Introduction

In today's fast-paced world, web applications need to be highly performant and scalable to meet the increasing demands of users. One way to improve web applications' performance and scalability is by using a distributed cache.

Distributed cache is a mechanism that stores frequently accessed data in memory to reduce the load on the database and improve the speed of data access.

Distributed cache is an essential component of modern web applications, where multiple users often access data across different regions. By storing frequently accessed data in memory, distributed cache reduces the number of requests made to the database, thereby improving the application's performance and scalability.

This article will explore the fundamentals of distributed cache, its common use cases, and its benefits to web applications.

We'll also discuss the different types of cache solutions available and the best practices for using distributed cache in distributed systems.

This blog post will thoroughly understand distributed cache and why HarperDB is an excellent edge caching solution.

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What Exactly is Distributed Cache?

A distributed cache is a cache with data spread across multiple nodes in a cluster and multiple clusters across multiple data centers worldwide.

A Simple Example:

Consider a toy box filled with a wide variety of toys. Sometimes you might want to play with a specific toy, but finding it in the toy box takes a while. This is similar to how computers work when they need to find information.

Distributed cache is like a special box where we put the toys we play with the most, so they're always easy to find. It's like having a special box just for your favorite toys, so you don't have to look for them in the big toy box whenever you want to play with them.

In the same way, computers use distributed cache to store important information they use a lot, so they don't have to search for it whenever they need it. This makes the computer faster and more efficient, like having your favorite toys always within reach!

1. Utilizing multiple nodes to store frequently accessed data in memory, distributed caching is a technique used in computer systems to increase performance and scalability.
2. Memory cache is a key component of distributed cache. It is responsible for storing the data in memory rather than accessing it from slower storage devices like hard disks.
3. Data storage is another important component of distributed cache, which is responsible for storing the data permanently and can be used to rebuild the cache if needed.
4. Distributed cache works by distributing the cached data across multiple nodes, making it available to multiple users, and ensuring high availability in case of node failures.
5. Web applications can use caching services to store user sessions and frequently accessed data, enhancing overall performance and responsiveness.

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Why is it Used in Our Web Apps?

Companies use distributed cache in their web applications (i.e. distributed applications) for several reasons.

• The primary reason is to improve the performance and scalability of the application.

By caching frequently accessed data in memory, they can reduce the number of database calls needed, significantly improving the application's response times.

Caching also helps to reduce the latency of the application by allowing the application to retrieve data quickly from memory rather than from slower storage devices like hard disks. This results in faster response times for users, which can improve their overall experience with the application.

• Distributed cache also helps to improve the availability and reliability of web applications.

By spreading the cached data across multiple nodes, they can ensure it is available even if one or more nodes fail. This means the application can function normally even during a hardware or software failure.

Overall, using distributed cache in web applications can help to improve performance, reduce latency, and increase availability and reliability.

This makes it a critical component of modern web application architectures.

A Good Example:

Many enterprise applications that use distributed cache are eBay, Amazon, etc.

eBay and Amazon are online marketplaces that handle a lot of traffic and user activity.

They use a distributed cache to store frequently accessed data to ensure the application is responsive and can handle the high traffic volume.

When a user searches the marketplace for a specific item, the search results are cached in memory to retrieve subsequent searches for the same item quickly without hitting the database. This helps improve the application's response times and reduce user latency.

In addition to caching search results, they also cache user profiles, item listings, and other frequently accessed data.

This helps to reduce the load on the database servers and ensures that the application can handle the high traffic volume and user activity.

Using a distributed cache, eBay and Amazon can improve their application's performance and scalability while ensuring high availability and reliability.

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How Distributed Cache Works at Twitter

At Twitter, they use a distributed cache to help their web application run faster and more efficiently.

For example, the cache stores frequently accessed data in memory, using a combination of memory cache and data store. In addition, they use a popular open-source distributed cache called Redis, designed for high performance and scalability.

When a user requests information from Twitter's web application, the application first checks the distributed cache to see if the requested information is already in the cache. If the data is discovered in the cache, it can be retrieved much more quickly than if it had to be fetched from the data store or disk.

If the data is not found in the cache, the application retrieves it from the data store or disk and then stores it in the cache for future requests.

This is where linked list and cache-based algorithms come into play, ensuring that the most frequently accessed data is stored in the cache and less frequently accessed data is evicted to make room for new data.

Twitter's distributed cache comprises multiple nodes spread across their data centers. This helps ensure high availability and redundancy in case any node goes down. The cache is also designed to store user session data, which helps to maintain user state across multiple requests.

In summary, Twitter uses distributed cache to store frequently accessed data in-memory, using a combination of memory cache and data store.

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Common Use Cases for Distributed Cache

Several use cases for distributed caches are listed below:

Caching of Databases:

To reduce latency and unnecessary load on a database, the Cache layer placed in front of it stores frequently used data in memory. When the cache is used, there is no DB bottleneck.

User Session Retention:

User sessions are cached to avoid losing the user state if any instances fail.

If any instances fail, a new instance starts, reads the user state from the cache, and continues the session without the user noticing anything is wrong.

Shared Storage & Inter-Module Communication:

Distributed in-memory caching is also used for message exchange between the various micro-services operating in tandem with one another.

It stores the shared data that all of the services access frequently. It serves as the foundation for communication among micro-services. In particular use cases, distributed caching is frequently used as a NoSQL datastore.

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Benefits of Distributed Cache

These are some of the core benefits of using a distributed cache methodology.

1. Keeps frequently accessed data in memory, which enhancing the application's response time and user experience.
2. Allows for adding more nodes to the cluster, enabling applications to scale horizontally without impacting performance.
3. Can replicate data across multiple nodes, ensuring that data is always available even if one or more nodes fail.
4. By caching data in memory, distributed cache reduces the need for network requests to fetch data from a database or file system, reducing network traffic and improving performance.
5. Reduces the need for expensive hardware upgrades or additional database lisences, making it a cost-effective solution for scaling applications.
6. By replicating data across multiple nodes, distributed cache ensures nodes have the same data, reducing the risk of inconsistencies.
7. Can handle high data requests, making it suitable for applications requiring high throughput.
8. Uses advanced algorithms, such as a linked list or LRU (Least Recently Used) caching, to ensure that frequently accessed data is always available in memory.
9. Can store user session data, improving the performance and scalability of web applications.
10. Can be integrated with other systems like Apache Kafka to provide real-time data processing capabilities.

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What is HarperDB

HarperDB is an edge data platform that can be an excellent web application caching option. HarperDB stores data across multiple nodes as a distributed database, allowing fast and efficient access to cached data.

HarperDB's cache-based architecture is designed to store frequently accessed data in the cache, using a linked list to quickly access the cached data. In addition, the cache can be configured to evict no longer needed data, ensuring that the most relevant data is always available.

By using a distributed cache, HarperDB can take advantage of the power of distributed systems, ensuring that data is always up-to-date and highly available for use by web applications.

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Why HarperDB is an excellent caching option:

• Uses an in-memory cache to store frequently accessed data.
• Designed to work as a distributed cache, with data stored across multiple nodes.
• Can store structured and unstructured data, making it a versatile caching option for many web applications.
• Uses a linked list to organize and manage cached data.
• It is intended to function as a cache, storing data in memory for quick access.
• HarperDB allows you to set limits on the amount of data stored in the cache, with the ability to automatically evict data that hasn't been accessed recently.
• Can store user session data in the cache, allowing for fast and efficient retrieval of user data.
• Designed to automatically update cached data when changes are made to the underlying data store.
• HarperDB can be integrated with popular web application servers, such as Apache Tomcat and Nginx, making it easy to use in many web applications.

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Conclusion

Distributed cache is an essential component in modern web applications that can help improve application performance, scalability, and user experience. For example, it can reduce application latency, improve response times, and enable faster data access by storing frequently accessed data in memory.

HarperDB is an excellent caching option that can help you leverage the benefits of distributed cache in your web applications. With its memory cache and data store architecture, it can provide a reliable and scalable caching service that can store and manage large amounts of data across multiple nodes.

HarperDB's distributed cache works seamlessly with web applications, enabling them to store user sessions, cache frequently accessed data, and update it in real-time.

Whether you're developing a small web application or a large-scale distributed system, HarperDB's caching services can help you improve performance, reduce latency, and scale easily.