I am building a multi-stage stored procedure that processes batches of data. When considering the best practices for writing efficient SQL queries, should I use temporary tables or table variables for intermediate storage? Which approach handles indexing better for large datasets?
3 answers
For intermediate data processing, choosing between temporary tables and table variables depends heavily on volume. According to best practices for writing efficient SQL queries, temporary tables are far superior for large datasets because they support statistics and allow you to build custom indexes after creation. Table variables do not have statistics, which often misleads the query optimizer into assuming a single row exists, leading to horrific execution plans. Stick to table variables only for tiny datasets under a few thousand rows to avoid TempDB compilation overhead.
Given that temporary tables generate statistics and utilize TempDB heavily, do we need to worry about severe TempDB allocation bottlenecks or metadata contention when scaling up to hundreds of concurrent transactions?
Temporary tables are definitely the way to go for big datasets because they allow you to create explicit indexes, ensuring your downstream joins stay incredibly fast and optimized.
I match Gloria's sentiment completely. Adding a primary key clustered index to our temp tables resolved a persistent bottleneck in our nightly data warehousing routines.
Arthur, you absolutely do. High concurrent creation of temporary tables can cause severe page latch contention in TempDB. To mitigate this, ensure you are running modern database setups that feature optimized memory-optimized metadata options or use table types with inline indexes.