Caching In System Design | SDE Interview
Caching is the process of storing frequently or recently accessed data in a faster storage medium, such as RAM, so that it can be quickly retrieved without having to fetch it from a slower storage device, such as a hard drive or a network server. This improves system performance by reducing the time it takes to access the data.
Caching works by maintaining a cache, which is a collection of data that has been stored in a faster storage medium, along with a record of where the data came from. When a program or process needs to access a piece of data, it first checks the cache to see if the data is already stored there. If it is, the program or process can access the data directly from the cache, which is much faster than fetching it from a slower storage device.
Types of Caching
Several types of caching can be used in system design, each with its unique characteristics and use cases. Some common types of caching include:
- Memory caching: This type of caching stores frequently or recently accessed data in RAM much faster than other storage devices. Memory caching is typically used to improve the performance of systems that can quickly access large amounts of data, such as databases or web servers.
- Disk caching: Disk caching stores frequently or recently accessed data on a hard drive, which is slower than RAM but faster than other types of storage devices. Disk caching can improve the performance of systems with limited RAM or that need to store large amounts of data that cannot fit in memory.
- Browser caching: Browser caching stores frequently or recently accessed web pages and other data on the user’s computer so that the data does not need to be downloaded from the server each time the user visits the site. This improves the performance of web-based systems by reducing the amount of data that needs to be transferred over the network.
- CDN caching: CDN caching stores frequently or recently accessed web pages and other data on servers located around the world so that users can access the data from the nearest server, which improves the performance of web-based systems by reducing the amount of data that needs to be transferred over the network.
- Object caching: Object caching stores the results of complex operations, such as database queries or API calls, in memory so that the same results can be reused without having to perform the operation again.
- Application caching: Application caching stores the results of complex operations, such as database queries or API calls, on disk so that the same results can be reused without having to perform the operation again.
Each type of caching has its own advantages and disadvantages, and the best type of caching to use will depend on the specific needs of the system or application.
Strategies for Caching
Several strategies can be used for caching, each with unique characteristics and use cases. Some common strategies include:
- Time-based expiration: This strategy involves setting a time limit for how long data should be stored in the cache. Once the time limit has been reached, the data is automatically removed from the cache and will need to be fetched again from the original source. This strategy is helpful for data that become stale over time, such as stock prices or weather forecasts.
- Least recently used (LRU): This strategy involves removing the data that has been accessed least recently from the cache when the cache becomes full. This strategy is helpful for systems that need to store a large amount of data but have limited cache space, ensuring that the most frequently accessed data will remain in the cache.
- Most frequently used (MFU): This strategy removes the data that has been accessed least frequently from the cache when the cache becomes full. This strategy is similar to LRU, but it ensures that the data that is accessed most frequently will remain in the cache.
- Least frequently used (LFU): This strategy removes the data that has been accessed least frequently from the cache when the cache becomes full. This strategy is helpful for systems that handle more read operations than write operations.
- Random replacement (RR): This strategy removes random data from the cache when the cache becomes full. It is used when the system lacks information about the data usage pattern.
It’s worth noting that each strategy has its own advantages and disadvantages, and the best strategy will depend on the system's specific needs or application. In addition, a combination of these strategies can also be used in some cases.
How Hash Tables are Used for Caching
Hash tables are a common data structure used in caching because they provide an efficient way to look up cached data. A hash table is a collection of key-value pairs, where each key is a unique identifier for a piece of data, and each value is the data itself.
In caching, the keys in the hash table are typically a representation of the data that is being cached, such as the URL of a web page or the primary key of a database record. The values in the hash table are the data being cached, such as the web page's contents or the database record.
When a program or process needs to access a piece of data stored in the cache, it first calculates a hash of the key, a unique identifier for the data. The hash function is designed to always return the same value for the same input. The program or process then uses the hash value to quickly look up the corresponding value in the hash table, which is the cached data.
Distributed Caching & Its Importance
Distributed caching is a technique used in distributed systems to store and retrieve data quickly and efficiently. It involves using a distributed cache, a network of cache nodes that work together to store and manage data.
Caching is essential in distributed systems because it can significantly improve the performance and scalability of the system. By storing frequently accessed data in the cache, the system can avoid the need to retrieve the same data from a slower storage system, such as a database. This can lead to faster response times, improved throughput, and reduced load on the storage system.
Additionally, distributed caching can also improve the fault tolerance of a system by allowing the cache nodes to take over in the event of a failure of a primary node.
The role of caching in modern infrastructure and microservices architecture
Caching plays an essential role in modern infrastructure and microservices architecture.
- Modern Infrastructure: In modern infrastructure, caching can help improve distributed systems' performance and scalability. Caching can store frequently accessed data in memory, reducing the load on slower storage systems and improving response times. Additionally, caching can also be used to improve the fault tolerance of a system by allowing for the cache nodes to take over in the event of a failure of a primary node.
- Microservices Architecture: In a microservices architecture, caching can help to improve the performance and scalability of individual services by reducing the load on the underlying storage systems. Additionally, caching can also be used to improve the fault tolerance of a system by allowing individual services to continue functioning even if a dependent service is down.
- Inter-Service Communication: In a microservices architecture, services often communicate with each other. Caching can improve the performance of these inter-service communications by reducing the number of requests sent between services and reducing the load on the underlying storage systems.
- Decoupling: Caching also allows for the decoupling of services. As services can cache the results of their calls to other services, they do not need to depend on their availability and responsiveness. This improves the system's fault tolerance and allows for more flexibility in scaling and deploying individual services.
Conclusion
In conclusion, caching is a powerful technique that plays a crucial role in improving the performance and scalability of modern systems. It can be used in various applications and technologies, such as web browsers, web applications, and databases, to store frequently accessed data and improve response times. Additionally, caching can also be used to improve the fault tolerance of a system by allowing for the cache nodes to take over in the event of a failure of a primary node.
In modern infrastructure and microservices architecture, caching can help reduce the load on underlying storage systems, improve the performance of inter-service communication and increase the system's fault tolerance. Furthermore, caching allows for the decoupling of services, which improves flexibility in scaling and deploying individual services.
Overall, caching is a valuable technique that should be considered when designing systems that handle large amounts of data and high traffic. It allows for faster data retrieval, improves performance and scalability, and improves fault tolerance.
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