests for making multiple requests asynchronously.
It’s important to note that async programming is not suitable for CPU-intensive tasks, as it focuses on leveraging idle time during I/O operations.
It’s essential to get comfortable with non-linear code execution to create scalable applications.
4. Cache Inconsistency and Invalidation Challenges in Scaling Web Apps
Caching can boost performance and reduce server load. But cache consistency and invalidation are challenging in distributed environments.
Mishandled caches can serve stale or outdated data, impacting scalability and user experience.
Reduced application server and database load
Using a cache to store frequently accessed data in memory allows for quick retrieval and avoids querying the database. This reduces the database server’s workload and the number of queries needed.
Consequently, the database can handle more queries effortlessly. Since most data is retrieved from the cache, which is faster, application servers can handle more requests per second.
By optimizing database resource usage, caching improves the system’s scalability, ensuring smooth operation under high user concurrency and large data volumes.
Adding a cache increases the system’s capacity for serving users, even with the same database and server configurations.
Mitigated load spikes
Caching helps during high read traffic by serving data from memory. This is valuable when the database can’t keep up.
Caching prevents bottlenecks and ensures a smoother user experience during peak usage.
5. Horizontal scaling
Horizontal scaling adds server instances to distribute workload across multiple machines. This increases capacity to handle more users and reduces impact of single points of failure. Load balancers distribute requests evenly among server instances.
Sharding
The sharding method involves dividing a database into smaller pieces and distributing them across multiple machines. Each machine stores and handles requests for a specific set of shards. This improves fault tolerance and availability.
However, it adds complexity as data must be evenly distributed and properly managed to avoid duplication or loss. In the example below, shards were divided by price range.
This method improves system reliability by preventing a single point of failure from affecting other machines.
Replication
The horizontal scaling method replicates the same database on multiple machines.
One machine is designated as the primary, and changes made to it are propagated to all other replicas.
This ensures all instances of the database are up-to-date. Advantages include improved availability, fault tolerance, and performance.
If the primary machine fails, another can be promoted. However, replication adds complexity and risks duplication or loss of data.
It also increases system traffic and storage requirements, resulting in additional costs.
Clustering
Clustering is a method of horizontal scaling that combines multiple servers to act as one system. This provides greater processing power than independent machines.
A load balancer is used to distribute traffic among the machines in the cluster.
Advantages of clustering include increased availability, fault tolerance, and efficiency through load balancing. The load balancer ensures no machine becomes overloaded. Disadvantages include added complexity and cost, similar to other scaling methods like sharding and replication.
Frameworks for Scalable Web Application Development
1. Node.js
Node.js is a powerful environment and library.
It allows you to run web applications outside of the browser. With Node.js, you can create real-time, event-driven services.
The V8 JavaScript engine from Google Chrome adds to its power. Node.js operates on a single-threaded event loop, handling numerous connections without overwhelming resources.
Its non-blocking I/O model enables simultaneous handling of multiple requests, making it highly scalable. Node.js offers a unified development experience.
You can use full-stack JavaScript development, simplifying code sharing and reducing the learning curve. There is also an extensive package ecosystem through npm, providing ready-to-use solutions.
Node.js empowers you to build real-time applications with interactive and responsive features.
It is also perfect for microservices architecture, dividing complex applications into smaller, independent services.
Each service can be developed and deployed separately, enhancing scalability and maintainability.
To build scalable web applications, choosing the right framework is crucial. Optimizing performance is also important, utilizing caching mechanisms, optimizing database queries, and minimizing blocking operations.
Asynchronous programming techniques like callbacks and promises ensure efficient handling of I/O operations.
Scaling can be achieved using the cluster module and load balancing can efficiently distribute incoming requests across multiple instances.
2. Django
There are two ways to scale an application: horizontally or vertically. Vertical scaling means upgrading the machine it runs on by adding more resources. This approach works well initially but has limitations.
You can only add so many resources to one machine, it’s difficult to create auto-scaling infrastructure, and it’s more expensive and less efficient.
Vertical scaling is easier at the beginning, but eventually, you will need to scale horizontally.
Horizontal scaling involves spawning more machines to handle the application’s load. Instead of one machine handling 1000 requests, you can have 10 machines handling 100 requests each.
The benefit is that you can easily add more machines as needed.
There is no upper limit to how much you can scale, and it can handle thousands to millions of visitors.
To scale horizontally, the application needs to be stateless, meaning it does not store data, images, sessions, or files.
It uses third-party services like databases, memory cache, and cloud storage.
Django provides features to run a stateless application, such as using a different database, custom file storage backends, storing sessions in a database, and using cache backends.
These features allow the application state to be stored elsewhere. The specific technology or storage location doesn’t matter as long as it’s separate from the application.
3. Ruby on Rails Applications
There are some tips for scaling Ruby on Rails applications.
One tip is to make good use of caching.
This includes caching at the client, using Ajax libraries to stream data, and using gateway/reverse proxy caches.
It’s also important to learn how to use expiration and etags.
Another tip is to segregate data and data serving. Instead of using a single database, it’s better to separate datasets that are independent.
Serving static assets from a separate tier or using a CDN can simplify scaling. It’s also worth considering whether a relational data model is necessary for all data stores. Using a key-value data store or Hadoop for simpler data can be more efficient.
Additionally, it’s important to minimize and handle external dependencies. If a service is not responding or can’t handle the request load, having a fallback strategy is crucial.
Tending to the database and job handlers is also important. Slow queries and resource consumption should be monitored regularly. Scrubbing the database for unused indices and segregating jobs in a separate tier can help with scalability.
Lastly, shard unavoidably relational data. This involves breaking up datasets into independent pieces based on a key, such as user IDs or access frequency. These tips can help with scaling Ruby on Rails applications effectively.