A company is currently employing DynamoDB for storing data related to tweets.
The number of rows is running in billions.
Which of the following are good practises when it comes to querying the data? Choose 2 answers from the options given below.
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - A and D.
The AWS Documentation mentions the following.
Because a Scan operation reads an entire page (by default, 1 MB), you can reduce the impact of the scan operation by setting a smaller page size.
The Scan operation provides a Limit parameter that you can use to set the page size for your request.
Each Query or Scanrequest that has a smaller page size uses fewer read operations and creates a "pause" between each request.
If possible, you should avoid using a Scan operation on a large table or index with a filter that removes many results.
Also, as a table or index grows, the Scan operation slows.
The Scan operation examines every item for the requested values and can use up the provisioned throughput for a large table or index in a single operation.
For faster response times, design your tables and indexes so that your applications can use Queryinstead of Scan.
(For tables, you can also consider using the GetItem and BatchGetItem APIs.)
The other options are incorrect as these would slow down the query performance.
For more information on the best practices for DynamoDB, please visit the url.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-query-scan.htmlWhen dealing with large amounts of data in DynamoDB, it is important to optimize your queries to avoid performance issues and high costs. Here are two good practices to consider when querying billions of rows of tweet data in DynamoDB:
Try to Query based on Indexes: When querying a large dataset in DynamoDB, it is best to use the Query operation instead of the Scan operation whenever possible. The Query operation is more efficient because it retrieves only the data that matches a specified partition key value or range key value (if the table has a composite primary key). The Scan operation, on the other hand, reads every item in the table and filters out any that don't match the specified criteria, which can be very expensive and slow for a large dataset. In order to perform efficient queries, it is important to create indexes on the table that can be used to retrieve specific subsets of data. Indexes can be created on one or more attributes of the table to enable more efficient queries that only read a subset of the data. So, it is a good practice to create indexes and use them for querying data whenever possible.
Try to use the Scan operation with certain projections: Sometimes it is not possible to use Query operations for certain types of queries. In such cases, the Scan operation is used. However, using the Scan operation can be expensive and slow for large datasets. So, in order to optimize the Scan operation, it is recommended to use certain projections. A projection specifies the attributes that should be returned in the query result, reducing the amount of data that needs to be read from the table. This can significantly improve query performance and reduce costs. For example, if the application only needs a few specific attributes from the table, the Scan operation can be used with the ProjectionExpression parameter to return only those attributes. This can help to reduce the amount of data that needs to be scanned and improve query performance.
Options A and B are not good practices for querying a large dataset in DynamoDB. Setting a smaller page size for the Scan operation (Option A) may result in more requests being sent to DynamoDB, which can increase latency and costs. Using the Scan operation with filter expressions (Option B) may read all of the data in the table, which can be very expensive and slow for a large dataset.
Option C may be a good practice in some cases, but it is not as important as the other two options for optimizing queries in DynamoDB.