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Zorluk: ZorHigh-Performing Database Solutions

A sports media company is building a real-time leaderboard and voting system for a global event. The application needs to handle millions of concurrent users. The system must support sub-millisecond latency for retrieving leaderboard data, up to 50,00050,000 write operations per second during peak voting windows, and the ability to scale capacity dynamically to handle spiky workloads. The system must also mitigate write bottlenecks for highly popular candidates. Which TWO strategies should a solutions architect implement to design a high-performing database architecture? (Select TWO.)

  1. A
    Configure the primary database table in Amazon DynamoDB using a monotonically increasing timestamp as the partition key to ensure chronological ordering of write operations.
  2. Deploy Amazon DynamoDB Accelerator (DAX) in front of the database table to cache read requests and offload query volume.Cevap
  3. C
    Deploy an Amazon Aurora MySQL database cluster and configure secondary Aurora Replicas to dynamically scale write capacity during peak voting windows.
  4. Implement write sharding by appending a random integer suffix to the candidate ID partition key when writing voting transactions to Amazon DynamoDB.Cevap
  5. E
    Configure the Amazon DynamoDB table in Provisioned Capacity Mode with static read and write capacity units to handle the sudden, highly irregular spikes in voting traffic.

Cevap

The correct strategies are to deploy Amazon DynamoDB Accelerator (DAX) to cache read requests and implement write sharding by appending a random integer suffix to the candidate ID partition key when writing voting transactions.
The correct strategies are implementing DynamoDB Accelerator (DAX) and write sharding. Caching with DAX offloads read traffic and provides microsecond-level latency, meeting the sub-millisecond retrieval requirement. Write sharding by appending a random suffix to the candidate ID distributes the voting writes across multiple partitions, preventing a write bottleneck on the popular candidate keys.

Adım Adım Çözüm

1
Analyze the performance requirements for reads.
Reads require sub-millisecond latency. Standard database queries typically operate in the millisecond range.
DynamoDB Accelerator (DAX) is selected because it provides microsecond latency caching for read-heavy workloads.
2
Analyze the write requirements and key distribution.
Writes peak at 50,00050,000 operations per second, targeting popular candidates. This will cause hot partitions if using a standard candidate ID as the partition key.
Write sharding (appending a random suffix to the partition key) is required to distribute the write load across multiple partitions.
3
Evaluate and eliminate incorrect database options.
Monotonically increasing keys cause hot partitions; Aurora replicas do not scale writes; static provisioned capacity cannot handle spiky traffic efficiently.
Eliminating these options ensures the architecture remains performant, cost-effective, and aligned with AWS best practices.

Anahtar Kavram

Mitigating hot partitions via write sharding and caching read-heavy tables using DAX to achieve low-latency, high-throughput scaling.
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High-Performing Database Solutions

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