DynamoDB Accelerator (DAX) Vs. ElastiCache | Overview.

DynamoDB Accelerator (DAX) Vs. ElastiCache - Overview.

Scope:

• Intro,
• The Core Difference,
• Architecture of DynamoDB Accelerator (DAX),
• Architecture of Amazon ElastiCache,
• Data Flow Example,
• Performance,
• Cost Model,
• Use Case Patterns (When to Choose DAX),
• Use Case Patterns (When to Choose ElastiCache),
• Decision Framework,
• Hybrid Approach (architecture using both),
• Sample of Hybrid Flow,
• Architecture Flow diagram for DynamoDB Accelerator (DAX) &  ElastiCache.

Intro:

• twtech is looking at their variation from the stand-point of:
•        Architecture,
•        Performance models,
•        Use cases,
•        Integration patterns,
•        Operational differences.

1. The Core Difference

Feature	DynamoDB Accelerator (DAX)	Amazon ElastiCache
Purpose.	Fully managed in-memory read cache specifically for DynamoDB.	Fully managed in-memory caching service for any application/data source
Supported Engines.	Proprietary DAX engine, DynamoDB API-compatible.	Redis, Memcached
Integration.	API-level integration — drop-in replacement for DynamoDB SDK calls.	App-level integration — twtech controls data model, serialization, and cache logic
Scope.	Narrow, DynamoDB-only.	Broad, can cache anything (DB queries, objects, sessions, API results)

2. Architecture of DynamoDB Accelerator (DAX)

• API-Aware: Acts as a managed DynamoDB client proxy.
• Write-through cache: Automatically updates cache on writes via DynamoDB SDK integration.
• Strongly Integrated: No need to manage cache key naming, TTLs manually — DAX manages them internally.
• Consistency:
• Eventually consistent reads by default.
• Can do transactional read-through for strongly consistent reads (slower).
• Latency: Microseconds (typical ~10s of μs), vs. DynamoDB's single-digit ms.
• Scaling: Multi-node clusters (up to 10) with read replicas for horizontal scale.
• Failures: Failover handled by DAX; clients reconnect to healthy node.

Data Flow Example:

1. App calls GetItem() via AWS SDK (DAX-enabled client).
2. DAX checks memory cache.
3. If miss, fetch from DynamoDB, populate cache, return result.

Architecture of Amazon ElastiCache

• Database-Agnostic: Can front any datastore (RDS, Aurora, S3, DynamoDB, APIs, ML inference results).
• Engines:
• Redis: Persistent store option, pub/sub, streams, Lua scripting.
• Memcached: Simple, fast, volatile key/value cache.
• Write Strategy:
• Write-through: Application writes to cache and DB.
• Lazy-loading: Cache misses trigger DB fetch + cache populate.
• Manual eviction or TTL-based expiration.
• Consistency: Determined by twtech application design (no automatic sync).
• Scaling:
• Redis: Sharded clusters, replicas for HA.
• Memcached: Horizontal scaling with client-side partitioning.
• Persistence (Redis): Optional RDB snapshots or AOF logs.

Data Flow Example:

1. App computes a cache key (e.g., user:twtech-pat:profile).
2. Queries ElastiCache (Redis/Memcached).
3. On miss, fetch from source DB/API, set cache key with TTL.

3. Performance

Factor	DAX	ElastiCache
Typical Latency.	~10 μs.	~1 ms (network RTT)
Throughput.	Millions of requests/sec per cluster.	Millions of requests/sec per cluster
Warm-up.	Starts empty, but fills on demand.	Starts empty, but TTL & eviction control is by wtech
Network Overhead.	Low (integrated with SDK).	Slightly higher due to manual client integration

4. Operational Model

Area	DAX	ElastiCache
Management.	AWS fully manages replication, failover, node replacement.	AWS manages infra, but you design cluster topology, replication, sharding
Versioning.	AWS upgrades engine automatically.	twtech chooses Redis/Memcached version upgrades
Monitoring.	CloudWatch metrics for latency, hit rate, errors.	CloudWatch metrics for CPU, memory, ops/sec, eviction count
Security.	VPC-only, IAM auth.	VPC-only, Redis AUTH/TLS, SGs

5. Cost Model

• DAX: Pay per node-hour + data transfer (in/out of VPC), no separate licensing.
• ElastiCache: Pay per node-hour (by instance type), replication factor affects cost, plus data transfer.

NB:

•  DAX reduces DynamoDB RCU costs directly, 
•  ElastiCache reduces load on any backend.

6. Use Case Patterns (When to Choose DAX)

• twtech has high read traffic to DynamoDB (e.g., leaderboards, session lookups).
• twtech needs drop-in caching without rewriting query logic.
• twtech wants automatic cache coherence with DynamoDB writes.
• twtech caches only DynamoDB data and want minimal operational overhead.

Example: 

• Gaming leaderboard reads, IoT device config lookups, e-commerce product catalog.

Use Case Patterns (When to Choose ElastiCache)

• twtech wants caching for multiple sources (DynamoDB + RDS + APIs).
• twtech needs complex caching patterns (e.g., sorted sets, pub/sub, geospatial queries).
• twtech wants full control over TTL, eviction, and cache keys.
• twtech may need persistent cache (Redis snapshot/AOF).

Example: 

• API gateway result caching, recommendation precomputation, multi-DB caching layer.

7. Decision Framework

Question	Go DAX	Go ElastiCache
Only DynamoDB caching needed?	✅	❌
Need cache to work with multiple backends?	❌	✅
Want zero cache-key logic in app?	✅	❌
Need advanced data structures (sets, sorted sets, pub/sub)?	❌	✅
Need persistence beyond TTL?	❌	✅
Optimize DynamoDB RCU costs directly?	✅	❌

8. Hybrid Approach architecture using both:

• DAX for instant DynamoDB query caching.
• ElastiCache (Redis) for cross-service aggregation or precomputed datasets.

Sample Hybrid Flow:

1. API calls check Redis for aggregated dashboard.
2. Redis aggregation jobs fetch raw data from DynamoDB (via DAX).
3. twtechUsers get ultra-fast responses from Redis.

Architecture flow diagram for DynamoDB Accelerator (DAX) &  ElastiCache:

Foot-Notes for the above flow diagram:

       Left Architecture flow (DAX):

     1.     App uses AWS SDK with DAX client.
2.     DAX automatically checks its in-memory cache.
3.     On miss, fetches from DynamoDB, stores in cache, returns result.
4.     On hit, returns instantly from cache.

      Right Architecture flow (ElastiCache):

     1.     App explicitly queries Redis/Memcached for a key.
2.     On hit, returns from cache.
3.     On miss, app fetches from backend DB/API, stores in cache with TTL, then returns result.