Resolving DynamoDB Hot Partition Throttling Using DAX
A gaming company uses Amazon DynamoDB as the database for a leaderboard feature. The table has a partition key of 'game_id' and a sort key of 'score'. The table uses on-demand capacity mode. During a new game launch, write traffic to a few popular game_id partitions becomes very hot, causing throttling. The company wants to improve performance for these hot partitions without changing the application code. What should they do?
Quick Answer
The answer is to enable DynamoDB Accelerator (DAX) for the table, which caches frequently accessed data to reduce read and write load on hot partitions. This resolves DynamoDB hot partition throttling without code changes by offloading repetitive requests to an in-memory cache, allowing the base table to handle the remaining traffic without exceeding partition throughput limits. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of caching strategies for on-demand capacity tables, where increasing capacity units is not applicable and adding indexes does not alleviate partition-level throttling. A common trap is to suggest adding a secondary index or using SQS, but these either fail to reduce base-table load or introduce latency and application changes. Memory tip: DAX = Dump the heat on a cache, not the partition.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement an Amazon SQS queue to buffer write requests.
Amazon SQS can buffer write requests to DynamoDB, smoothing out traffic spikes and reducing throttling on hot partitions. The SQS queue decouples the application from DynamoDB, allowing writes to be processed at a manageable pace. Option A is incorrect because adding a GSI does not reduce write load on the base table's partitions. Option B is incorrect because DAX is a read cache and does not help with write throttling. Option C is incorrect because switching to provisioned capacity does not resolve per-partition throttling limits; hot partitions still have a maximum throughput regardless of table-level capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a global secondary index with a different partition key.
Why it's wrong here
Adding a GSI does not reduce write load on the base table's partitions; it only provides an alternative query path.
- ✗
Enable DynamoDB Accelerator (DAX) for the table to cache frequently accessed data.
Why it's wrong here
DAX is an in-memory cache for reads only and does not alleviate write throttling on hot partitions.
- ✗
Switch to provisioned capacity and increase read/write capacity units for the table.
Why it's wrong here
Switching to provisioned capacity does not bypass per-partition throughput limits; hot partitions will still throttle if they exceed the partition's maximum capacity.
- ✓
Implement an Amazon SQS queue to buffer write requests.
Why this is correct
Amazon SQS buffers write requests, allowing them to be processed at a steady rate, reducing the likelihood of throttling on hot partitions.
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Same concept, more angles
1 more way this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Amazon DynamoDB as its primary database. The operations team is seeing increased read latency during peak hours. The table has a provisioned read capacity of 1000 RCU, but CloudWatch metrics show that consumed read capacity frequently reaches 1000 RCU. The application uses eventually consistent reads. What is the MOST cost-effective way to reduce read latency?
medium- A.Switch to strongly consistent reads to improve consistency.
- ✓ B.Enable DynamoDB Accelerator (DAX) to cache frequently read items.
- C.Create a global secondary index (GSI) on the table to offload reads.
- D.Increase the provisioned read capacity to 2000 RCU.
- E.Use Amazon ElastiCache for Memcached as a read cache.
Why B: DynamoDB Accelerator (DAX) provides an in-memory cache that reduces read latency by serving frequently accessed items from cache, without increasing provisioned RCU. This is cost-effective as it adds minimal cost compared to increasing capacity. Option A is wrong because switching to strongly consistent reads consumes double the RCU (since they require a read of the primary replica), increasing cost and potentially worsening latency. Option C is wrong because a Global Secondary Index (GSI) offloads reads from the base table but does not reduce latency for reads on the base table itself; it also incurs additional write costs. Option D is wrong because increasing RCU to 2000 would double the cost, though it might reduce throttling, it's not the most cost-effective. Option E is wrong because using ElastiCache adds operational complexity and cost, and DAX is a more seamless, DynamoDB-native caching solution.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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