MindPyramid Limited is a multinational information technology and outsourcing company headquartered in Vizag, India and New Jersey, USA.
Founded in 2003, the company employs approximately 2000 employees.
The company offers consulting services in cloud computing, big data and analytics.
They offer services to major cloud providers including AWS.
The team is working with one of the major clients having their infrastructure build on AWS.
Currently the client is having lot of performance issues and wants to understand the design best practices from MindPyramid team.
Please suggest the best practices in terms of designing tables.
select 2 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E. F. G.Answer : A, D.
Option A is correct - Amazon Redshift query optimizer uses sort order when it determines optimal query plans.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-sort-key.htmlOption B is incorrect -Amazon Redshift does not enforce unique, primary-key, and foreign-key constraints.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-defining-constraints.htmlOption C is incorrect - You can specify compression encodings when you create a table, but mostly, automatic compression produces the best results.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-auto-compression.htmlOption D is correct -the query optimizer redistributes the rows to the compute nodes as needed to perform any joins and aggregations through optimal distribution techniques.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-best-dist-key.htmlOption E is incorrect - Use the Smallest Possible Column Size
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-smallest-column-size.htmlOption F is incorrect -Use Date/Time Data Types for Date Columns
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-timestamp-date-columns.htmlOption G is incorrect -This is a best practice for loading data into tables, not for design.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-multiple-files.htmlSure, I'll be happy to explain the best practices for designing tables in AWS.
When designing tables in AWS, there are several best practices that can help optimize performance and ensure efficient use of resources. Here are some of the most important ones:
A. Choose the optimal sort key: The sort key determines the order in which data is stored and retrieved from a table. It's important to choose a sort key that is well-suited to the types of queries that will be performed on the data. For example, if the most common type of query involves retrieving the most recent data, a sort key based on a timestamp field would be appropriate. If frequent range filtering or equality filtering will be performed, the sort key should be based on the field being filtered on. And if joining of multiple tables is common, the sort keys should be based on the common columns between the tables.
B. Define constraints: Constraints help ensure the integrity and consistency of the data in a table. It's important to define constraints that enforce uniqueness, primary-key, and foreign-key constraints. Uniqueness constraints ensure that no two rows in a table have the same values for the specified columns. Primary-key constraints ensure that every row in the table has a unique identifier. Foreign-key constraints ensure that data in one table is related to data in another table, and that the relationship is maintained even if data is deleted or modified.
C. Understand and define compression encoding techniques: Compression encoding techniques can help reduce the amount of storage space required for tables, and can also improve query performance. It's important to understand the different compression encoding techniques available in AWS, such as Run Length Encoding (RLE), Dictionary Encoding, and Delta Encoding, and to choose the appropriate technique for the data being stored.
D. Define best distribution key: The distribution key determines how data is distributed across nodes in a cluster. It's important to choose a distribution key that is well-suited to the types of queries that will be performed on the data. For example, if the fact table and one dimension table have a common column, it's best to distribute them based on that column. Large dimensions with high cardinality in the filtered result set should also be distributed based on their common columns. And small dimension tables should be distributed uniformly.
E. Use appropriate column sizes: Using the maximum column size for convenience of design of large tables is not a best practice. In fact, it can lead to wasted storage space and slower query performance. It's important to choose appropriate column sizes based on the types of data being stored and the expected query patterns.
F. Use appropriate data types: Varchar and char data types should not be used more frequently than date data types. In fact, it's important to choose appropriate data types based on the types of data being stored and the expected query patterns. Varchar and char data types are appropriate for storing text data, while date data types are appropriate for storing date and time data.
G. Split data into multiple files: Splitting data into multiple files can help improve query performance by allowing parallel processing of the data. However, it's important to balance the number of files with the size of the data, and to ensure that the data is split logically based on the query patterns.
In summary, when designing tables in AWS, it's important to choose appropriate sort keys, define constraints, understand and define compression encoding techniques, choose appropriate distribution keys, use appropriate column sizes and data types, and split data into multiple files as necessary. These best practices can help optimize performance and ensure efficient use of resources.