When to perform RUNSTATS in DB2 is the prime question in SQL interviews. Here are ideas and examples for you to tell correct answer in your next interview.
You may also like Array vs List in Python.
This is a question for developers while working on projects and when they give interviews. Runstats is a utility that collects statistics and sends these to the optimizer for the right decision.
When to Run Runstats in DB2
You need to run this utility during the below scenarios.
- When a table is loaded
- When an index is created
- When a tablespace is reorganized
- When there have been extensive updates, deletions, or insertions in a tablespace
- After the recovery of a tablespace to a prior point in time
How Optimizer Decision Affects When You Not Collect Statistics
- The optimizer may not take the right decision for multi-joint tables query
- The optimizer may choose the wrong choice of index, which in turn affects performance
- When a column designed to have default values, you need to run the RUNSTATS utility for frequency value statistics help in better decision
How to Know When to Execute Runstats
- You need to refer to SYSIBM.SYSCOLDIST catalog table about the running of Runstats utility is needed or not. The IBM Data Studio tool will also show those statistics.
- DB2 gets the frequency value of statistics when Runstats utility is executed.
Keep Reading
Azure Data Factory (ADF): The Complete Beginner-Friendly Guide (2026 Edition)
Azure Data Factory (ADF) is Microsoft’s fully managed, cloud-based data integration and orchestration service. It helps you collect data from different sources, transform it at scale, and load it into your preferred analytics or storage systems. Whether you are working with Azure SQL, on-premises databases, SaaS applications, or big-data systems, ADF gives you a unified…
Complete Terraform CI/CD Pipeline Setup with GitHub Actions — Beginner to Advanced
The complete terraform setup example ci cd pipeline to create AWS resources using GitHub actions
AWS SageMaker + S3 Tutorial: Build, Train, and Deploy a LiDAR ML Model
This end-to-end tutorial shows how to upload LiDAR images to AWS S3, preprocess point cloud data, train an ML model in Amazon SageMaker, deploy the model, and store prediction outputs back in S3. Includes clear practical steps for beginners and ML engineers.
Srinimf 