Since deletes and updates both flag the old data but don’t actually remove it, you must use vacuuming to reclaim the disk space that was occupied by table rows that were marked for deletion by the previous UPDATE and DELETE operations. It also affects the performance of queries. The vacuuming process in Amazon Redshift is essential for the health and maintenance of your Amazon Redshift cluster. Updating and deleting data creates dead rows that must be vacuumed, and even append-only tables must be resorted if the append order isn't consistent with the sort key. ![]() For more information, see Working with data distribution styles in the Amazon Redshift documentation.Īlthough Amazon Redshift provides industry-leading performance out of the box for most workloads, keeping Amazon Redshift clusters running well requires maintenance. We recommend that you use distribution keys to facilitate the most common joins. Choosing the right distribution style for a table helps minimize the impact of the redistribution step by locating the data where it needs to be before the joins are performed. When you run a query, the query optimizer redistributes the data to the compute nodes as needed to perform any joins and aggregations. To learn more about using automatic data compression, see Loading tables with automatic compression in the Amazon Redshift documentation.Īmazon Redshift stores data on the compute nodes according to a table's distribution style. The best way to enable data compression on table columns is by using the AUTO option in Amazon Redshift to apply optimal compression encodings when you load the table with data. Because columnar storage stores similar data sequentially, Amazon Redshift can apply adaptive compression encodings specifically tied to columnar data types. By loading less data into memory, Amazon Redshift can allocate more memory to analyzing the data. When you run a query, the compressed data is read into memory and then uncompressed when the query runs. For more information, see Working with sort keys in the Amazon Redshift documentation.ĭata compression reduces storage requirements, which reduces disk I/O and improves query performance. We recommend that you use sort keys to facilitate filters in the WHERE clause. This improves query speed significantly by reducing the amount of data to process. ![]() The query optimizer and the query processor use the information about where the data is located within a compute node to reduce the number of blocks that must be scanned. Understanding these properties is crucial for optimizing the performance, security, and cost-effectiveness of Amazon Redshift tables.Īmazon Redshift stores data on disk in sorted order according to a table’s sort keys. These properties include sorting, distribution style, compression encoding, and many others. Amazon Redshift tables are the fundamental units for storing data in Amazon Redshift, and each table has a set of properties that determine its behavior and accessibility.
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