Is your application suffering from throttled or even rejected requests from DynamoDB? Even if you are not consuming all the provisioned read or write throughput of your table? You've run into a common pitfall! Read on to learn how Hellen debugged and fixed the same issue.

Hellen is working on her first serverless application: a TODO list. She uses DynamoDB to store information about users, tasks, and events for analytics.

Problem

Today users of Hellen’s TODO application started complaining: requests were getting slower and slower and sometimes even a cryptic error message ProvisionedThroughputExceededException appeared.

First Hellen checks the CloudWatch metrics showing the provisioned and consumed read and write throughput of her DynamoDB tables. Everything seems to be fine. The consumed throughput is far below the provisioned throughput for all tables as shown in the following figure.

Hellen is at lost. What is wrong with her DynamoDB tables? She starts researching for possible causes for her problem. Hellen finds detailed information about the partition behavior of DynamoDB.

Her DynamoDB tables do consist of multiple partitions. The number of partitions per table depends on the provisioned throughput and the amount of used storage.

MAX( (Provisioned Read Throughput / 3,000), (Provisioned Write Throughput / 1,000), (Used Storage / 10 GB))

Details of Hellen’s table storing analytics data:

Provisioned Read Throughput: 2,000 Units
Provisioned Write Throughput: 3,000 Units
Used Storage: 2 GB
========================================
Number of Partitions: 3

Provisioned throughput gets evenly distributed among all shards. Therefore the TODO application can write with a maximum of 1000 Write Capacity Units per second to a single partition.

Hellen is looking at the CloudWatch metrics again. The consumed write capacity seems to be limited to 1,000 units. Exactly the maximum write capacity per partition.

Time to have a look at the data structure. Hellen is revising the data structure and DynamoDB table definition of the analytics table.

Hellen uses the Date attribute of each analytics event as the partition key for the table and the Timestamp attribute as range key as shown in the following example.

{
  "Date": "2016-11-21", <- Partition Key
  "Timestamp": 1479718722848, <- Range Key
  "EventType": "TASK_CREATED",
  "UserId": "UUID"
}

Solution

Now Hellen sees the light: As she uses the Date as the partition key, all write requests hit the same partition during a day. So the maximum write throughput of her application is around 1000 units per second.

Hellen changes the partition key for the table storing analytics data as follows.

{
  "Date": "2016-11-21", 
  "Timestamp": 1479718722848, <- Range Key
  "EventType": "TASK_CREATED",
  "UserId": "UUID" <- Partition Key
}

She uses the UserId attribute as the partition key and Timestamp as the range key. Writes to the analytics table are now distributed on different partitions based on the user. The application makes use of the full provisioned write throughput now.

Hellen opens the CloudWatch metrics again. The write throughput is now exceeding the mark of 1000 units and is able to use the whole provisioned throughput of 3000 units.

Problem solved, Hellen is happy! No more complaints from the users of the TODO list.

Think twice when designing your data structure and especially when defining the partition key: Guidelines for Working with Tables. To get the most out of DynamoDB read and write request should be distributed among different partition keys. Otherwise, a hot partition will limit the maximum utilization rate of your DynamoDB table.