How can you optimize SQL queries for specific hardware?

1 Know your hardware

Before you write or optimize any SQL query, you should have a basic understanding of the hardware specifications and limitations of your database server and client. For example, you should know how many cores and threads your CPU has, how much memory and cache your system has, how fast and reliable your disk and network are, and how they are configured and partitioned. These factors can influence how you design your database schema, indexes, and partitions, as well as how you write your SQL queries, such as choosing the right join methods, filtering and sorting criteria, and aggregation functions.

2 Use query execution plans

One of the most useful tools for optimizing SQL queries for specific hardware is the query execution plan, which is a graphical or textual representation of how the database engine will execute your query and what resources it will use. You can use query execution plans to identify potential bottlenecks, such as table scans, nested loops, hash joins, sorting operations, memory spills, and parallelism issues. You can also use query execution plans to compare different versions of your queries and see how changing the syntax, logic, or parameters can affect the performance and resource consumption.

3 Tune your queries for CPU

CPU is one of the most important and often the most scarce hardware resources for SQL queries. CPU-intensive queries are those that involve complex calculations, expressions, functions, or joins that require a lot of processing power. To optimize your queries for CPU, you should try to simplify your calculations and expressions, use scalar and inline functions instead of multi-statement or table-valued functions, avoid unnecessary conversions and casts, use indexed views or materialized views for complex aggregations, and use appropriate join methods and hints.

4 Tune your queries for memory

Memory is another critical hardware resource for SQL queries, as it is used to store intermediate results, cache data pages, and perform sorting and hashing operations. Memory-intensive queries are those that require a large amount of memory to execute, such as queries that use large temp tables, cursors, subqueries, or window functions. To optimize your queries for memory, you should try to reduce the size and number of temp tables, use set-based operations instead of cursors, use common table expressions or derived tables instead of subqueries, and use row_number or rank functions instead of window functions.

5 Tune your queries for disk

Disk is the hardware resource that stores your data and indexes, and it is also used as a backup for memory when it is not enough to handle the query workload. Disk-intensive queries are those that read or write a lot of data from or to the disk, such as queries that use full table scans, large joins, or bulk operations. To optimize your queries for disk, you should try to use indexes and partitions to filter and access your data more efficiently, use clustered indexes or columnstore indexes for large tables, use compression and encryption techniques to reduce the disk space and IO, and use batch or bulk operations instead of row-by-row operations.

6 Tune your queries for network

Network is the hardware resource that connects your database server and client, and it is also used to transfer data between nodes in a distributed or parallel system. Network-intensive queries are those that send or receive a lot of data over the network, such as queries that use distributed queries, linked servers, remote functions, or large result sets. To optimize your queries for network, you should try to use local queries and data sources as much as possible, use stored procedures or functions instead of remote queries or functions, use filters and aggregates to reduce the size of the result sets, and use compression and encryption techniques to reduce the network bandwidth and latency.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?