How to resolve File OPEN errors better way in VSAM

This is very common error in VSAM files. The below are the reasons and resolution for OPEN error (File status code is 35).

Opening a file (ESDS, KSDS, or RRDS)

Before you can use WRITE, START, READ, REWRITE, or DELETE statements to process records in a file, you must first open the file with an OPEN statement.

File availability and creation affect OPEN processing, optional files, and file status codes 05 and 35. For example, if you open a file that is neither optional nor available in EXTEND, I-O, or INPUT mode, you get file status 35 and the OPEN statement fails. If the file is OPTIONAL, the same OPEN statement creates the file and returns file status 05.

An OPEN operation works successfully only when you

[VSAM FIle OPEN Error]
set fixed file attributes in the DD statement or data-set label for a file and specify consistent attributes for the file in the SELECT clause and FD entries of your COBOL program. 

Resolution and Check in the Program

Mismatches in the following items result in a file status code 39 and the failure of the OPEN statement:

  • Attributes for file organization (sequential, relative, or indexed)
  • Prime record key
  • Alternate record keys
  • Maximum record size
  • Record type (fixed or variable)

Additional Points

  • How you code the OPEN statement for a VSAM file depends on whether the file is empty (a file that has never contained records) or loaded.
  • For either type of file, your program should check the file status key after each OPEN statement.

The excellent VSAM Free tutorial presented in simple way

Virtual Storage Access Method – VSAM – is a data management system introduced by IBM in the 1970s as part of the OS/VS1 and OS/VS2 operating systems. Although there are still datasets that are best managed with the several other (non-VSAM) data management methods, VSAM is a major component of modern IBM operating systems. Since MVS 3.8 is one of those operating systems, I thought it might be useful to other Hercules’ users to set down some basic information about VSAM.
I have divided the material presented here into two main segments

  • Concepts and Facilities
  • Access Method Services

In the first segment, I will try to provide a simple description of the components of VSAM, with the goal of introducing VSAM to those who have not had practical experience with it. I don’t want to write a textbook, as I have several of those in my own library and they can be quite dry and boring. But, it is my perception that quite a few people are coming into the Hercules (and MVS) community who have not had any formal exposure to this type of material and I think there may be some positive benefit to my efforts. [ Read for More ]

Big data Vs Machine Learning, why both skills you need

machine-learning vs Big data
[machine-learning vs Big data]
Both Big data and Machine Learning skills you need for your next analytics career. Let us see how these are related. To solve Big data problems you need Machine learning Skills. This what actual point. Machine Learning basically a concept. There are many tools and Software technologies involved in developing  Machine Learning framework and algorithms.

How do you say Big data is increasing

We are entering the era of big data. For example, there are about 1 trillion web pages. One hour of video is uploaded to YouTube every second, amounting to 10 years of content. Walmart handles more than 1M transactions per hour and has databases containing more than 2.5 petabytes (2.5 × 1015) of information.

Where Machine Learning comes

This deluge of data calls for automated methods of data analysis, which is what machine learning provides. In particular, we define machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data!).

The best way to solve such problems is to use the tools of probability theory. Probability theory can be applied to any problem involving uncertainty. In machine learning, uncertainty comes in many forms: what is the best prediction about the
future given some past data? what is the best model to explain some data? what measurement should I perform next? etc. The probabilistic approach to machine learning is closely related to the field of statistics.