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 loading data into tables.
Select 4 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E. F. G.Answer : A, D, E, F.
Option A is correct - copy command to load multiple files is a best practice to load the data from S3, EMR and Others.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-copy.htmlOption B is incorrect -the optimal size of the files should be of equal size between 1 MB and 125 MB.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-multiple-files.htmlOption C is incorrect -the he number of files should be a multiple of the number of slices in your cluster.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-multiple-files.htmlOption D is correct - Manifest files address eventual consistency issues during loads.
https://docs.aws.amazon.com/redshift/latest/dg/best-practices-preventing-load-data-errors.htmlOption E is correct -Load your data in sort key order to avoid needing to vacuum.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-sort-key-order.htmlOption F is correct -load the data in sequential blocks according to sort order to eliminate the need to vacuum.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-load-data-in-sequential-blocks.htmlOption G is incorrect -the Use a staging table to perform a merge operations.
load your data into a staging table and then join the staging table with your target table for an UPDATE statement and an INSERT statement.
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.htmlSure, here's a detailed explanation of the best practices for loading data into tables:
A. Use copy command here to load multiple files from Amazon S3, Amazon EMR, Amazon DynamoDB, or multiple data sources on remote hosts
The COPY command is a fast and efficient way to load data into tables in Amazon Redshift. It can handle large amounts of data in parallel, and can load data from multiple sources including Amazon S3, Amazon EMR, Amazon DynamoDB, and remote hosts. The COPY command is optimized for loading data in bulk, so it's recommended to use it instead of INSERT statements for large data sets.
B. For optimal parallelism, split your load data files so that the files are about equal size, between 1 MB and 1 GB after compression
Splitting data files into equal sized chunks helps to achieve optimal parallelism when loading data into Redshift tables. Redshift distributes data across multiple slices, so splitting files into equal sized chunks ensures that data is evenly distributed across slices. The recommended file size range is between 1 MB and 1 GB after compression, as larger files can cause performance issues.
C. The number of files should be a multiple of the (number of slices+ X) in your cluster. X indicates the number of leader nodes
The number of files being loaded should be a multiple of the (number of slices + X) in the Redshift cluster. This helps to ensure that each slice is processing an equal amount of data, which improves performance. X represents the number of leader nodes in the cluster, which should also be taken into consideration when calculating the number of files.
D. Manage data consistency using a manifest file to load data and address eventual consistency issues
A manifest file is a JSON file that lists all the data files to be loaded into a Redshift table. It includes information about the data files, such as their location and format, and can be used to manage data consistency when loading data. Redshift can sometimes experience eventual consistency issues, where data changes may take some time to propagate across the cluster. Using a manifest file can help to address these issues by ensuring that all the data files are loaded correctly and in the right order.
E. In order to reduce the need for VACUUM, load data in the sort key order of the table
Redshift uses a sort key to sort data within a table, which helps to improve query performance. When loading data, it's recommended to load it in the same sort key order as the table, as this can help to reduce the need for VACUUM operations. VACUUM is a process that reclaims space and sorts data in tables, but it can be time-consuming and can impact performance if done frequently.
F. Load the data in sequential blocks according to sort order to eliminate the need to VACUUM
Loading data in sequential blocks according to the sort key order can also help to eliminate the need for VACUUM operations. By loading data in sequential blocks, the data can be loaded directly into the correct blocks within the table, without the need for sorting or reorganizing the data later.
G. Staging tables benefits inserts and updates but create performance issues when upserts are performed.
Staging tables are temporary tables used to load and manipulate data before it's loaded into a final table. They can be useful for managing data consistency and performing transformations on the data, but they can create performance issues when used for upserts. Upserts involve inserting new data and updating existing data in the table, which can be slower when using staging tables. It's recommended to use other methods, such as merge statements or temporary tables, for upserts instead.