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. There is a DynamoDB table that uses a composite primary key with UserID as the partition key and MessageID as the sort key.
The data comes from different users as different files which is collected, processed will be uploaded to DynamoDB.
Each file is based on 1 partition key. The administrator observes that the load performance is too slow.
How can we improve performance of Distributing Write Activity Efficiently During Data Upload? select 1 option.
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option D is correct - Distribute your upload work by using the sort key to load one item from each partition key value, then another item from each partition key value, and so on.
This improves the Distributing Write Activity Efficiently During Data Upload.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-key-data-upload.htmlTo improve the performance of distributing write activity efficiently during data upload for KindleYou's DynamoDB table, we need to consider the distribution of data across partitions and the way we are uploading the data.
A DynamoDB table uses partition keys to partition data across different servers. Each partition key has a limit of 1,000 write capacity units (WCUs) and 3,000 read capacity units (RCUs). If the write load is not distributed evenly across partition keys, it can lead to hot partitions, where a few partitions receive a disproportionate amount of write traffic and become a bottleneck.
In this scenario, the DynamoDB table uses a composite primary key with UserID as the partition key and MessageID as the sort key. The data comes from different users as different files, which are collected, processed, and then uploaded to DynamoDB. Each file is based on one partition key.
Option A suggests segregating each user's data into different files and uploading them. This approach can improve the partition load performance because it ensures that each file is based on a unique partition key, and the write load is distributed evenly across partitions. However, this approach may not be practical because it requires creating and managing a large number of files.
Option B suggests collating each user's data into the same or multiple files and uploading them based on the partition key's order. This approach can also improve the partition load performance because it ensures that the write load is distributed evenly across partitions. However, it requires sorting the data by the partition key, which can be time-consuming for large datasets.
Option C suggests distributing the upload work by using the partition key to load one item from each partition key value, then another item from each partition key value, and so on. This approach can help distribute the write load evenly across partitions, but it may not be practical for large datasets with many partition keys.
Option D suggests distributing the upload work by using the sort key to load one item from each partition key value, then another item from each partition key value, and so on. This approach can be more efficient than Option C because it ensures that the write load is distributed evenly across partitions and also takes advantage of the sort key's order. However, this approach may not be practical if the sort key has a high cardinality or if there are many partition keys.
In summary, Option D is the most efficient approach for distributing write activity during data upload for KindleYou's DynamoDB table, but the best approach may depend on the specific characteristics of the data and the use case.