SQL optimization is an incredibly important part of the data access lifecycle, especially for analytics applications. Yet, knowing exactly how to optimize SQL and databases can be incredibly difficult. This post looks to help by providing a succinct SQL Optimization Checklist to help those trying to optimize their database.
SQL Optimization: Overview
SQL optimization is the process of ensuring that the underlying SQL query being run is as efficient as possible and the underlying hardware running the SQL query is best-in-class. While simple at a high-level, knowing the strategies of areas that may be problematic can be cumbersome and overwhelming. The sections below go into the most important areas of SQL optimization including:
Indexing – the practice of caching and speeding up commonly accessed pieces of data by pre-flagging columns and certain portions of the data
SQL query writing – optimizing the methods by which data is accessed
Database configuration – ensuring that the hardware is up-to-date to support the data access needs of the workflow
SQL Indexing Strategies
Use appropriate index types (e.g., B-tree, hash, GIN, GiST) based on the query patterns and data characteristics.
Consider composite indexes for queries that filter or sort on multiple columns.
Index expressions or computed columns when frequently querying derived data.
SQL Query Writing Best Practices
Prefer JOINs over subqueries for better performance in many cases.
Use WHERE clauses to filter rows early in the query process to reduce the amount of data processed.
Leverage window functions for complex analytics, which can be more efficient than equivalent GROUP BY operations in some cases.
Utilize WITH clauses (CTEs) judiciously, as they can improve readability but might not always be optimized by the query planner.
Batch updates and deletes to minimize locking and improve transaction throughput.
Reduce unnecessary expensive comparisons such as string comparisons or case statements
SQL Data Access Patterns
Paginate results with LIMIT and OFFSET carefully, as skipping a large number of rows can be very expensive. Consider keyset pagination for large datasets.
Minimize the use of wildcard characters at the start of a LIKE pattern (e.g., %searchTerm) as they prevent efficient index use.
SQL Database Configuration and Maintenance
Regularly update statistics to help the database optimizer make informed decisions.
Adjust configuration settings such as buffer pool size, work memory, and others based on the database workload and hardware.
Partition large tables to improve query performance and maintenance tasks.
Use database-specific features like MySQL's query cache or PostgreSQL's materialized views for caching and faster data retrieval.
SQL Performance Analysis and Tools
Analyze slow query logs to identify and prioritize optimization efforts.
Monitor and optimize memory and disk usage to prevent bottlenecks.
Use EXPLAIN plans not just to audit, but also to iteratively refine queries by understanding their cost and execution paths.
SQL: Avoiding Common Pitfalls
Beware of locking issues in concurrent environments, especially with long transactions or heavy write operations.
Minimize transaction scopes to improve concurrency and reduce locking conflicts.
Be cautious with OR conditions and IN lists with many values, as they can lead to suboptimal query plans. Consider breaking them into multiple queries or using joins.
Advanced SQL Optimization Techniques
Consider query rewrite rules for specific cases where the database might not choose the optimal execution plan.
Use materialized views to precompute expensive aggregations or joins, especially for frequently accessed but infrequently updated data.
Leverage database-specific extensions and tools for performance tuning and query analysis.
Connection pool where possible to avoid connection overhead
SQL Optimization Checklist
For those looking to optimize their SQL, the below checklist based on the above rules can be incredibly helpful to refer to anytime there is a question around SQL optimization.
SQL Optimization Checklist Rules
It is advised to keep this checklist with you anytime you are running into SQL optimization issues and to ensure that the rules are followed. If by the end of the checkedlist you are still experiencing slow performance, it is a great idea to bring in another professional to help.
SQL Optimization Analysis and Tools
There are a lot of tools on the market that exist to help in SQL optimization. Below are a few of the most common tools across different SQL systems. While by no means exhaustive, this list should help in your search for SQL optimization tools.
SQL Optimization Checklist Analysis and Tools
Cross-Platform Tools
SQL Server Management Studio (SSMS) and Azure Data Studio for Microsoft SQL Server: Provide comprehensive environments for managing SQL Server infrastructure, including performance tuning, query optimization, and execution plan analysis.
pgAdmin for PostgreSQL: Offers a graphical interface to manage PostgreSQL databases, including monitoring tools and the execution plan visualizer.
MySQL Workbench for MySQL: Includes performance dashboard and query optimization tools, allowing for visual explanation plans and server configuration.
Oracle SQL Developer for Oracle: A free, integrated development environment that simplifies the development and management of Oracle databases, including performance tuning and optimization tools.
Performance Monitoring and Analysis Tools
Prometheus and Grafana: For cross-database monitoring, these tools can be used together to collect and visualize metrics, including SQL execution times, resource usage, and more.
New Relic and Datadog: These application performance management (APM) tools offer SQL monitoring capabilities, providing insights into query performance and database health across various platforms.
SolarWinds Database Performance Analyzer: A cross-platform tool that provides detailed analysis of SQL execution, wait times, and optimization recommendations for SQL Server, Oracle, MySQL, MariaDB, Aurora, and PostgreSQL.
Query Optimization and Profiling Tools
EXPLAIN and EXPLAIN ANALYZE commands: Available in most SQL databases, these commands show the execution plan of a SQL query, highlighting how the database will execute (or has executed) your query, including joins, indexes used, and estimated costs.
Percona Toolkit: A collection of advanced command-line tools to perform a variety of MySQL and MongoDB database tasks related to performance, such as query reviews and optimizations.
pgMustard: A PostgreSQL-specific tool that provides a user-friendly interface for analyzing EXPLAIN output, offering suggestions for query improvements.
Index Management and Analysis
pgBadger for PostgreSQL: A log analyzer that provides detailed reports on query performance and suggestions for index optimizations.
Index Advisor tools: Many databases offer built-in index advisors (e.g., SQL Server's Database Tuning Advisor, Oracle's SQL Tuning Advisor) that analyze query workloads and recommend index changes to improve performance.
Log Analyzers
Slow Query Log: Available in MySQL and PostgreSQL, this log records queries that exceed a defined execution time, helping identify slow-running queries for optimization.
Automated Performance Tuning Tools
AWS RDS Performance Insights: For databases running on Amazon RDS, this tool offers SQL-level metrics, visualizations, and automated performance tuning recommendations.
Azure SQL Database Automatic Tuning: Offers automatic performance optimization for queries, including automatic index management.
Custom Scripts and Utilities
Many DBAs and developers write custom scripts to automate routine checks and gather performance metrics tailored to their specific environment and needs.
Selecting the right tools requires assessing your database environment, the complexity of your SQL queries, and specific performance issues you're encountering. Combining these tools with best practices in SQL query writing, indexing, and database configuration will significantly enhance your database's performance and scalability.
SQL Optimization: Is it Always Possible?
It is always possible to optimize SQL. However, sometimes that can still lead to long query times. For example, if a query is run only once a year on millions of rows, a company may decide it doesn’t make sense to do an index, as indexing does add overhead to a database write operation. Further, because the query is run so infrequently, companies may decide not to optimize the hardware for performance as well, since hardware optimization can be expensive. While the query could take minutes or hours to run, in this case, it may be just fine. If you are having trouble optimizing SQL given business constraints, it is okay to ask yourself if it is a necessity to optimize. Optimization can sometimes be at the cost of other business objectives.
Automating SQL Optimization
There are many tools on the market that seek to automate SQL optimization. Most of these tools can be helpful in that process, but analyzing the underlying query plan and understanding your database-specific hardware is always going to be a more informed way to do this optimization process. As tools continue to evolve, many will integrate into these tools as well to perform advanced diagnostics; regardless, understanding these rules yourself is always a great starting point!
Why Optimize? SQL Optimization for Internal and External BI
Users of both internal and external BI products expect SQL queries to be incredibly fast; for the external use case, even a few seconds can be too long for some use cases. To that end, SQL optimization is an incredibly important part of the embedded analytics and internal analytics process. With the rise of data-driven approaches at almost all companies, it is becoming more important than ever to understand and practice SQL optimization.
SQL Optimization Automation Guide
SQL Optimization Conclusion
SQL optimization is by no means easy; however, with a guideline of where to look to optimize, it becomes a much less daunting task. With the rise of artificial intelligence (AI) and Large Language Models (LLMs), it is only becoming easier to automate many of these best practices for everyday SQL writing. However, knowing these rules yourself is still incredibly important, as it allows for you to make sure all best practices are being used. Hopefully this piece was able to shed light on how to optimize SQL for your needs.
