Disturbed Cache

In application architecture, to implemented a sophisticated distributed cache system that leverages the power of hash key partitioning to ensure efficient and balanced data distribution across multiple servers. This innovative approach optimizes data retrieval and storage, delivering enhanced performance and responsiveness for our users.

fig: Disturbed Cache implementation

At the core of this system lies the Hash Map and Linked List combination, strategically placed on each server. This combination forms the foundation for handling cache requests efficiently. With the Hash Map, we achieve rapid key-value lookups, allowing us to pinpoint data quickly. Complementing this, the Linked List structure ensures seamless management of keys’ access history, facilitating the application of the Least Recently Used (LRU) technique.

To ensure a well-balanced distribution of keys, we have harnessed the capabilities of hash key partitioning. This mechanism divides our data keys into partitions based on their hash values, distributing them across three servers. This approach guarantees an even workload distribution, preventing any individual server from becoming a bottleneck. Moreover, it fosters a high degree of fault tolerance, as the loss of one server does not compromise the overall cache system’s availability.

To manage the hash key range partitioning and ensure smooth data routing, we have integrated ZooKeeper into our architecture. ZooKeeper dynamically manages the hash key ranges assigned to each server, orchestrating the forwarding of data requests to their respective destinations. This ensures that every data access request finds its way to the appropriate server, streamlining the overall cache retrieval process.

This distributed cache system is poised to deliver exceptional performance gains, thanks to its combination of hash key partitioning, data routing facilitated by Zoo Keeper, and efficient cache management using LRU techniques. It exemplifies our commitment to utilizing cutting-edge technologies to enhance system performance, scalability, and reliability. As we present this innovative solution to a larger forum, we are excited to showcase how these components harmoniously work together to elevate our application’s cache capabilities to new heights.

hash partitioning. This scheme works with any key, without requiring a key in the form object_name:<id>, and is as simple as:

· Take the key name and use a hash function (e.g., the crc32 hash function) to turn it into a number. For example, if the key is foobar, crc32(foobar) will output something like 2323233.

· Use a modulo operation with this number in order to turn it into a number between 0 and 3, so that this number can be mapped to one of my four Redis instances. 2323233 modulo 4 equals 1