Application Data Stores
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Included in this chapter:
- Pick the store from the access pattern
- DynamoDB data model: keys, item size, indexes
- Capacity, consistency, transactions, and conditional writes
- Reading at scale: Query vs Scan, batch, pagination, TTL, Streams
- Caching hot reads: DAX vs ElastiCache
Matching the AWS data store to the access pattern
| Store | Data model | Best access pattern | Consistency / latency note |
|---|---|---|---|
| Amazon DynamoDB | Key/value + document (NoSQL) | Single-key lookups and writes at massive scale | Eventual by default; strongly consistent on request; single-digit ms |
| Amazon RDS / Aurora | Relational (SQL) | Joins, ad hoc filters, multi-row transactions | Strongly consistent on primary; replicas can lag (usually < 100 ms on Aurora) |
| Amazon ElastiCache | In-memory key/value (Redis or Memcached) | Hot reads, sessions, leaderboards in front of a DB | Cache, not durable; sub-millisecond reads |
| Amazon DAX | In-memory cache for DynamoDB | Read-heavy DynamoDB tables, same API | Write-through cache; microsecond cached reads (eventual) |
| Amazon S3 | Object store by key | Large objects, files, backups, static assets | Strong read-after-write for new and overwritten objects |
Decision tree
Cheat sheet
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