“There are only two hard things in Computer Science: cache invalidation and naming things.”
– Phil Karlton
- In-memory caching
- HTTP caching
- Caching patterns
Caching is a special form of replication where data is copied by a client and saved for later use [1, P. 23].
Caching can improve a system’s response time significantly and help improve the scalability of a system by reducing load [2, P. 2].
Some commonly cached items include:
- HTML pages (partial or full)
- API responses
- Database queries
- Session data
One form of caching is in-memory caching. With in-memory caching, the result of an expensive computation or network request is stored in memory to be accessed later.
A local cache stores entries on the same machine, usually in a hash table. This makes data retrieval fast because accessing the cache doesn’t involve the network, however it means that each node in a system will have its own cache. [3, P. 2].
A more robust solution is to use a remote (or distributed) cache. A remote cache is a separate instance that is dedicated to storing cached values in memory. Commonly, remote caches are distributed key-value stores like Redis or Memcached which can handle millions of requests per second per node. With a remote cache, multiple nodes can share the same cache [3, Pp. 2-3].
In-memory caches generally expose an API to get, set, and delete entries:
set(key, value, ttl)
In order to stop the cache from growing indefinitely, caches remove items when the cache reaches a certain size. The items that are removed depend on the cache’s eviction policy.
A cache’s eviction policy determines the order in which entries are removed from a full cache.
LRU (Least Recently Used) is a popular eviction policy that removes the least recently used entries first. LRU requires extra memory to track the relative usage of entries.
LFU (Least Frequently Used) removes the least frequently used entries first. Again, LFU requires extra memory to track the usage of entries.
FIFO removes items in the order they were added to the cache.
A TTL eviction policy evicts entries after they expire, based on a TTL (Time To Live) value. To determine a suitable TTL value you should consider both how frequently the origin data is updated, and what the consequences of returning stale data are [3, P. 6].
The HTTP protocol supports the caching of some responses. HTTP caching can significantly improve the performance of a web site or API.
Browsers, proxies, reverse proxies, and CDNs can all be used to cache HTTP assets.
HTTP distinguishes between shared caches and private caches. Shared caches can be accessed by multiple users (e.g., a CDN) whereas private caches are user-specific (e.g., a browser) [4, P. 4].
HTTP caches normally store cached assets to disk, with the mapping keys stored in memory.
For more details on the specifics of HTTP/1 caching, see the HTTP1 section on caching.
Caching patterns are design patterns for integrating a cache into a system.
In the cache-aside pattern data is loaded into the cache as it’s required.
The workflow is:
- Before executing an expensive operation, the application checks to see if the result already exists in the cache.
- If the data is available (a cache hit), the application returns the cached data.
- If the data is not available (a cache miss), the application executes the expensive operation and stores the result in the cache for future use.
def get_image(image_id): image = cache.get(image_id) if image == None: image = db.get_image(image_id) cache.set(image_id, image, IMAGE_TTL) return image
One advantage of the cache-aside pattern is that the cache only contains data that the application has actually requested [3, P. 5].
One downside of the cache-aside pattern is that each cache miss adds extra latency [3, P. 5].
In the read-through pattern, all requests go through the cache. If the requested item is not in the cache, then the cache fetches the item from the data source [5, P. 6].
The read-through pattern is similar to the cache-aside pattern in that data is only requested as it’s required.
In the write-through pattern, an application writes to the cache at the same time as it writes to the database [5, P. 7].
def edit_comment(comment_id, comment_content): db.update_comment(comment_id, comment_content) cache.set(comment_id, comment_content)
When paired with the cache-aside pattern, write-through ensures that data in the cache is always up-to-date [5, P. 7].
In the write-back pattern (also known as the write-behind pattern) the client updates the cache, rather than the backing data store. The cache is then responsible for updating the backing data store after some delay [5, P. 9].
The write-back pattern improves write performance and works well for write-heavy workloads [5, P. 9].
One downside of the write-back pattern is that pending updates can be lost in the case of failure [5, P. 9].
A cache is said to be cold when it has no entries. A cold cache will result in many cache misses.
A warm cache contains many entries, resulting in more cache hits.
Pre-warming a cache involves loading entries before using the cache in production. Pre-warming can help improve performance by reducing the number of cache misses.
CDNs (Content Delivery Networks) improve latency, reliability, and redundancy by replicating resources geographically and load balancing requests between replicas [6, P. 1].
A CDN consists of edge servers which serve content and origin servers which supply the content [7, P. 5].
Request-routing infrastructure directs user requests to the closest edge server. Akamai, for example, hosts its own authoritative DNS servers to dynamically resolve domain names to a suitable edge server based on the user’s location and Akamai’s network data [7, P. 16].
Commonly, CDNs take a pull-based approach to serving content. If an edge server is unable to fulfill the user request (e.g., if it does not have the requested content in its cache), then it must make a request to an origin server to get the content. Once the CDN has the content, it can cache the response for future use [7, P. 16].
Some CDNs take a push-based approach, for example Netflix’s Open Connect.
Open Connect is Netflix’s push-based CDN for serving video and image files [8, P. 3].
Open Connect is made up of OCAs (Open Connect Appliances) and a control plane that manages the OCAs. The control plane is also responsible for resolving client requests to a list of OCA URLs that clients can use to fetch static assets from [8, P. 3].
Each OCA stores a portion of the Netflix catalog. During off-peak hours, the OCAs contact control plane services to update their content [8, P. 5].
OCAs are installed at thousands of IXPs and in ISP data centers around the world [8, P. 2].
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-  I. Haber, “15 Reasons to use Redis as an Application Cache,” 2016.
-  AWS, “Database Caching Strategies Using Redis,” 2017.
-  R. T. Fielding, M. Nottingham, and J. Reschke, “Hypertext Transfer Protocol (HTTP/1.1): Caching,” no. 7234. RFC Editor, Jun-2014.
-  I. Haber, “Whitepaper: Deploying a Highly Available Distributed Caching Layer on Oracle Cloud Infrastructure using Memcached & Redis,” 2018.
-  A. Pathan and R. Buyya, “A taxonomy and survey of content delivery networks,” Technical Report, GRIDS-TR-2007-4, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia., Feb-2007.
-  E. Nygren, R. K. Sitaraman, and J. Sun, “The Akamai Network: A Platform for High-Performance Internet Applications,” SIGOPS Oper. Syst. Rev., vol. 44, no. 3, pp. 2–19, Aug. 2010.
-  Netflix, “Open Connect Overview.” 2019.