DynamoDB Data Distribution for Tweets

Achieving Uniform Data Distribution in DynamoDB

Question

A company is planning on using DynamoDB for storing all data related to tweets.

The data will go into millions of rows and needs to scale based on demand.

The design team needs to ensure that the objects inserted into the DynamoDB tables are uniformly distributed via the partitions created in DynamoDB.

Which of the following can help achieve this? Choose 2 answers from the options given below.

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

Answer - C and D.

The AWS Documentation mentions the following as strategies for better partitioning of keys in DynamoDB.

Options A and B are incorrect since these are not used for equal distribution of objects in a DynamoDB table.

Sharding Using Random Suffixes.

One strategy for distributing loads more evenly across a partition key space is to add a random number to the end of the partition key values.

Then you randomize the writes across the larger space.

Sharding Using Calculated Suffixes.

A randomizing strategy can greatly improve write throughput.

But it's difficult to read a specific item because you don't know which suffix value was used when writing the item.

To make it easier to read individual items, you can use a different strategy.

Instead of using a random number to distribute the items among partitions, use a number that you can calculate based upon something that you want to query on.

For more information on key sharding in DynamoDB, please refer to the below URL.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-sharding.html

To ensure uniform distribution of data via partitions in DynamoDB, the following two techniques can be used:

C. Use Random Suffixes as a sharding technique: This technique involves adding a random suffix to the partition key value to ensure even distribution of data across partitions. By doing so, items with the same partition key are distributed evenly across partitions. This technique helps to avoid creating "hot" partitions where a large amount of data is stored in a single partition, leading to performance degradation. This technique can be implemented programmatically by generating a random suffix using a hash function and appending it to the partition key.

D. Use Calculated Suffixes as a sharding technique: This technique involves adding a calculated suffix to the partition key value to ensure even distribution of data across partitions. By doing so, items with the same partition key are distributed evenly across partitions. This technique can be implemented programmatically by calculating the hash value of the partition key and using the modulo function to determine the partition where the item should be stored. This technique helps to avoid creating "hot" partitions where a large amount of data is stored in a single partition, leading to performance degradation.

A. Place a higher read capacity for the tables: This option is incorrect as it does not have any direct impact on the distribution of data across partitions. Increasing the read capacity only increases the number of read operations that can be performed in parallel.

B. Ensure to choose a sort key when creating the table: This option is incorrect as it is related to sorting the data within the partition and does not have any direct impact on the distribution of data across partitions.

In summary, to achieve uniform distribution of data via partitions in DynamoDB, it is recommended to use sharding techniques such as random suffixes or calculated suffixes.