LUC #13: Essential Caching Strategies for Optimal Performance
Plus, how quantum computing works, components of Docker explained, and lists vs tuples in Python
Hello and welcome back!
We're excited to share another issue of Level Up Coding’s newsletter with you.
In today’s issue:
Read time: 7 minutes
Caching Strategies: A Comparative Overview
In the current world of big data and high-speed applications, performance is a prominent consideration for any development team. Caching is one of the most used techniques to boost performance due to its simplicity and wide range of use cases.
With caching, data is copied and stored in locations that are quick to access such as on the browser or a CDN.
How data is updated and cleared is a key component of the design of any caching strategy. There are many techniques to choose from, all with their own unique set of use cases that they aim to accommodate.
Least Recently Used (LRU) is an approach to cache management that frees up space for new data by removing data that has not been accessed or utilized for the longest period of time. It assumes that recently accessed data will be needed again soon. This is quite a common approach and is often used in browsers, CDNs, and operating systems.
Most Recently Used (MRU) is the opposite of LRU, where the most recently used data is removed first. This approach is more commonly used in streaming or batch-processing platforms where data is unlikely needed again once it has been used.
Least Frequently Used (LFU) removes data that is used the least. Although it is a more accurate approach than LRU, it requires a mechanism to keep count of how often data is accessed which adds complexity. LFU also has the risk of keeping outdated data in the cache. For these reasons, it is often used in combination with other strategies such as LRU.
With Time-To-Live (TTL), data is kept in the cache for a pre-defined period of time. This is ideal for cases where the current state of data is only valid for a certain period of time, such as session data.
Two-tiered caching provides a more complex approach that strikes a balance between speed and cost. In this design, data is split up between a first and second tier. This first tier is a smaller, faster, and often more expensive caching tier that stores frequently used data. The second tier is a larger, slower, and less expensive tier that stores data that is used less frequently.
The five strategies mentioned above are the most popular approaches to caching. There are other notable mentions, such as the following:
First In, First Out (FIFO): The oldest data is deleted first.
Random Replacement (RR): Randomly selects data to be deleted.
Adaptive Replacement Cache (ARC): Uses a self-tuning algorithm that tracks recency and frequency to determine which data to delete first.
The best caching strategy depends on the system’s specific requirements and constraints. Understanding and appropriately leveraging the different caching strategies available can make a significant difference in the performance of your application.
Components of Docker (recap)
Software inconsistencies across different environments lead to significant issues including deployment failures, increased development and testing complexity, and more. Docker solves the "it worked on my machine" problem, and streamlines application deployment by encapsulating applications and their dependencies into standardized, scalable, and isolated containers (containerization).
Below are the core components powering Docker:
Image: A read-only template for creating containers. Contains application code, libraries, and dependencies.
Container: An instance of an image. It is a lightweight and standalone executable package that includes everything needed to run an application.
Dockerfile: A script-like file that defines the steps to create a Docker image.
Docker engine: Responsible for running and managing containers. Consists of the daemon, a REST API, and a CLI.
Docker daemon: A background service responsible for managing Docker objects.
Docker registry: Repositories where Docker images are stored and can be distributed from; can be private or public.
Docker network: Provides the communication gateway between containers running on the same or different hosts; allowing them to communicate with each other and the outside world.
Volumes: Allow data to persist outside of containers and to be shared between container instances.
How Does Quantum Computing Work? (recap)
Quantum computers can perform multiple calculations simultaneously, which gives them much more processing power than classical computers. Two of the primary principles responsible for the ability to process multiple possibilities concurrently are superposition and entanglement.
Unlike classical computing, which operates on a binary system of 1s and 0s, a quantum bit (qubit) can exist in multiple states at the same time; this is called ‘superposition’.
Entanglement suggests that two qubits can be intrinsically linked, meaning the state of one qubit is directly related to the state of another.
Superposition and entanglement allow quantum computers to process information in a very different way from classical computers. Qubits can handle information that is far denser than the classical binary approach. Entanglement helps make computation shortcuts leading to algorithms that are far more efficient and powerful.
Lists vs Tuples in Python (recap)
Both lists and tuples are data structures in Python that are used to hold sequences of items. A list is a mutable, dynamic array where elements can be added, deleted, and modified even after the list has been created.
List key features:
🔹 Initialised w/ square brackets
🔹 Generally slower and uses more memory than tuples
🔹 Built-in manipulation methods are available
Tuple key features:
🔸 Initialised with parentheses
🔸 Generally faster and uses less memory than lists
🔸 Only query methods are available
A tuple is a collection of elements that cannot be modified after they are created. While both lists and tuples are useful for storing sequences of items, their use cases and properties differ due to their mutability.
That wraps up this week’s issue of Level Up Coding’s newsletter!
Join us again next week where we’ll explore OAuth 2.0, HTTP vs HTTPS, and fault tolerance.