Inserts, updates and deletes on large tables can be very slow and expensive, cause locking and blocking, and even fill up the transaction log. One of the main benefits of table partitioning is that you can speed up loading and archiving of data by using partition switching.
Partition switching moves entire partitions between tables almost instantly. It is extremely fast because it is a metadata-only operation that updates the location of the data, no data is physically moved. New data can be loaded to separate tables and then switched in, old data can be switched out to separate tables and then archived or purged. All data preparation and manipulation can be done in separate tables without affecting the partitioned table.
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There are many benefits of partitioning large tables. You can speed up loading and archiving of data, you can perform maintenance operations on individual partitions instead of the whole table, and you may be able to improve query performance. However, implementing table partitioning is not a trivial task and you need a good understanding of how it works to implement and use it correctly.
Being a business intelligence and data warehouse developer, not a DBA, it took me a while to understand table partitioning. I had to read a lot, get plenty of hands-on experience and make some mistakes along the way. (The illustration to the left is my Table Partitioning Cheat Sheet.) One of my favorite ways to learn something is to figure out how to explain it to others, so I recently did a webinar about table partitioning. (Update in 2020: The webinar has now been archived. Please contact Pragmatic Works if you would like to watch it, as they are the owners and publishers.) I wanted to follow that up with focused blog posts that included answers to questions I received during the webinar. This post covers the basics of partitioned tables, partition columns, partition functions and partition schemes.
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