3.4 NoSQL stores and caching
You can choose a non-relational store for a real reason and add a cache without corrupting data.
Before:02. ComputeUnlocks:04. Networking in the Cloud08. Observability & Reliability
The same thing, three names
One idea, sold under a different brand by each provider. Learn it once and you can read any job advertisement.
- On AWS
- DynamoDB + ElastiCache
- On Azure
- Cosmos DB + Azure Cache for Redis
- On Google Cloud
- Firestore/Bigtable + Memorystore
Key-value, document and wide-column stores each fit a shape of data and access, and this topic is about choosing one for a real reason rather than for fashion. Partition keys get particular attention because the hot-partition failure is how well-designed tables die under load. Caching joins the same topic because it shares the failure mode: cache-aside and write-through patterns, TTLs, and the invalidation bugs that serve stale data. The common confusion is adding a cache to fix a slow query nobody examined; the cache then hides the problem until it returns at scale.
Work through these
Key-value, document, wide-column and when each fits
Key-value, document and wide-column stores each suit a different access pattern, and choosing one because it is not relational is not a reason. This item is about having an actual reason.
Partition keys and the hot-partition failure
The partition key decides how data is spread, and a badly chosen one sends most traffic to one partition while the rest sit idle. That failure is silent until you are at scale.
Caching patterns: cache-aside, write-through, TTLs
The common caching arrangements differ in who writes to the cache and when, and every one of them needs an expiry policy. Getting this right is what keeps a cache from serving yesterday's answers.
Cache invalidation and stale-data bugs
Deciding when a cached copy stops being valid is famously the hard part, and stale data bugs are the result of getting it wrong. Naming the failure makes it recognisable in the wild.
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