![]() You should test and experiment to find the right balance between considerations such as query frequency, complexity, and criticality when deciding which distribution style and which distribution keys to use. The following distribution style guidelines are not hard-and-fast rules but rather a good place to begin with optimizations. For more information, see Defining constraints in the Amazon Redshift Database Developer Guide. This duplication violates the primary key constraint. ![]() For example, if you load the same file twice with the copy command, Amazon Redshift does not enforce primary keys and will duplicate the rows in the table. To avoid unexpected query results, you should ensure that the data being loaded does not violate foreign key constraints and that primary key uniqueness is maintained by enforcing no duplicate inserts. Generally, the query optimizer detects redundant joins without constraints defined if you keep statistics up to date by running the ANALYZE command as described later in this article. In certain circumstances, Amazon Redshift uses this information to optimize the query by eliminating redundant joins. Even though Amazon Redshift does not currently enforce these relationships, the query optimizer uses them as a hint when it analyzes a query. When you move your data model to Amazon Redshift, you can declare your primary and foreign key relationships. The following sections explain how to apply these optimizations in the context of a star schema. You also have a number of optimization options under your control that affect query performance whether you are using a star schema or another data model. Optimizations for Star SchemasĪmazon Redshift automatically detects star schema data structures and has built-in optimizations for efficiently querying this data. Most customers experience significantly better performance when migrating their existing data models to Amazon Redshift largely unchanged, though you should test for performance using either the actual or a representative dataset to ensure that your data model design and query patterns perform well before putting the workload into production. You should start from the assumption that your existing data model design will just work on Amazon Redshift. The Amazon Redshift design accommodates all types of data models, including 3NF, denormalized tables, and star and snowflake schemas. For more information, see star schema and snowflake schema. For example, a product dimension may have the brand in a separate table. Snowflake schemas extend the concept by further normalizing the dimensions into multiple tables. The fact table has foreign key relationships to one or more dimension tables that contain descriptive attribute information for the sold item, such as customer or product. Star schemas are organized around a central fact table that contains measurements for a specific event, such as a sold item. ![]() ![]() Such solutions typically have tooling that depends upon a star schema design. Many business intelligence solutions use a star schema or a normalized variation called a snowflake schema. To get in depth knowledge on AWS you can enroll for free live demo AWS Online Course For a detailed architecture overview of the Amazon Redshift service and optimization techniques, see the Amazon Redshift system overview. At the same time, Amazon Redshift minimizes operational overhead by freeing you from the hassle associated with provisioning, patching, backing up, restoring, monitoring, securing, and scaling a data warehouse cluster. Amazon Redshift uses many techniques to achieve fast query performance at scale, including multi-node parallel operations, hardware optimization, network optimization, and data compression. Amazon Redshift offers you fast query performance when analyzing virtually any size dataset using the same business intelligence applications you use today. We’ll talk about considerations for migrating data, when to use distribution styles and sort keys, various ways to optimize Amazon Redshift performance with star schemas, and how to identify and fix performance bottlenecks.Īmazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Many of these techniques will also work with other schema designs. This article explains how to optimize performance for an Amazon Redshift data warehouse that uses a star schema design.
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