Tuning VSAM files tips are necessary to improve file performance. Data with Index faster in response, so these are useful to apply in the mainframe projects.

VISAM Tuning Tips

  1. Always use efficient Data and Index CI sizes.
  2. Assuming your record size permits, 4K/8K works best for the Data CI size of
  3. CICS online files. The Index CI should be large enough to hold all of the index entries for a DATA. VSAM is clearly explained in the express edition.
  4. For large keys that do not compress well, this could be 8K/16K. Too small an Index CI may result in unnecessary CA splits.
  5. The Index CI should be also large enough so that Index Levels is no more than 2 if possible. You can make it too big but 2K is often too small.
  6. Always define your VSAM clusters with the SPEED parameter.
  7. CI and CA Splits can greatly reduce CICS response time.
  8. Code CI and CA Freespace very carefully and monitor regularly.
  9. REORG of files does NOT fix splitting problems. It just covers the problem up for a short period of time.
  10. Allocate better FREESPACE.
  11. Never use SHROPT 4 if at all possible.
  12. Do not use ERASE ever.
  13. Do not use WRITECHECK ever.
  14. Do not use IMBED or REPLICATE, they are no longer supported and they waste DASD space.
  15. Optimize VSAM performance for both random and sequential processing by always specifying the appropriate number of NSR or LSR buffers.
  16. Remove Catalog Orphans from the Catalog.
  17. Make all ESDS files use the SPANNED parameter. (ref: IBM)
Positive Thinking

LATEST POSTS

FAANG-Style SQL Interview Traps (And How to Avoid Them)

SQL interviews at FAANG (Facebook/Meta, Amazon, Apple, Netflix, Google) are not about syntax. They are designed to test logical thinking, edge cases, execution order, and data correctness at scale. Many strong candidates fail—not because they don’t know SQL, but because they fall into subtle traps. In this blog, we’ll walk through real FAANG-style SQL traps,…

Common Databricks Pipeline Errors, How to Fix Them, and Where to Optimize

Introduction Databricks has become a premier platform for data engineering, especially with its robust integration of Apache Spark and Delta Lake. However, even experienced data engineers encounter challenges when building and maintaining pipelines. In this blog post, we’ll explore common Databricks pipeline errors, provide practical fixes, and discuss performance optimization strategies to ensure your data…

Something went wrong. Please refresh the page and/or try again.