This week’s issue brings you:

• How Caching Powers Our Digital World

• What is Kafka, and How Does it Work (Recap)

• SSL vs TLS (Recap)

• Distinguishing Types of Binary Trees (Recap)

READ TIME: 6 MINUTES

How Caching Powers Our Digital World

A major focus in the computing field is to provide rapid access and processing of data. Caching stands out as a key approach to this initiative, providing a method for temporarily storing data for swift access. It is a significant contributor to improving speed and efficiency, ranging from enhancing basic computer operations to streamlining web content delivery.

With caching playing a multi-layered role in technology, there's a lot to dive into let's start with hardware-level caching - a crucial element that significantly influences computing speed and performance.

Inside Hardware-Level Caching

At the hardware level, caching's impact on enhancing computing speed and performance is significant. CPU caches, with their L1, L2, and L3 layers, are central to this enhancement. The L1 cache is the closest to the CPU cores, the smallest of all other layers but offers the fastest data access. The larger L2 and L3 caches, though a bit slower, play a vital role in bridging the speed disparity between the fast CPU and the slower RAM.

Disk caching is another important component. Reading and writing data to the hard disk is a much slower process than the cache. By moving frequently used data into a cache, disk operations become faster leading to a boost in the overall performance of the system.

These hardware-level caches provide a solid foundation for providing a smooth and speedy computing experience, laying the groundwork for further enhancement to computing efficiency.

Streamlining System Performance

Another critical caching layer can be found within the system level. Operating systems utilize two types of caching: page caches and buffer caches. The page cache stores memory pages read from storage, significantly speeding up subsequent data accesses and reducing I/O operations.

Buffer caches temporarily store data being transferred to or from disk buffers. This helps balance the data transfer speeds between different components of the computer’s subsystems, boosting overall system performance.

These caching methods play a big role in marrying CPU speed with slower disk storage, which contributes to the operating system's responsiveness and efficiency.

Boosting Web and App Efficiency

At the network and distributed level, caching significantly influences internet and web application efficiency. DNS caching speeds up internet surfing by holding onto the addresses of frequently accessed websites, which minimizes the frequency of domain name queries.

CDN (Content Delivery Network) caching, essential in internet content delivery, stores web content at various locations worldwide, reducing latency and enhancing content delivery speed.

For web applications where high performance is needed, distributed caching systems such as Memcached and Redis are essential. They efficiently handle large volumes of data across several servers, which is a necessity during high-traffic and real-time data processing scenarios.

Transforming Efficiency and User Experience

Caching at the application level, especially in databases, is a key factor in keeping modern-day applications and services speedy. Caching query results and prepared statements significantly lightens the database load, enabling quicker transaction processing. This is especially important in high-transaction, rapid-access environments.

Web browsers also utilize caching to store resources like images and scripts after your first visit to a webpage which helps cut down load times during subsequent visits.

Caching also has a positive impact on the functionality of mobile and IoT devices. Edge caching is used to store data closer to the user, leading to a drop in latency and more efficient data transmission, even when the internet connection is weak or limited. This level of efficiency is vital in the modern world where mobile phone usage is ever-increasing.

Wrapping up

Despite its crucial impact on the efficiency and speed at every level of computing, caching is often an unsung hero and an often overlooked solution.

Beyond our current digital world, caching will continue to play a key role as technology evolves; ensuring that as our digital world grows, it remains as fast and seamless as ever.

What is Kafka (Recap)

• Kafka is an open-source distributed streaming platform designed for building real-time data pipelines and streaming applications.

• Kafka operates as a distributed pub-sub messaging system. Allowing applications to publish and subscribe to real-time or near-real-time data feeds.

• The high throughput, scalability, fault-tolerance, durability, and ecosystem Kafka provides have made it a very popular choice for use cases where real-time data feeds are required.

• The key components of Kafka include Producer, Consumer, Broker, Topic, and Partition.

• Kafka has many use cases, from aggregating data from different sources to monitoring and real-time analytics.

Check out the full post for an extended explanation of Kafka.

SSL vs TLS (Recap)

• SSL (Secure Sockets Layer) and TLS (Transport Layer Security) are cryptographic protocols designed to provide secure communication over a network.

• TLS is the successor to SSL and has addressed the vulnerabilities present in SSL.

• The process of how data is sent using these protocols involves roughly 8 steps. The most significant differences between these two protocols occur during the key exchange (step 4) and encrypted data transfer (step 7).

• SSL predominantly uses RSA for key exchange, whereas TLS uses other mechanisms like DH (Diffie-Hellman) and ECDH (Elliptic Curve Diffie-Hellman).

• SSL uses older encryption algorithms, some of which are vulnerable. TLS uses updated & more robust encryption algorithms that have overcome the vulnerabilities in SSL.

See here for a detailed breakdown of the differences between SSL and TLS.

Distinguishing Types of Binary Trees (Recap)

A binary tree is a tree data structure where 𝗲𝗮𝗰𝗵 𝗻𝗼𝗱𝗲 𝗵𝗮𝘀 𝗻𝗼 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘁𝘄𝗼 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻.

A binary tree can come in many forms, such as the following:

• Full binary tree: every node has either zero or two children.

• Complete binary tree: each level of the tree has a complete set of nodes, with the last level being the exception.

• Perfect binary tree: Every level of the tree including the last level is complete.

• Balanced binary tree: the depth of the left and right sub-trees of all nodes differ by no more than 1.

• Binary search tree: each node is larger than all the nodes in their left sub-tree, and smaller than all the nodes in their right sub-tree.

Check out the full post for more details about binary trees including typical use cases and implementation.

That wraps up this week’s issue of Level Up Coding’s newsletter!

Join us again next week where we’ll explore MVC vs MVP architecture, strategies to prevent system misuse and resource overload, URL components, and Linux Permissions.