Amazon Redshift is a popular and powerful data warehousing solution, known for its ability to handle large volumes of data and complex queries. One of the key features that can significantly impact query performance in Redshift is the choice of distribution style. In this blog post, we’ll dive deep into distribution styles in AWS Redshift, exploring their types, benefits, and best practices.

Understanding Distribution Styles

In Amazon Redshift, data distribution is the process of distributing data across compute nodes for parallel processing. Distribution styles determine how data is distributed, and Redshift offers three primary distribution styles: KEY, EVEN, and ALL.

1. KEY Distribution

In KEY distribution, data is distributed based on the values in one or more columns chosen as the distribution key. Each unique value in the distribution key determines which slice (compute node) the data goes to. Rows with the same distribution key value will always reside on the same slice. This style is useful for tables with a clear and frequently used join key.


  • Efficient for join-heavy queries.
  • Minimizes data movement during joins.
  • Reduces network overhead.


  • Uneven data distribution may lead to performance issues.
  • Choose a distribution key that evenly distributes data.

2. EVEN Distribution

In EVEN distribution, data is distributed evenly across all slices, regardless of the values in any specific column. This style is suitable for tables that aren’t frequently joined with other tables or when no clear distribution key exists.


  • Uniform data distribution.
  • Suitable for standalone tables or staging tables.


  • May lead to data shuffling during joins, affecting performance.
  • Use when joining is minimal or when data distribution doesn’t significantly impact query performance.

3. ALL Distribution

In ALL distribution, a full copy of the table is stored on each slice. This style is used for small dimension tables or lookup tables that are frequently used in join operations. ALL distribution minimizes data movement during joins but comes at the cost of storage redundancy.


  • Ideal for small lookup or dimension tables.
  • Minimal data movement during joins.


  • Increases storage usage for replicated data.
  • Should only be used for small tables.

Choosing the Right Distribution Style

Selecting the appropriate distribution style is crucial for optimizing query performance in Amazon Redshift. Consider these factors when making a choice:

  1. Query Patterns: Analyze how your data is queried. If you frequently join tables on specific columns, KEY distribution may be the best choice. If not, EVEN distribution or a mix of distribution styles might work.
  2. Data Size: Large fact tables may benefit from EVEN distribution to evenly distribute the workload. Smaller dimension tables may use ALL distribution for optimal join performance.
  3. Data Distribution: Review the cardinality and distribution of your data. If you choose KEY distribution, ensure the distribution key is evenly distributed.

Best Practices for Distribution Styles

To make the most of distribution styles in AWS Redshift, consider the following best practices:

  1. Analyze Query Performance: Regularly analyze query performance and adjust distribution styles as needed based on changing query patterns.
  2. Distribution Key Selection: Carefully choose the distribution key based on query patterns and even data distribution.
  3. Data Loading: When loading data into Redshift, use the COPY command’s ‘SORTKEY’ option to sort data based on the distribution key.
  4. Table Size Monitoring: Keep an eye on table sizes, especially if using ALL distribution, to manage storage costs.


Distribution styles play a pivotal role in optimizing query performance in Amazon Redshift. By understanding the nuances of KEY, EVEN, and ALL distribution styles and selecting the right one for your specific use cases, you can ensure that your data warehousing environment delivers the performance and scalability needed for your analytics workloads. Regular monitoring and adjustments are key to maintaining optimal query performance as your data and query patterns evolve.

AWS Redshift’s flexibility in distribution styles empowers data engineers to design data warehouses that meet the unique needs of their organizations, making it a top choice for handling large-scale data analytics workloads.

By Abhishek K.

Author is a Architect by profession. This blog is to share his experience and give back to the community what he learned throughout his career.