In-memory caching stores data in the application's memory, which allows for quick and easy access to the cached data. This type of caching is particularly useful for frequently accessed data that can be regenerated quickly, such as session data.
AWS: ElastiCache, DynamoDB Accelerator (DAX)
GCP: Cloud Memorystore, Cloud Datastore
Azure: Azure Cache for Redis, Azure Cosmos DB
Redis, Memcached, Hazelcast
Distributed caching
Distributed caching distributes the cached data across multiple servers, which can improve performance and scalability. This type of caching is particularly useful for large-scale applications that require high performance and availability.
AWS: ElastiCache, DynamoDB Accelerator (DAX)
GCP: Cloud Memorystore, Cloud Datastore
Azure: Azure Cache for Redis, Azure Cosmos DB
Hazelcast, Memcached, Apache Ignite
Client-side caching
Client-side caching stores data in the user's browser, which can improve the performance of web applications by reducing the number of server requests. This type of caching is particularly useful for static resources such as images, stylesheets, and JavaScript files.
AWS: CloudFront, S3
GCP: Cloud CDN, Firebase Hosting
Azure: Azure Content Delivery Network
Content delivery network (CDN) caching
A CDN is a network of distributed servers that can cache and deliver content to users from a server that is geographically closer to them. This type of caching is particularly useful for delivering static content such as images, videos, and other large files.
AWS: CloudFront, S3
GCP: Cloud CDN, Firebase Hosting
Azure: Azure Content Delivery Network
Akamai, Cloudflare, Fastly
Database caching
Database caching caches the results of database queries to improve the performance of database-driven applications. This type of caching is particularly useful for applications that require frequent database access.
Apache Ignite, Coherence, GigaSpaces
API caching
AWS: API Gateway, ElastiCache
GCP: Apigee API Platform, Cloud Memorystore
Azure: Azure API Management, Azure Cache for Redis
Kong, Tyk, AWS API Gateway caching
Object Storage caching
AWS: S3, ElastiCache
GCP: Cloud Storage, Cloud Memorystore
Azure: Azure Blob Storage, Azure Cache for Redis
Caffeine, JCS, Ehcache
Application-level caching
Application-level caching caches frequently accessed data in the application itself, which can improve the performance of the application. This type of caching is particularly useful for applications that require high performance and low latency, such as real-time applications.
Full-page caching stores the entire HTML output of a page, which can significantly reduce the time it takes to load the page. This type of caching is particularly useful for high-traffic websites or e-commerce sites that require fast page load times.
AWS: CloudFront
GCP: Cloud CDN
Azure: Azure Content Delivery Network
Database query caching
Queries that are frequently executed can be cached in memory, reducing the load on the database and improving performance.
AWS: ElastiCache, DynamoDB Accelerator (DAX)
GCP: Cloud Memorystore, Cloud Datastore
Azure: Azure Cache for Redis, Azure Cosmos DB
Browser caching
Browsers can cache static content like images and scripts, reducing the amount of data that needs to be downloaded and improving page load times.
AWS: CloudFront, S3
GCP: Cloud CDN, Firebase Hosting
Azure: Azure Content Delivery Network
Expires header, Cache-Control header, ETag header
Reverse proxy caching
A reverse proxy server can cache content from a backend server, serving the cached content directly to clients instead of forwarding requests to the backend server. This can reduce the load on the backend server and improve performance.
AWS: Elastic Load Balancing, EC2 instances with NGINX or Varnish installed
GCP: Google Cloud Load Balancing, Compute Engine instances with NGINX or Varnish installed
Varnish, NGINX cache, Apache mod_cache, Apache Traffic Server
Message Caching
RabbitMQ, Apache Kafka, ActiveMQ
Disk caching
This type of caching involves storing frequently used data on the hard drive of the computer system. This can help to reduce the time it takes to retrieve the data from external sources, such as a web server.
Data is written to the cache and the backend store at the same time. This ensures that the data in the cache is always up-to-date, but it can slow down write operations. Write-through is ideal for applications that require strong consistency, such as financial applications.
Write-behind
Writes data to the cache first, and then asynchronously writes the data to the backend store. This strategy is used to speed up write operations by reducing the time it takes to write to the backend store. Write-behind is ideal for applications where write performance is critical, such as gaming applications.
Read-through
When a cache miss occurs, the cache retrieves the data from the backend store and then stores it in the cache before returning the data to the client. This strategy is used to speed up read operations and reduce the load on the backend store. Read-through is ideal for applications that have a high read-to-write ratio, such as news or weather applications.
Read-behind
This strategy is similar to write-behind, but for read operations. When a cache miss occurs, the cache asynchronously retrieves the data from the backend store and stores it in the cache for future reads. This strategy is used to speed up read operations by reducing the time it takes to read from the backend store. Read-behind is ideal for applications that have high read throughput and large datasets, such as e-commerce applications.
Cache-aside
With this strategy, the application reads from and writes to the cache directly, and the cache is responsible for retrieving data from the backend store if it is not in the cache. This strategy is used to reduce the load on the backend store and speed up read operations. Cache-aside is ideal for applications that have a low read-to-write ratio, such as analytics applications.
Cache-pinning
This strategy involves keeping frequently accessed data in the cache, even if it is not being used. This strategy is used to reduce the frequency of cache misses and improve cache hit rates. Cache-pinning is ideal for applications that have a small number of frequently accessed data items.
Time-to-live (TTL)
Setting an expiration time for cached data, after which the data is invalidated and must be retrieved from the backend store. This strategy is used to ensure that cached data does not become stale and to free up space in the cache. TTL is ideal for applications that have data with a short lifespan, such as news or weather data.
Invalidating Cache:
Invalidating the cache whenever the data in the underlying data source changes. For example, if you're caching data from a database, you would invalidate the cache whenever a record is updated or deleted. This ensures that the cache always contains up-to-date data.
Lazy Loading:
This caching strategy involves loading data into the cache only when it's needed. For example, if you have a large dataset that's not frequently accessed, you might choose to lazily load the data into the cache only when a request is made for that data. This can help conserve memory and improve performance by only caching data that's actually used.
You can cache events in memory using a data structure such as a hash table or a linked list. This approach is suitable for small-scale systems where the number of events is limited.
Distributed caching
You can use a distributed caching system like Redis or Memcached to store and retrieve events. This approach is suitable for large-scale systems where the number of events is high.
Event Sourcing
Event Sourcing is a technique where every change to the system state is captured as an event and stored in an event log. The event log can be used to replay events and rebuild the system state. This approach is suitable for systems where the event history is critical to the system operation.
Log-based caching
You can cache events in a log-based system like Kafka or Apache Pulsar. These systems store events in a log, which can be used for replay and recovery in case of failure.
Hybrid caching
You can use a combination of the above approaches to create a hybrid caching system. For example, you could use in-memory caching for frequently accessed events and a distributed caching system for less frequently accessed events.
Caching Choices