Do you use popular apps like Facebook, Instagram and Amazon? They are some of the most visited websites.
Have you ever noticed that they load very quickly compared to new websites? Also, have you noticed that when you use a slow internet connection, the text on a website loads faster than high-quality images?.
Computers keep critical information they might need again later in a cache which is similar to a temporary storage area. It functions like a little notebook where you can rapidly record critical information for later reference Instead of beginning again every time the computer wants to find anything it first looks in the cache to see whether it has already been saved there and If so, the information can be instantly accessed by the computer much like finding it in the notebook.
How It works
Imagine you have a shelf with all your favorite books in your room, It takes time to go to the shelf and search for the book you want to read then take it back to your bed to start reading.
Now, imagine if you had a smaller shelf near your bed that only holds a few books where you can keep the books you are currently reading on this shelf. This way, you do not have to walk to the big shelf every time you want to change the book and you can easily access the books you are currently reading from the small shelf.
A cache works similarly because It is like a small shelf near your computer's RAM (Random-access memory) that stores frequently used data so it can be accessed more quickly. This means that when you need that data you can access it faster and without having to go all the way to the slower storage layer like a hard drive.
What are the types of caching
There are 5 major types of caching;
Browser caching: This is a type of caching that occurs on the user's browser. When a user visits a website for the first time, their browser will store certain files such as images, stylesheets and JavaScript files in its cache.
When the user visits the website again the browser will retrieve these files from the cache instead of downloading them again which can improve the website's load time.
Server caching: This is a type of caching that occurs on the server when a user requests data, the server will cache the data so that it can be quickly retrieved the next time it is requested.
Server caching can help to improve website performance and reduce server load.
Content delivery network (CDN) caching: A CDN is a network of servers that are distributed around the world. When a user requests data from a website that uses a CDN the data will be retrieved from the server that is closest to the user then the CDN server will cache the data so that it can be quickly retrieved the next time it is requested by any user in the same geographic area.
Database caching: This is a type of caching that occurs when frequently accessed data is stored in memory rather than being retrieved from the database every time it is requested. Database caching can help to improve website performance by reducing the number of database queries that need to be made.
Application caching: This is a type of caching that occurs within the application itself.
Application caching can be used to store frequently accessed data or to reduce the number of requests that need to be made to external services or APIs.
Benefits of caching
Caching helps make applications run faster by storing data in fast memory (RAM) instead of slower storage (like a hard drive), this means that the application can retrieve data much more quickly which improves overall performance.
Caching can also help save money by reducing the need for expensive database instances by using a high-throughput in-memory cache, the application can perform hundreds of thousands of input/output operations per second, which reduces the load on the backend database and can even eliminate hotspots in the database and this can help avoid costly charges for high database throughput.
Caching can also help ensure predictable performance even during spikes in application usage by utilizing a high-throughput in-memory cache, the application can maintain fast and predictable performance even during times of high usage.
Caching can also help increase read-throughput (IOPS) by providing much higher request rates relative to a disk-based database where a single instance used as a distributed side cache can serve hundreds of thousands of requests per second making it possible to handle large amounts of traffic without slowing down.
Caching strategies
How you fill and keep your cache up-to-date depends on what kind of information you store and how people use that information. For example, you wouldn't want to use the same approach for managing a list of popular articles and tracking the latest scores of a game.
Here, we will talk about common ways to maintain your cache.
Lazy loading, write-through and adding TTL are three common strategies used in caching to optimize performance.
Lazy Loading: Lazy loading means loading data into the cache only when it is needed, which means that the cache is initially empty but data is added to it as it is requested.
This method saves resources by not loading unnecessary data into the cache. However, the first request for data may take longer since the cache must obtain it from the base storage layer.
Write-through: Write-through is a strategy that involves writing data to both the cache and the base storage layer at the same time and this ensures that the data is always up-to-date in both locations.
The advantage of this approach is that it ensures consistency between the cache and the underlying storage layer. However, it can be slower than other strategies because the data needs to be written to both locations.
Adding TTL: Adding TTL (Time To Live) to cached data is a strategy that sets an expiration time for data stored in the cache, when the TTL expires the data is automatically removed from the cache.
The advantage of this approach is that it frees up space in the cache for new data and ensures that the cached data is not outdated. However, setting the TTL too short can result in unnecessary data evictions while setting it too long can result in stale data being served.
Cache eviction policies
Cache eviction policies are rules that determine how data in a cache is managed. These rules decide how and when data should be removed from the cache.
Common cache eviction policies include Least Recently Used (LRU), First In First Out (FIFO), and Random Replacement.
Least Recently Used (LRU): This policy removes the least recently accessed data first.
First In First Out (FIFO): This policy removes the oldest data in the cache first.
Random Replacement: This policy randomly selects data to remove from the cache.
Cache synchronization
When we use a cache we copy the information so it is in two places. To make sure we don't have problems with different information in different places we need to make sure the original information and the copy are always the same otherwise we risk serving outdated data to our users. So to keep the cache synchronized we need to have a way to detect changes in the system of record and update the cache accordingly.
These are popular types of cache synchronization;
Write-through: In this strategy, data is written simultaneously to both the cache and the backend database and this ensures that both the cache and the database have the same data and reduces the risk of data inconsistencies.
Write-behind: This strategy involves writing data only to the cache first and then asynchronously updating the backend database at a later time. This can improve performance by reducing the number of writes to the backend database but there is a risk of data inconsistencies if the backend database is not updated on time.
Cache-aside: Data is only read from the cache when it is requested by the application and If the data is not in the cache the application will fetch it from the backend database and then add it to the cache for future requests.
This strategy can be more efficient than the other two strategies as it only caches data that is used by the application but it can lead to a higher initial latency when data is first requested.
Some common Caching challenges
These are some common caching challenges;
Cache Invalidation: Caching can lead to outdated data which can lead to incorrect results. When the data in the database changes and the cache should be invalidated to ensure that the correct data is used.
Cache Consistency: Consistency between the cache and the database is important because It is essential to make sure that the data in the cache is consistent with the data in the database.
Cache Concurrency: Multiple requests can access the cache at the same time leading to data inconsistencies or cache thrashing So we need to have systems in place to make sure that the data in our cache is always up-to-date and matches the data in our main database.
Cache Size: If the cache size is too small then data eviction can occur frequently leading to a reduction in performance and if the cache size is too large, it can lead to memory problems.
Cache Performance: The performance of a cache can be impacted by the cache configuration, the size of the cache, the number of nodes in the cache and the cache architecture.
Cache Security: Caching can lead to data leaks if sensitive data is stored in the cache. Therefore, Access control mechanisms should be in place to ensure data privacy and security.
Caching in different layers
Caching can be implemented in various layers of a web application to improve performance and reduce the load on the backend, The three main layers where caching can be implemented are:
Client-Side Caching: This type of caching is implemented on the user's device such as a web browser. When a user visits a website the browser stores some data like HTML, CSS, JavaScript and images in the local storage of the user's device. When the user revisits the website the browser checks the local storage and loads the data from there instead of downloading it again from the server which saves time and reduces network usage.
Server-Side Caching: This type of caching is implemented on the server side of a web application and it involves storing frequently accessed data such as database queries, in the server's memory or a separate cache server. When a user requests this data the server retrieves it from the cache instead of querying the database or other external sources which can be time-consuming, therefore this process reduces the load on the database and improves the performance of the application.
Database Caching: This type of caching involves storing frequently accessed data in the database's memory or a separate cache server which allows the application to retrieve the data faster and reduces the load on the backend database. Database caching can be implemented at different levels such as query caching object caching or result-set caching depending on the type of data that needs to be cached.
Caching frameworks
Some popular frameworks you might have heard of;
Redis: Redis is another open-source caching framework that stores data in memory but it also supports persistent storage to disk, Redis is highly configurable and can be used for caching, message queuing and as a database. It is known for its high performance and support for advanced data types like hashes, sets and sorted sets.
NGINX: NGINX is a high-performance web server and reverse proxy that can be used to improve the performance of web applications, It provides a powerful caching module that can be used to cache static and dynamic content and it helps to improve the performance of web applications by serving cached content directly from memory therefore reducing the load on application servers and improving response times.
Apache Ignite: Apache Ignite is an in-memory computing platform that provides distributed caching, computing and processing capabilities, it supports a wide range of data structures and can be used for high-performance transaction processing, real-time analytics and machine learning.
Memcached: Memcached is an open-source caching framework that stores data in memory and can be used across multiple servers it is highly scalable and can handle large amounts of data. Memcached is commonly used in web applications to improve performance and reduce database load.
Guava Cache: Guava Cache is a caching framework provided by Google as part of the Guava library, It is a lightweight in-memory cache that provides features like automatic eviction, cache loading and cache statistics.
Caching best practices
list of some caching best practices:
Determine the right cache size: The cache size should be neither too small nor too large.
A cache that is too small will lead to more cache misses and a cache that is too large will lead to unnecessary memory consumption.
Set an appropriate expiration time: Caching data indefinitely can lead to outdated data which can be harmful to the application, Setting an appropriate expiration time helps to ensure that the data in the cache is fresh.
Use the appropriate caching strategy: Different data types and access patterns require different caching strategies and It is important to use the appropriate strategy to optimize performance.
Keep data in sync: If the data in the cache is outdated, it can cause inconsistencies between the cache and the backend data store therefore It is important to keep the data in sync to avoid these inconsistencies.
Monitor cache performance: Monitoring cache performance can help to identify issues and optimize performance.
Metrics like cache hit rate, cache miss rate and cache size can be used to monitor performance.
Fields Where caching is applied
Application in microservices: In microservices, cache can be used to store frequently accessed data and reduce the number of calls made to other microservices or databases, this can improve the performance of the system and reduce the load on the network. Caching can also help to maintain the consistency of data across multiple microservices and reduce the risk of data inconsistencies.
Application in distributed systems: Where different parts of an application run on different machines, caching can be used to improve performance and reduce the load on the network. Caching data closer to the application or user can reduce the number of requests that need to be sent across the network which can improve the overall speed and reliability of the system also reducing the load on individual nodes in the system making it easier to scale and maintain.
Application in cloud environments: cache is often used to improve the performance of applications and reduce the load on backend services. Cloud providers typically offer caching services that can be used to store frequently accessed data such as session data, metadata and configuration information. This can help reduce the amount of data that needs to be retrieved from slower storage layers, such as databases and improve the overall performance of the application.
Application in IoT: cache can be used to improve the performance and efficiency of devices and systems. Caching data in the edge devices can reduce the amount of data that needs to be sent back to the cloud for processing and analysis which can save time and resources. It can also help ensure that critical data is always available even if there is a loss of connectivity. By caching data at the edge, IoT devices can operate more efficiently and reliably thereby improving the overall user experience.
Application in big data: Cache can be used to improve the performance of data processing and analysis, Big data systems typically involve large amounts of data that need to be processed in real-time or near real-time. Caching can help reduce the time it takes to access and retrieve this data which can improve the overall performance of the system.
By caching commonly accessed data, the system can avoid repeatedly fetching the same data from slower storage layers such as disk or network storage and instead retrieve it quickly from the cache.
Application in machine learning: Caching can be used to speed up the processing of large datasets that are frequently accessed. By storing frequently used data in memory, machine learning algorithms can access the data faster, improving the overall performance of the model. Caching can also be used to save the results of intermediate computations so that they can be reused later further improving the speed of the model. Additionally, caching can be used to store the weights of trained models so that they can be quickly accessed and reused for inference or further training.
Application in eCommerce: caching can be used to display personalized content and promotions to customers leading to better engagement and higher conversion rates, it can be used to improve the speed and performance of the online store. By storing frequently accessed data in the cache, the eCommerce application can quickly retrieve the data and avoid repeatedly querying the database which can slow down the system. Caching can also reduce the load on the backend, improve scalability and ensure a smoother shopping experience for customers. Commonly cached data in eCommerce includes product information, user session data and shopping cart details.
Application in social media: Caching is widely used in social media applications to improve performance and reduce the load on servers. In social media, data like profiles, posts and images are frequently accessed by users. Caching allows the most popular data to be stored in memory so that it can be accessed quickly without having to repeatedly fetch it from the server. This helps to reduce latency and improve the overall user experience. Additionally, caching can help reduce the load on the backend servers ensuring that the application is available and responsive even during high-traffic periods. Some social media applications also use caching to implement features like personalized recommendations, trending topics, and real-time updates.
Application of Caching in web development
SEO Management: This involves optimizing a website's content and structure to make it more visible and rank higher on search engine results pages (SERPs), thus driving more traffic to the website.
User Experience: This refers to the overall experience a user has when interacting with a website or application. Caching can improve user experience by reducing load times and improving performance.
Session Management: In web applications, a session is a period of interaction between the user and the server. Caching can help improve session management by reducing the load on the server and improving performance.
Content delivery: Caching can improve content delivery by caching frequently accessed content on edge servers closer to the end user, thus reducing the time it takes for the content to reach the user.
API development: Caching can improve API performance by reducing the number of requests made to the API, thus reducing the load on the server and improving response times.
Summary
Caching is a fascinating concept that involves storing frequently accessed data in a faster storage location such as memory or disk to reduce access times and improve performance. It is used in a variety of applications including websites, mobile apps and databases to speed up data retrieval and reduce the load on servers. Whether it's optimizing website performance or building scalable microservices, caching is an essential tool for modern-day computing.
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