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Design Patterns and Principles

Event Sourcing Pattern: An Overview

Event Sourcing is a design pattern that enables developers to maintain the state of an application by storing all the changes (events) that have affected its state over time. This pattern treats changes as a series of events that can be queried, interpreted, and replayed to recreate past states or predict future states.

In traditional systems, we usually save the latest state of the data. However, in the event sourcing pattern, instead of storing the current state, all changes to the application state are stored as a sequence of events. This creates a comprehensive log that can be used to recreate the state of the system at any given point in time.

Caching Patterns

Write-Behind Cache Pattern: Benefits and Drawbacks

In the Write-Behind Cache Pattern, instead of writing data directly to the data store, the application writes to a cache. The cache then asynchronously writes the data to the data store. This approach allows the application to continue processing other tasks without waiting for the data store write operation to complete.

Caching Patterns

Write-Through Cache Pattern with Kotlin and Redis

The write-through cache pattern is a caching strategy that ensures data consistency and reliability. In this pattern, every write operation to the cache is immediately written to the database as well. This means that the cache always contains the most recent version of the data, ensuring that read operations are always accurate.

Caching Patterns

Read-Through Caching Pattern: An Example with Caffeine

The read-through cache pattern is a caching strategy that enhances performance by maintaining a cache between the application and the data store. Unlike the cache-aside pattern, where the application is responsible for reading from and writing to the cache, in the read-through pattern, the cache itself manages the interaction with the data store.

Caching Patterns

Cache-Aside Caching Pattern: An Example with Caffeine

The cache-aside pattern is a widely used and most common caching technique that enhances performance by maintaining a cache of data that’s expensive to fetch or compute. It’s typically used in systems or use cases where read operations are more frequent than write operations.

Exploring Distributed Locks Across Various Platforms

Distributed locks are a critical concept in the world of distributed systems, necessary for maintaining data consistency, coordination, and synchronization across various nodes of a system. They serve as a concurrency control mechanism to restrict multiple processes from accessing or modifying shared resources simultaneously. In this post, we take a look at a few ways to implement a distributed locks using redis, zookeeper and etcd.

Bulkhead Pattern vs. Circuit Breaker Pattern: Key Differences in Resilient Design Patterns with Python Code Examples and Explanations

In modern software development, ensuring the reliability and resilience of your application is of utmost importance. This is where design patterns like the Bulkhead Pattern and Circuit Breaker Pattern come into play. They are widely used to build fault-tolerant and resilient systems that can withstand failures and provide a high-quality user experience. In this blog post, we’ll explore both patterns in-depth, highlighting their key differences, and provide Python code examples with explanations to illustrate their implementation.

Enhancing Resilience in Distributed Systems with the Circuit Breaker design pattern

In modern distributed systems, components and services often depend on each other to function correctly. The Circuit Breaker pattern is a design pattern that helps protect systems from cascading failures by monitoring the health of a dependent system and, if it detects that the system is failing, it will stop sending requests to that system.