Efficient Workload Distribution for DynamoDB Sharding
Question
KindleYou is a location-based social search mobile app that allows users to like or dislike other users, and allows users to chat if both parties liked each other in the app.
It has more than 1 billion customers across the world. They use DynamoDB to support the mobile application and S3 to host the images and other documents shared between users. DynamoDB has a table with 60 partitions and is being heavily accessed by users.
There are lots of hot partitions.
How can we address writing to different shards to distribute the workload efficiently to both read and write operations? select 2 options.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A, B.
Option A is correct -Through random suffixes.
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.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-sharding.htmlOption B is correct -Sharding Using Calculated Suffixes improves reads.
use a number that you can calculate based upon something that you want to query on.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-sharding.htmlOption C is incorrect - Through random suffixes.
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.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-sharding.htmlOption D is incorrect -Sharding Using Calculated Suffixes improves reads.
use a number that you can calculate based upon something that you want to query on.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-sharding.htmlIn DynamoDB, data is distributed across partitions based on the partition key. A hot partition is a partition that is accessed frequently by a large number of users, leading to increased latency and potential throughput issues.
To address the issue of hot partitions and distribute the workload more efficiently, two options can be considered:
- Distributing loads more evenly across a partition key space is to add a random number to the end of the partition key values to improve writes:
By adding a random number to the end of partition key values, the distribution of data across partitions can be improved. This technique is called "sharding" and involves splitting the data into smaller pieces to improve efficiency. By adding a random number, the distribution of data across partitions becomes more even, reducing the likelihood of hot partitions.
For example, if the partition key is "user_id", adding a random number to the end of the key value will distribute the data across partitions in a more even manner. So, instead of using "user_id" as the partition key, you can use "user_id + random_number" to distribute the data more evenly.
This technique is useful for improving write performance because it spreads the data more evenly across the partitions, reducing the number of hot partitions.
- Use a number that you can calculate based upon something that you want to query on to improve reads:
This technique involves selecting a partition key value that is based on the attribute you want to query. By doing this, you can distribute the data across partitions in a way that is optimized for the queries you want to perform.
For example, if you frequently query the data based on location, you can use the "location" attribute as the partition key. This way, the data will be distributed across partitions based on location, and querying the data based on location will be more efficient.
This technique is useful for improving read performance because it optimizes the distribution of data across partitions for the queries you want to perform.
In summary, to address the issue of hot partitions and distribute the workload more efficiently in DynamoDB, you can use sharding by adding a random number to the end of the partition key value to improve writes, and select a partition key value that is based on the attribute you want to query to improve reads.
