Top 18 Caching Interview Questions And Answers

Question 1

What is Caching?

Accepted Answer

In computing, a **cache** is a high-speed data storage layer which stores a subset of data, typically transient in nature, so that future requests for that data are served up faster than is possible by accessing the data’s primary storage location. _Caching allows you to efficiently reuse previously retrieved or computed data_.

Question 2

What is Cache Invalidation?

Accepted Answer

> There are only two hard things in Computer Science: cache invalidation and naming things.
>
> – Phil Karlton

HTTP caching is a solution for improving the performance of your web application. For lower load on the application and fastest response time, you want to cache content for a long period (TTL). But at the same time, you want your clients to see fresh (_validate the freshness_) content as soon as there is an update.

**Cache invalidation** gives you the best of both worlds: you can have very long TTLs, so when content changes little, it can be served from the cache because no requests to your application are required. At the same time, when data does change, that change is reflected without delay in the web representations.

Question 3

What usually should be cached?

Accepted Answer

The results for the following processes are good candidates for caching:

* Long-running queries on databases,
* high-latency network requests (for external APIs),
* computation-intensive processing

Question 4

Is Redis just a cache?

Accepted Answer

Like a cache Redis offers:
*   in memory key-value storage

But unlike a cash Redis:
* Supports multiple datatypes (strings, hashes, lists, sets, sorted sets, bitmaps, and hyperloglogs)
* It provides an ability to store cache data into physical storage (if needed).
* Supports pub-sub model
* Redis cache provides replication for high availability (master/slave)
* Supports ultra-fast lua-scripts. Its execution time equals to C commands execution.
* Can be shared across multiple instances of the application (instead of in-memory cache for each app instance)

Question 5

Name some Cache Writing Strategies

Accepted Answer

There are two common strategies to write data in a cache:

1.  **Pre\-caching data**, for small pieces of data, usually during the application initialization, before any request.
2.  **On\-demand**, checking first if the requested data is in the cache (if the data is found, it is called a *cache hit*), using it, improving the performance of the application. Whenever the requested data has not been written to the cache (*cache miss*), the application will need to retrieve it from the slower source, then writing the results in the cache, thus saving time on subsequent requests for the same data.

Question 6

What is Resultset Caching?

Accepted Answer

**Resultset caching** is storing the results of a database query along with the query in the application. Every time a web page generates a query, the applications checks whether the results are already cached, and if they are, pulls them from an in-memory data set instead. The application still has to render the page.

Question 7

Name some Cache Invalidation methods

Accepted Answer

* **Purge** - Removes content from _cache_ immediately. When the client requests the data again, it is fetched from the application and stored in the cache. This method removes all variants of the cached content.

* **Refresh** - Fetches requested content from the application, even if cached content is available. The content previously stored in the cache is replaced with a new version from the application. This method affects only one variant of the cached content.

* **Ban** - A reference to the cached content is added to a blacklist (or ban list). Client requests are then checked against this blacklist, and if a request matches, new content is fetched from the application, returned to the client, and added to the cache. This method, _unlike purge_, does not immediately remove cached content from the cache. Instead, the cached content is updated after a client requests that specific information.

Question 8

What are some alternatives to Cache Invalidation?

Accepted Answer

There are three alternatives to cache invalidation.

* The first is to **expire** your cached content quickly by reducing its time to live (TTL). However, short TTLs cause a higher load on the application because content must be fetched from it more often. Moreover, reduced TTL does not guarantee that clients will have fresh content, especially if the content changes very rapidly as a result of client interactions with the application.
* The second alternative is to** validate the freshness** of cached content at every request. Again, this means more load on your application, even if you return early (for instance by using HEAD requests).
* The last resort is **to not cache volatile content** at all. While this guarantees the user always sees changes without delay, it obviously increases your application load even more.

Question 9

Compare caching at Business Layer vs Caching at Data Layer

Accepted Answer

* **Caching on the DAL is straightforward and simple**. Data access and persistence/storage layers are irresistibly natural places for caching. They're doing the I/Os, making them handy, easy place to insert caching. I daresay that almost every DAL or persistence layer will, as it matures, be given a caching function-if it isn't designed that way from the very start. Caching at the DAL/persistence layer risk having the "cold" reference data sitting there, pointlessly occupying X mb of cache and displacing some information that will, in fact, be intensively used in just a minute. Even the best cache managers are dealing with scant knowledge of the higher level data structures and connections, and little insight as to what operations are coming soon, so they fall back to guesstimation algorithms.
* **Caching in the business is flexible and potentially more efficient**. Application or business-layer caching requires inserting _cache management operations_ or hints in the middle of other business logic, which makes the business code more complex. But the tradeoff is: Having more knowledge of how macro-level data is structured and what operations are coming up, it has a much better opportunity to approximate optimal ("clairvoyant" or "Bélády Min") _caching efficiency_.

Whether inserting cache management responsibility into business/application code makes sense is a judgment call, and will vary by applications.  Lower complexity encourages higher correctness and reliability, and faster time-to-market. That is often considered a great tradeoff - less perfect caching, but better-quality, more timely business code.

Question 10

Why is Cache Invalidation considered difficult?

Accepted Answer

The **non-determinism** is why cache invalidation are unique and intractably hard problem in computer science.

* Computers can perfectly solve **deterministic** problems. But they can’t predict when to _invalidate_ a cache because, ultimately, we, the humans who design and build computational processes, shall agree on when a cache needs to be invalidated. The hard and unsolvable problem becomes: _how up-to-date do you really need data to be and when to change (or remove) it_?

* Another problem a cache is (often by nature) much smaller compared to the overall amount of data that needs to be stored and if you just keep adding and adding elements to your cache, it becomes a full copy of your data. Respectively, you _run out of memory_ quickly.

Basically it's difficult to achieve a desirable balance between stale objects stinking up your cache, and frequent unnecessary refreshes of unchanged objects considering limitation of the cache size.

Top 18 Caching Interview Questions

What is Caching?

What is Resultset Caching?

Name some Cache Writing Strategies

What is Cache Invalidation?

Is Redis just a cache?

What usually should be cached?

What are some alternatives to Cache Invalidation?

Name some Cache Invalidation methods

What are some disadvantages of Cache Invalidation?

Explain what is Cache Stampede

What is the difference between Cache replacement vs Cache invalidation?

Why is Cache Invalidation considered difficult?

What are Cache Replacement (or Eviction Policy) algorithms?

Compare caching at Business Layer vs Caching at Data Layer

When to use LRU vs LFU Cache Replacement algorithms?

What are best practices for caching paginated results whose ordering/properties can change?

Cache miss-storm: Dealing with concurrency when caching invalidates for high-traffic sites

Name some Cache Stampede mitigation techniques