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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.

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.