Understanding DynamoDB Internals

Partitioning and Throughput Model

DynamoDB partitions data based on the partition key and distributes it across multiple nodes. Each partition is allocated a portion of the provisioned or on-demand throughput. Misbalanced partitions can lead to hot keys and throttling, even if global throughput isn't exhausted.

Consistency and Access Patterns

By default, reads are eventually consistent. Strong consistency must be explicitly requested and may increase latency. Query and Scan operations behave differently in terms of performance and cost, especially on large datasets.

Common Symptoms and Diagnoses

1. Throttling Despite Low Utilization

ProvisionedThroughputExceededException
Rate exceeded for table / index

Indicates that a partition is receiving more traffic than it can handle, leading to request throttling. A common sign of a hot partition.

2. High Latency on Queries

Symptoms include long response times or intermittent spikes in query durations. Often tied to:

  • Inefficient access patterns
  • Use of Scan instead of Query
  • Under-indexed queries (missing GSIs or LSIs)

3. Unexpected Empty Query Results

This can occur when:

  • Query key does not match the exact schema
  • Case sensitivity issues
  • Incorrect filter expression applied post-scan

4. Unpredictable Costs in On-Demand Mode

Caused by high read/write volume spikes or unbounded scans, leading to unplanned consumption charges.

Root Causes and Pitfalls

Hot Partitions

When a small subset of partition keys receives the majority of traffic, the corresponding partition becomes a bottleneck. This is common in time-series datasets using timestamps as partition keys.

Poor Indexing Strategy

Failure to define appropriate GSIs or relying on multiple filters instead of key conditions leads to full-table scans and degraded performance.

Large Item Size and Attribute Bloat

DynamoDB has a 400KB item size limit. Oversized attributes (e.g., large blobs, metadata) lead to slow reads and increased latency.

Overreliance on Scan

Scan reads every item in a table or index and is expensive both in cost and latency. It should be avoided in latency-sensitive applications.

Step-by-Step Troubleshooting

1. Identify Throttled Partitions

# Use CloudWatch metrics
Check: ThrottledRequests, ReadThrottleEvents, WriteThrottleEvents
Filter by PartitionId if available in logs

Correlate request timestamps with provisioned capacity to pinpoint hot keys.

2. Enable and Analyze DynamoDB Contributor Insights

# Via AWS Console or CLI
aws dynamodb enable-contributor-insights --table-name YourTable

This reveals the most frequently accessed keys, which can expose imbalance in traffic distribution.

3. Optimize Query Patterns

# Use KeyConditionExpression for indexed queries
response = table.query(
  KeyConditionExpression=Key('user_id').eq('1234')
)

Avoid Scan unless paginated and filtered properly. Use projection expressions to minimize payload size.

4. Analyze Item Size and Attributes

# Estimate item size locally
import sys, json
item = {"user_id": "1234", "data": "..."}
print(sys.getsizeof(json.dumps(item)))

Trim unused or redundant attributes. Offload large content to S3 with references in DynamoDB.

5. Review GSI Usage

Check that GSI partition/sort keys match the intended access pattern. Monitor GSI-specific CloudWatch metrics for throttling or underutilization.

Architectural Best Practices

  • Use composite keys and prefix strategies to distribute write load
  • Model data access patterns up front; avoid ad-hoc querying
  • Avoid Scan operations on large tables; use pagination with filters if necessary
  • Apply DynamoDB Auto Scaling or on-demand with budget alerts
  • Decouple large payloads via S3 and store metadata in DynamoDB

Conclusion

DynamoDB delivers performance at scale, but only when designed and operated with care. The root causes of latency and throttling often stem from misuse of partition keys, under-optimized indexes, and suboptimal access patterns. By applying targeted diagnostics and following architectural best practices, teams can maintain highly scalable, cost-effective, and performant NoSQL systems.

FAQs

1. Why am I seeing throttling when my table isn't at full capacity?

Throttling is likely due to a hot partition. Capacity is distributed per partition, not per table globally.

2. Should I always use on-demand capacity?

On-demand is ideal for unpredictable workloads, but can become expensive. Use provisioned capacity with auto-scaling for stable traffic.

3. How do I migrate from Scan to Query?

Redesign your schema to support key-based access using partition and sort keys. Introduce GSIs where necessary.

4. How do I monitor item size growth?

Enable DynamoDB Streams and inspect payload sizes. Alternatively, serialize and estimate item size during writes.

5. What tools help visualize access patterns?

DynamoDB Contributor Insights, CloudWatch Metrics, and the AWS NoSQL Workbench are valuable for visualizing and refining schema design.