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ElastiCache DVA

Links: 102 AWS DVA Index
Recommended Reads: ElastiCache

Caching strategies

  • If your data is changing very slowly then caching is a good idea.
  • Data should be structured carefully for caching.
  • Another important task is to find the appropriate caching strategy.
  • You will be asked to look at pseudo code and find out the type of strategy.

Lazy Loading / Cache-Aside / Lazy Population

  • Lazy loading loads data into cache only when necessary i.e. when there is a cache miss.
  • Only requested data is cached (the cache isn't filled up with unused data)
    • attachments/Pasted image 20220514103230.jpg
  • Cons
    • Cache miss penalty that results in 2 round trips, noticeable delay for that request. This means there is a read penalty.
    • Stale data: data can be updated in the database and outdated in the cache
  • Writes are fast but reads are slow.
  • Python pseudocode
    • attachments/Pasted image 20220514103411.jpg
    • get_user is for read.
You have an ElastiCache cluster with small cache size, so you want to ensure that only the data that's requested will be loaded into the cluster. Which caching strategy should you use?

Lazy Loading would load data into the cache only when necessary (actively requested data from the database).

Write Through

  • You add or update the cache when database is updated. Because of this data is never stale.
    • attachments/Pasted image 20220514104242.jpg
  • There is a write penalty (each write requires 2 network calls) instead of a read penalty.
  • Writes are slow but reads are fast.
  • Cons

    • Missing Data until it is added / updated in the DB.
    • So nothing will be done if there is a cache miss.
    • Mitigation is to implement Lazy Loading strategy as well for cache miss scenario.
  • Python pseudocode

    • attachments/Pasted image 20220514104053.jpg
    • save_user is for write
If you want minimum latency read requests and you are ok with write taking longer then go with write through.
If you want your cache to never go out of sync with the backend then go with write through cache

Cache Eviction and TTL

  • Cache eviction is the process of removing items from cache. It can occur in three ways:

    • You delete the item explicitly in the cache
    • Item is evicted because the memory is full and it's not recently used (LRU)
    • You set an item Time-To-Live (or TTL)
  • TTL can range from few seconds to hours or days

If too many evictions happen due to memory, you should scale up or out. Get a bigger cache.


  • Lazy Loading / Cache aside is easy to implement and works for many situations as a foundation, especially on the read side.
  • Write-through is usually combined with Lazy Loading as targeted for the queries or workloads that benefit from this optimisation.
  • Setting a TTL is usually not a bad idea, except when you're using Write through. Set it to a sensible value for your application
  • Only cache the data that makes sense (user profiles, blogs, etc...). Don't cache thinks like account balance.

There are only two hard things in Computer Science: cache invalidation and naming things

Last updated: 2022-06-09