DynamoDB anti-patterns¶

Anti-patterns are common practices that seem reasonable but lead to poor performance, high costs, or scalability issues. Understanding what NOT to do is just as important as knowing best practices.

⚠️ warning about consequences¶

Anti-patterns can have serious impacts on your application:

• Performance Degradation: Response times can increase from milliseconds to seconds
• Cost Explosion: Operations can consume 10-100x more capacity than necessary
• Throttling: Your application may experience frequent throttling and errors
• Scalability Limits: You may hit hard limits that prevent scaling
• Data Integrity Issues: Race conditions and inconsistent data

The good news: most anti-patterns are easy to fix once you recognize them. This library includes tools to detect anti-patterns automatically.

Common anti-patterns¶

table scans¶

Using Scan operations when Query operations would work. Scans examine every item in your table, consuming massive amounts of capacity and time.

Impact: 100x slower and more expensive than queries on large tables.

hot partitions¶

Concentrating traffic on a single partition key value. DynamoDB partitions have throughput limits, and hot partitions cause throttling.

Impact: Throttling, high latency, and inability to scale beyond single partition limits.

string concatenation for keys¶

Manually concatenating strings to create composite keys instead of using helper functions. This leads to parsing errors and inconsistent formats.

Impact: Bugs, data corruption, and difficult-to-debug issues.

read-before-write¶

Reading an item before updating it when conditional writes would work. This doubles your capacity consumption and introduces race conditions.

Impact: 2x capacity consumption, race conditions, and data integrity issues.

missing projections¶

Retrieving entire items when you only need a few attributes. This wastes capacity and bandwidth.

Impact: 50-90% wasted capacity depending on item size.

inefficient batching¶

Making individual requests when batch operations would work, or not handling batch operation limits properly.

Impact: 10x slower throughput and higher latency.

How to use this section¶

Each anti-pattern page includes:

• What It Is: Clear explanation of the problematic practice
• Why It's Problematic: Detailed consequences and impacts
• Visual Diagrams: Illustrations showing the problem
• Problem Code: Examples of the anti-pattern in action
• Solution Code: The correct approach to use instead
• Impact Metrics: Real-world performance and cost implications
• Detection: How to identify if you're doing this

Detecting anti-patterns¶

The @ddb-lib/stats package includes an anti-pattern detector that analyzes your usage patterns:

import { StatsCollector, AntiPatternDetector } from '@ddb-lib/stats'

const stats = new StatsCollector()
const detector = new AntiPatternDetector(stats)

// Analyze your operations
const issues = detector.detectAll()

for (const issue of issues) {
  console.log(`⚠️  ${issue.type}: ${issue.message}`)
  console.log(`   Severity: ${issue.severity}`)
  console.log(`   Impact: ${issue.impact}`)
  console.log(`   Recommendation: ${issue.recommendation}`)
}

The detector can identify:

• Excessive scan operations
• Hot partition keys
• Missing projection expressions
• Inefficient batching patterns
• Read-before-write patterns

Priority guide¶

If you're experiencing issues, address anti-patterns in this order:

Critical (fix immediately)¶

1. Table Scans - Massive performance and cost impact
2. Hot Partitions - Causes throttling and prevents scaling

High priority (fix soon)¶

1. Read-Before-Write - Data integrity and performance issues
2. Missing Projections - Significant cost waste

Medium priority (optimize when possible)¶

1. Inefficient Batching - Performance optimization
2. String Concatenation - Code quality and maintainability

Prevention¶

The best way to avoid anti-patterns is to:

1. Use the Library: Helper functions prevent common mistakes
2. Monitor Operations: Use the stats collector to track patterns
3. Review Regularly: Run the anti-pattern detector periodically
4. Follow Best Practices: See Best Practices section
5. Learn Patterns: Study Patterns for correct approaches

Real-world impact¶

Here's what fixing anti-patterns can achieve:

These are real improvements from fixing common anti-patterns in production systems.

Related resources¶

• Best Practices - What you should do instead
• Patterns - Proven solutions to common problems
• Guides - Detailed usage guides
• Monitoring Guide - Track and analyze your operations

Getting help¶

If you're unsure whether you're following an anti-pattern:

1. Run the anti-pattern detector on your operations
2. Review the specific anti-pattern pages in this section
3. Check the Best Practices for correct approaches
4. Look at Examples for working code

Remember: recognizing and fixing anti-patterns is one of the fastest ways to improve your DynamoDB application's performance and reduce costs.