# Execution Plan Properties in SQL Server > [!Note] > Understanding execution plan properties is essential for diagnosing performance and choosing optimal query strategies. **Operations and Structure:** An execution plan node specifies a **Physical Operation**, which is the concrete algorithm SQL Server uses at that step (for example, an [[Index Scan Operator]] or **Hash Match**), alongside its corresponding **Logical Operation**, the relational‐algebra concept it implements (such as [[Table Scan Operator]] or **Join**). The plan is represented as a tree where each operator node links to its parent, and directional arrows indicate the flow of rows through the plan. **Cost Estimations:** Each operator carries an **Estimated I/O Cost**, forecasting the disk read and write overhead, and an **Estimated CPU Cost**, projecting processor usage. The **Estimated Subtree Cost** aggregates the cumulative cost of the operator and all its descendant nodes, allowing the optimizer to compare alternative plan shapes and select the lowest‐cost overall strategy. **Cardinality and Row Size:** The **Estimated Number of Rows** predicts how many rows each operator will produce, driving decisions about join methods and memory grants. After execution, the **Actual Number of Rows** reveals the true rowcount returned by the operator, which is vital for identifying misestimation issues. The **Estimated Row Size** approximates the average byte size of each row processed by the operator, influencing memory usage estimates. **Execution Counts:** To account for repeated operations in nested or parallel plans, the **Estimated Number of Executions** shows how many times the optimizer expects an operator to run. The **Number of Executions** displays the actual run count observed during query execution, highlighting discrepancies that can uncover inefficient plan reuse or unexpected loop iterations. --- ## References - Korotkevitch, D. (2022). _SQL Server advanced troubleshooting and performance tuning: Best practices and techniques_. O’Reilly Media. - Nevarez, B. (2022). _SQL Server query tuning and optimization: Optimize Microsoft SQL Server 2022 queries and applications_. Packt Publishing.