AWS DynamoDB Best Practices for High Performance

Amazon DynamoDB is one of the most widely used NoSQL databases in the cloud, known for its high speed, automatic scaling, and serverless architecture. While DynamoDB can handle billions of records and thousands of requests per second, achieving consistent high performance requires thoughtful table design, access pattern planning, and usage of the right optimization features.

This guide covers the essential best practices to help you build efficient, scalable, and high-performing applications using DynamoDB.

Why Focus on DynamoDB Performance?

By default, DynamoDB is designed for high throughput and low latency, but poor design choices can result in:

• Hot partitions that slow down performance

• Increased read and write costs

• Unnecessary scans that degrade speed

• Data modeling challenges over time

Implementing the right performance strategies early helps maintain predictable latency, control cost, and support exponential growth in workloads.

1. Design Tables Based on Access Patterns

Unlike relational databases, DynamoDB requires access pattern–driven design. You should know how your application will query data before designing your tables.

Key principles:

• Identify all read and write patterns upfront

• Avoid ad-hoc or dynamic queries

• Use denormalization to store data in the format needed by your queries

• Model with Single-Table Design when possible for related entities

Trying to apply relational design rules in DynamoDB often leads to poor performance and unnecessary scans.

2. Choose the Right Partition Key

The partition key greatly impacts performance because DynamoDB distributes data across partitions based on its value.

A good partition key is:

• High-cardinality (many possible unique values)

• Evenly distributed (reduces “hot keys”)

• Frequently accessed through queries

Examples of poor partition keys:

• Boolean fields (yes/no)

• Country (too few values)

• Status (active/inactive)

If one partition receives significantly more traffic than others, it becomes a hot partition and causes throttling.

3. Use Sort Keys for Query Flexibility

Adding a sort key allows you to retrieve multiple items from the same partition but in a sorted manner.

Sort keys are useful for:

• Time-based queries (events, logs, messages)

• Versioning of data

• Filtering by ranges (>, <, begins_with, between)

Example use case:

Partition Key: UserID
Sort Key: OrderDate

This allows you to query all orders of a user in date order efficiently.

4. Avoid Full Table Scans

Scans read every item in the table and are expensive and slow. Use Query operations instead wherever possible.

If a scan is unavoidable:

• Use filters to reduce returned items

• Use parallel scans carefully

• Add indexes to support targeted queries

Limit scans to batch analytics or background jobs, not user-facing requests.

5. Use Secondary Indexes Wisely

Secondary indexes enable alternative query patterns without duplicating tables.

Types:

Best practices:

• Limit the number of GSIs to necessary use cases

• Keep indexes lean because each GSI consumes read/write capacity

• Monitor index utilization to avoid overprovisioning

Remember that GSIs replicate data, meaning more storage and cost.

6. Optimize Read and Write Capacity

DynamoDB supports two capacity modes:

Performance tip:

• Use Auto Scaling with provisioned mode for cost-efficient scaling

• Use on-demand for new workloads until traffic patterns are known

To avoid throttling:

• Distribute traffic evenly across keys

• Use exponential backoff and retries for throttled requests

• Batch operations when possible

7. Implement Caching with DynamoDB Accelerator (DAX)

DAX is a fully managed in-memory cache for DynamoDB that can reduce read latency from milliseconds to microseconds.

Ideal use cases:

• High read-intensive workloads

• Repetitive read queries that don’t require frequent updates

• Gaming, e-commerce product catalog, social applications

DAX improves performance by offloading reads, but should not be used for write-heavy workloads requiring strong consistency.

8. Minimize Item Size

DynamoDB charges and processes data based on item size. Keeping items small improves performance and reduces cost.

Optimization tips:

• Store only essential data

• Use compression for large attribute values

• Offload large objects (images, blobs, logs) to S3

For large documents, store a reference (e.g., S3 URL) instead of storing the data directly in DynamoDB.

9. Use Single-Table Design for Related Entities

Single-table design stores multiple entity types in one table using structured keys. This enables fast relational-like queries without joins.

Example:

Instead of separate tables for Users, Orders, Addresses, store all in one table with appropriate PK and SK patterns.

Benefits:

• Fewer queries per request

• Reduced index overhead

• Better performance due to co-located data

This design requires careful planning but is highly efficient at scale.

10. Use TTL to Delete Expired Data Automatically

Time to Live (TTL) automatically removes expired items to reduce storage usage and improve performance.

Use cases:

• Session tokens

• Temporary cache data

• Expiring events or logs

TTL ensures tables remain lean, improving read and write performance.

11. Monitor and Tune Performance

Use CloudWatch, DynamoDB metrics, and Performance Insights to monitor:

• ThrottledRequests

• SuccessfulRequestLatency

• Read/Write Capacity Utilization

• Hot partitions

• GSI performance

Set alerts on abnormal patterns and scale accordingly.

12. Leverage Streams for Event-Driven Processing

DynamoDB Streams capture item changes for asynchronous processing.

Uses:

• Audit logs

• Real-time analytics

• Cross-region replication

• Trigger Lambda for workflows

This offloads compute from the main application and improves responsiveness.

13. Salt Keys to Prevent Hot Partitions

If a partition key is predictable or uneven, add a random suffix or prefix.

Example:

Instead of key:
user_123

Use:
user_123_1, user_123_2, user_123_3

This distributes load across multiple keys to prevent hotspots.

Conclusion

DynamoDB provides exceptional performance when tables are designed with the right access patterns, indexing strategies, and scaling practices. The key to high performance lies in understanding how DynamoDB partitions, reads, and writes data. By planning for growth, minimizing scans, using indexes sparingly, caching frequently accessed data, and monitoring performance metrics, you can build applications that scale seamlessly to millions of users.

Applying these best practices ensures predictable low latency, reduced costs, and high operational efficiency. With deliberate design and optimization, DynamoDB can power some of the most demanding real-time workloads in the cloud.