In today's fast-paced digital world, user experience plays a crucial role in the success of any web application. Users expect lightning-fast loading times and seamless interactions, and any lag or delay can lead to frustration and abandonment. Therefore, optimizing the performance of your web application is paramount. In this blog post, we'll explore ten proven strategies to enhance the performance of your web application, ensuring a smoother and more enjoyable user experience.
REST which stands for Representational State Transfer is an architectural style that governs how APIs are designed and built. REST’s popularity and ease of implementation make it the most preferred API architectural style for modern-day software development as compared to other protocols such as SOAP (simple object access protocol). REST APIs or RESTful web services have become the backbone for efficient communication between client and server in modern-day software development. However, to build efficient and robust REST APIs, it is crucial to follow some standard best practices. In this blog, we’ll explore REST API best practices that can help you build scalable, maintainable, secure, and robust APIs.
While consistency is vital, it’s essential to understand that achieving strong consistency in distributed systems can come at the expense of increased latency and reduced availability. Strong consistency may require additional coordination mechanisms that slow down operations. Therefore, choosing the appropriate consistency model involves striking a balance between data correctness and system performance, based on the specific requirements of the application and use case. Different systems may opt for eventual consistency or other weaker consistency models if absolute real-time consistency is not necessary for their functionality.
Rate limiting runs within an application, rather than running on the web server itself. Typically, rate limiting is based on tracking the IP addresses that requests are coming from, and tracking how much time elapses between each request. The IP address is the main way an application identifies who or what is making the request. A rate limiting solution measures the amount of time between each request from each IP address, and also measures the number of requests within a specified timeframe. If there are too many requests from a single IP within the given timeframe, the rate limiting solution will not fulfill the IP address's requests for a certain amount of time.
Consistent hashing is a distributed hashing technique used in computer science and distributed systems to achieve load balancing and minimize the need for rehashing when the number of nodes in a system changes. It is particularly useful in distributed hash tables (DHTs), distributed caching systems, and other distributed storage systems. Consistent hashing is a technique used in computer systems to distribute keys (e.g., cache keys) uniformly across a cluster of nodes (e.g., cache servers). The goal is to minimize the number of keys that need to be moved when nodes are added or removed from the cluster, thus reducing the impact of these changes on the overall system.
ACID transactions refer to a set of properties that are designed to ensure the reliability and consistency of database transactions. The term “ACID” stands for Atomicity, Consistency, Isolation, and Durability, which are the four key properties of an ACID transaction. Essentially, ACID transactions guarantee that database operations are executed correctly, and if there is any kind of failure, the database can recover to a previous state without losing any data or impacting the consistency of the data. In other words, ACID transactions provide a high level of assurance that database transactions will be processed reliably and that data will be stored accurately and consistently.
In computer science, the CAP theorem, sometimes called CAP theorem model or Brewer’s theorem after its originator, Eric Brewer, states that any distributed system or data store can simultaneously provide only two of three guarantees: consistency, availability, and partition tolerance (CAP). During times of normal operations, a data store covers all three. However, according to the CAP theorem, a distributed database system can provide either consistency or availability when it experiences a network failure. In other words, in case of a network failure, it’s a tradeoff between consistency or availability, and that choice must be made in advance.
Latency and throughput are two metrics that measure the performance of a computer network. Latency is the delay in network communication. It shows the time that data takes to transfer across the network. Networks with a longer delay or lag have high latency, while those with fast response times have lower latency. In contrast, throughput refers to the average volume of data that can actually pass through the network over a specific time. It indicates the number of data packets that arrive at their destinations successfully and the data packet loss.
Scalability is a crucial aspect of system design, especially in today’s world of rapidly growing data and user bases. As applications and services become more popular, they must be able to handle increased traffic and data without compromising performance or reliability. In this article, we will explore what scalability is, why it is important, and how to achieve it in system design. The truth is, many of us don’t dive deep enough into scalability to truly grasp its significance in system design. Consequently, we fail to impress interviewers who are looking for candidates with a comprehensive understanding of this crucial aspect.