What are Distributed CACHES and how do they manage DATA CONSISTENCY?

Summary

This video script delves into the concept of caching in system design, explaining its purpose and benefits. It highlights two main use cases: reducing database load and saving network calls for frequently accessed data. The script also covers cache policies like LRU, the challenges of cache eviction and data consistency, and the strategic placement of caches. It concludes by discussing the importance of caching in system design and invites viewers to engage with the content.

Takeaways

• 😀 Caching is a technique used in system design to store data temporarily to improve response times and reduce database load.
• 🔍 The primary use cases for caching are to save on network calls and to avoid redundant computations, both aimed at speeding up client responses.
• 💡 A cache works by storing key-value pairs, where the key represents a unique identifier for the data, and the value is the data itself.
• 🚀 Caching is beneficial when dealing with commonly accessed data, such as user profiles, to reduce database queries and network traffic.
• 🔑 The decision to load data into the cache and when to evict data from it is governed by cache policies, which are crucial for cache performance.
• 📚 Least Recently Used (LRU) is a popular cache policy that evicts the least recently accessed items first when the cache is full.
• 🔄 Cache size is a critical factor; too large a cache can lead to increased search times, making it less effective and potentially counterproductive.
• 💡 Predicting future requests is essential for effective caching, as it helps in determining which data should be stored in the cache for quick access.
• 🔒 Consistency between the cache and the database is vital, especially when updates are made to ensure that clients receive the most current data.
• 📍 The placement of the cache can vary, with options including local in-memory caches on servers or a global cache that multiple servers can access.
• 🔄 Write-through and write-back are caching mechanisms for handling updates to cached data, with each having its advantages and disadvantages in terms of performance and consistency.
• 🌐 Redis is an example of a distributed cache that can be used as an in-memory data structure store, suitable for scenarios requiring fast data access and manipulation.

Q & A

What is a cache and why is it used in system design?

A cache is a storage layer that temporarily holds data for faster access. It is used in system design to improve performance by reducing the need to fetch data from a slower data source, such as a database, repeatedly.

What are the two main scenarios where caching is beneficial?

Caching is beneficial in two main scenarios: 1) When querying for commonly used data to save network calls, and 2) When avoiding computations, such as calculating the average age of all users, to reduce load on the database.

How does a cache help in reducing network calls?

A cache helps in reducing network calls by storing frequently requested data, such as user profiles. When a user requests their profile, the system can retrieve it from the cache instead of making a new request to the database.

What is the purpose of caching when it comes to avoiding computations?

Caching is used to avoid computations by storing the results of expensive operations, like calculating averages, in the cache. This way, when the same computation is requested again, the result can be served directly from the cache without re-computing it.

Why is it not advisable to store everything in the cache?

Storing everything in the cache is not advisable because cache hardware, typically SSDs, is more expensive than regular database storage. Additionally, a large cache can lead to increased search times, which can negate the performance benefits of caching.

What are the two key decisions involved in cache management?

The two key decisions in cache management are when to load data into the cache (cache entry) and when to evict data from the cache (cache exit). These decisions are governed by a cache policy.

What is a cache policy and why is it important?

A cache policy is a set of rules that determine how and when to load and evict data in the cache. It is important because the performance of the cache, and thus the system, largely depends on the effectiveness of the cache policy.

What is the Least Recently Used (LRU) cache policy and how does it work?

The Least Recently Used (LRU) cache policy is a popular cache eviction strategy where the least recently accessed items are removed first. It works by keeping recently accessed items at the top of the cache and evicting items from the bottom when the cache reaches capacity.

What is thrashing in the context of caching?

Thrashing in caching refers to a situation where the cache is constantly being updated with new data without ever serving any requests. This can occur if the cache is too small and cannot hold the data needed between requests, leading to inefficient use of resources.

Why is data consistency important when using a cache?

Data consistency is important to ensure that the data served from the cache is up-to-date and accurate. Inconsistencies can occur if updates to the database are not reflected in the cache, leading to outdated or incorrect information being served to users.

What are the two main strategies for handling cache updates and why are they used?

The two main strategies for handling cache updates are write-through and write-back. Write-through ensures data consistency by updating the cache and the database simultaneously, while write-back improves performance by updating the cache immediately and the database later or in bulk, but it can lead to data inconsistency if not managed properly.

What is Redis and how is it used in caching?

Redis is an in-memory data structure store that can be used as a database, cache, and message broker. It is used in caching to provide fast data retrieval and storage, and it supports various data structures, making it suitable for a wide range of caching use cases.

What are the advantages of placing a cache close to the servers?

Placing a cache close to the servers can reduce network latency and improve response times, as data can be served directly from the cache without the need to access the database. It also simplifies the implementation of the caching layer.

What is the concept of a global cache and what are its benefits?

A global cache is a centralized cache that is accessible to all servers in a distributed system. The benefits of a global cache include consistent data access across all servers, reduced load on the database due to shared cache usage, and the ability to scale the cache independently from the application servers.