With this new feature, you can now dynamically change the Sort Keys of your existing table. Changing your Sort Keys meant re-sorting your data. This was required because, Redshift physically sorts the data in the underlying storage. Previously, this meant, recreating your table with the new set of Sort Keys and loading all the data into that newly created table. However, over time, as your workload evolves there may be a need to modify the Sort Keys that you originally picked. When you start using Redshift, you pick Distribution and Sort Keys for your tables. How much percentage benefit would you derive by running “VACUUM SORT” against the tableĬheck the following documentation for more details: ALTER SORT KEY Dynamically.What percentage of a particular table is “unsorted”.Together, these columns tell you the following: You can also monitor the “vacuum_sort_benefit” and “unsorted” columns in the SVV_TABLE_INFO table. However, if you do have large data loads, you may still want to run “VACUUM SORT” manually (as Automatic Sorting may take a while to fully Sort in the background). With this new feature, Redshift automatically performs the sorting activity in the background without any interruption to query processing. So, you choose a SORT Key for your table initially and on incremental data loads, you had to earlier run “VACUUM SORT” command to make sure the data blocks are sorted. If your data is NOT sorted well enough, Redshift may read unwanted blocks and then later skip them in the memory. This would directly improve query performance as Redshift can read specific blocks of data (when your query has a filter) and also apply compression better. One of the most important best practices when it comes to Redshift is to keep the data Sorted. You can find the complete set current limitations here: Automated Table Sort Over time, I am sure AWS folks would address these limitations based on customer feedback. For example, you need to manually refresh the MV whenever your base tables undergo changes. There are some current limitations though. You can now use the native MV (available in preview) capability to address such needs. Till now, Redshift lacked support for MV and the recommendation has been to either modify your workloads or implement architectural changes such as performing a query rewrite using pg_bouncer Materialized Views (MV) have a significant improvement on query performance for repeated workloads such as Dashboarding, queries from BI tools or certain predictable steps in ETL pipelines. This has been one of the most wanted asks from many customers who migrate from other DW systems into Redshift. Here are some of the new features that were announced around the re:Invent 2019 timeframe that I specifically think a lot of customers (based on my earlier interaction with them) would look to put in use. Redshift has been going through a series of major changes which tremendously simplifies schema design and overall management of workloads. This is part of the Beyond The Shiny New Toys series where I write about AWS reInvent 2019 announcements
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