Amazon Redshift now leverages Bloom filters to improve data lake query performance by up to 2x
Amazon Redshift now leverages Bloom filters to enable early and effective data filtering for up to 2x faster query performance on external tables in Amazon S3. A Bloom filter is a probabilistic, memory-efficient data structure that accelerates join queries at scale by filtering rows that do not match the join relation, significantly reducing the amount of data transferred over the network. Amazon Redshift automatically determines what queries are suitable for leveraging Bloom filters at query runtime. You can power a lake house architecture with Amazon Redshift Spectrum to directly query and join data across your data warehouse and data lake, enabling you to gain unique insights not possible otherwise.
Amazon Redshift now leverages Bloom filters to enable early and effective data filtering for up to 2x faster query performance on external tables in Amazon S3. A Bloom filter is a probabilistic, memory-efficient data structure that accelerates join queries at scale by filtering rows that do not match the join relation, significantly reducing the amount of data transferred over the network. Amazon Redshift automatically determines what queries are suitable for leveraging Bloom filters at query runtime. You can power a lake house architecture with Amazon Redshift Spectrum to directly query and join data across your data warehouse and data lake, enabling you to gain unique insights not possible otherwise.
Amazon Redshift Bloom filters are enabled by default with release version 1.0.15503 or later in the AWS regions where Amazon Redshift Spectrum is available. For more information visit the documentation. Refer to the AWS Region Table for Amazon Redshift availability.