Amazon DynamoDB - AI-Optimized Technical Reference

Core Technology Profile

What it is: AWS-managed NoSQL database launched 2012, used internally by Amazon retail platform and Alexa
Performance: 1-5ms for simple key-value lookups, consistent at 100-100K requests/second
Management: Zero server administration - fully serverless
Primary Use Case: High-scale applications requiring fast key-value access with predictable performance

Critical Implementation Requirements

Schema Design (Non-Negotiable)

• Access patterns must be designed upfront - no retrospective indexing like PostgreSQL
• Partition key determines physical data distribution - poor choice causes hot partitions and throttling
• No JOINs supported - data must be denormalized or handled in application code
• 400KB item size limit - larger records require splitting or S3 storage

Query Limitations (Breaking Points)

• Key-value lookups only - complex WHERE clauses not supported
• No ad-hoc queries - unplanned query patterns require expensive full table scans
• PartiQL support limited - SQL-like syntax but constrained functionality
• Transactions limited to 100 items and cost more than regular operations

Performance Characteristics

When Fast (1-5ms)

• Simple key lookups using partition key
• Consistent performance regardless of table size
• Auto-scaling handles traffic spikes (with delay)

When Slow/Expensive

• Full table scans for unplanned queries
• Hot partition scenarios (all traffic hits single partition)
• Complex aggregations (not designed for analytics)

Throttling Triggers

• Exceeding provisioned capacity
• Hot partitions (even with unused overall capacity)
• Auto-scaling delay during sudden traffic spikes
• Error: ProvisionedThroughputExceededException

Pricing Model Analysis

On-Demand Pricing

• Rates: $0.25/million reads, $1.25/million writes (2024 pricing)
• Good for: Unpredictable workloads, small-medium applications ($5-50/month typical)
• Danger: Poor access patterns can generate $10K+ bills overnight
• Example failure: Full table scan in production = massive unexpected costs

Provisioned Capacity

• Savings: 60-80% cheaper than on-demand for predictable workloads
• Risk: Under-provision = throttling, over-provision = waste
• Reserved capacity: Up to 77% discount for 3-year commitments
• Migration cost example: One company paid $50K/year reserved but used only 20% after product pivot

Hidden Costs

• Global Secondary Indexes double storage costs
• Point-in-time recovery accumulates monthly
• Cross-region replication approximately doubles total costs
• DynamoDB Streams: $0.02 per 100K reads
• Standard-IA storage savings offset by access charges if accessed frequently

Global Distribution Reality

Multi-Region Setup

• Replication: Automatic through DynamoDB Streams
• Consistency: Eventually consistent (1-2 second delay typical)
• Conflict resolution: Last writer wins by timestamp
• Data loss risk: Simultaneous writes to same item can lose updates
• Real-world impact: E-commerce inventory updates lost during flash sales

Cost Impact

• Roughly doubles operational costs (storage + throughput per region)
• Cross-region bandwidth charges additional
• Each region maintains complete table copy

Migration Complexity Assessment

From SQL Databases

• Timeline: Budget weeks to months, not days
• Data model redesign required - cannot port SQL schema directly
• Query rewrite necessary - no direct SQL translation
• Learning curve: 2-3 months for experienced SQL developers
• Item size validation: Check for records >400KB before migration

Vendor Lock-in Severity

• AWS integration: Tight coupling with Lambda, API Gateway, Streams
• Migration difficulty: 8+ months observed for complex applications
• Architecture dependency: Application patterns become AWS-specific
• Cost example: One startup required additional funding round for migration costs

Decision Matrix

Use DynamoDB When

• Simple key-value access patterns
• Need zero database administration
• Predictable query patterns known upfront
• Require consistent low latency at scale
• AWS ecosystem integration valuable

Avoid DynamoDB When

• Need complex queries or JOINs
• Require ad-hoc reporting capabilities
• Data analysis is primary use case
• Schema evolution expected
• Multi-vendor portability required

Common Failure Scenarios

Hot Partition Anti-Pattern

• Cause: Using high-cardinality fields (userId) as partition key when few users generate most traffic
• Result: Throttling even with unused table capacity
• Solution: Compound partition keys or write sharding

Query Pattern Mismatch

• Cause: Treating DynamoDB like SQL database
• Result: Expensive scans, poor performance, high costs
• Example: Analytics queries burning $500 in single morning

Transaction Overuse

• Cause: Using transactions for operations exceeding 100 items
• Result: Significant cost increase
• Example: Feature increasing monthly bill from $200 to $800

Monitoring Requirements

Cost Control

• Set billing alerts at $100, $500, $1000 thresholds
• Monitor first few months closely after deployment
• Watch for scan operations vs. key lookups in CloudWatch

Performance Indicators

• Track throttling events
• Monitor hot partition metrics
• Measure actual vs. expected latency

Technology Comparison Context

vs. MongoDB Atlas

• DynamoDB advantage: Zero server management
• MongoDB advantage: Familiar query syntax, aggregation pipelines
• Performance: MongoDB ~10ms vs. DynamoDB 1-5ms for simple operations

vs. PostgreSQL

• DynamoDB advantage: Auto-scaling, consistent performance
• PostgreSQL advantage: Full SQL, complex queries, mature ecosystem
• Migration complexity: PostgreSQL to DynamoDB requires complete application rewrite

vs. Redis

• DynamoDB advantage: Persistent storage, automatic scaling
• Redis advantage: Sub-millisecond performance, rich data structures
• Reliability: DynamoDB more reliable for persistent data storage

Resource Requirements

Development Expertise

• NoSQL data modeling skills essential
• AWS ecosystem knowledge beneficial
• 2-3 month learning curve for SQL developers
• Access pattern design critical skill

Operational Investment

• Initial setup: Medium complexity
• Ongoing maintenance: Minimal (major advantage)
• Cost optimization: Ongoing requirement
• Monitoring setup: Essential for cost control