How to Read the Query Execution Plans to Optimize Your Queries

Understanding and optimizing database queries involves comprehending the computational complexity and the specific query execution plans used by your database system. Computational complexity, particularly time complexity, plays a crucial role in determining how efficiently a query can scale with increased data inputs, with Big O notation often used to describe this complexity independent of hardware variations. Database query execution plans, generated through declarative query languages like SQL or Cypher, outline the sequence of steps necessary for data retrieval, and tools like EXPLAIN and PROFILE can provide insight into these plans and their performance metrics. Indexes significantly influence query execution by altering query plans to improve efficiency, as demonstrated by the Memgraph database, which uses a highly-concurrent skip list for indexing. The article emphasizes the importance of creating and utilizing indexes to reduce execution time and improve scalability, highlighting how different query structures and index configurations can lead to varying performance outcomes. Effective query optimization requires understanding the query plan details, minimizing unnecessary operations, and ensuring proper indexing, with the potential to engage with the database community for additional support and insights.