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Difficulty: MediumResolving DynamoDB Throttling and Key Distribution Issues

A smart grid monitoring application named VoltGuard collects hourly utility consumption metrics from millions of smart meters and writes the records to an Amazon DynamoDB table. The table is configured with provisioned write capacity and uses the hour of the reading (formatted as `YYYY-MM-DD-HH`) as the partition key, and the smart meter ID as the sort key. During the first few minutes of every hour, the application experiences a massive spike in write requests, leading to frequent `ProvisionedThroughputExceededException` errors, while the overall consumed capacity remains well below the table's total provisioned limits.

What is the most effective way to resolve these write throttling errors?

  1. A
    Increase the table's provisioned Write Capacity Units (WCUs) to accommodate the peak hourly write traffic.
  2. B
    Configure a longer visibility timeout for the Amazon SQS queue that processes the incoming meter readings.
  3. Redesign the table schema to use the smart meter ID as the partition key and the timestamp of the reading as the sort key.Answer
  4. D
    Configure the AWS SDK client to use static credentials with administrator access to bypass DynamoDB write limitations.

Answer

Redesign the table schema to use the smart meter ID as the partition key and the timestamp of the reading as the sort key.
Redesigning the table schema to use the smart meter ID as the partition key and the timestamp of the reading as the sort key distributes the write load across millions of distinct partition keys. This takes full advantage of DynamoDB's internal hashing to spread write requests across multiple physical partitions, preventing any single partition from becoming a bottleneck and eliminating the ProvisionedThroughputExceededException errors.

Step-by-Step Solution

1
Analyze CloudWatch metrics and application logs showing ProvisionedThroughputExceededException.
Identify that total consumed write capacity is low compared to the table's provisioned limit, indicating a key distribution issue (hot partition) rather than overall capacity exhaustion.
To pinpoint whether the issue is database-wide capacity starvation or partition-level bottlenecking.
2
Examine the current table schema partition key design.
Observe that using the date and hour (YYYY-MM-DD-HH) as the partition key forces all concurrent writes from millions of smart meters to target the exact same partition key value within that hour.
To understand the root cause of the hot partition key issue.
3
Select a high-cardinality attribute for the partition key.
Migrate the schema to use the smart meter ID as the partition key and the reading timestamp as the sort key, ensuring writes are distributed across a large pool of unique partitions.
To leverage DynamoDB's automatic partitioning mechanism for scale and uniform load distribution.

Key Concept

Resolving DynamoDB throttling issues by redesigning the partition key schema to utilize high-cardinality attributes.
Estimated Time:1m 30s
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Topics

Resolving DynamoDB Throttling and Key Distribution Issues