¿Cómo se utiliza ICETOOL para crear informes y resúmenes a partir de datos ordenados?

ICETOOL is the greatest, flexible, easy to use sorting, data manipulation tool you can find. I used it all the time to analyse data such as dbase records, log entries, smf records etc.. When developing DFSMSrmm iused it extensively and we provided many examples and samples of its use. Thank you Frank.

ICETOOL was originally developed by Frank Yaeger of DFSORT Development (now retired). It enabled him to add things to the DFSORT product that would be difficult to add to ICEMAN itself. ICETOOL can do basically everything ICEMAN can - and a lot more besides. For example, some ICETOOL operators drive ICEMAN with exit routines. You could write these exit routines yourself- but you really wouldn't want to.

To use ICETOOL for creating reports and summaries from sorted data in a mainframe environment, follow these steps: 1. **Define ICETOOL Operations**: Begin by specifying the operations you need, such as SELECT, DISPLAY, COUNT, or SPLICE. 2. **Specify Data Sets**: Indicate the input and output data sets. Input data sets should already be sorted if required for your operations. 3. **Set Conditions**: Define any conditions or criteria for selecting or summarizing data. This may include specifying fields and their values for filtering. 4. **Format Output**: Use formatting options to determine how the report or summary should appear, including field placement, headers, and footers

ICETOOL statements are inserted into TOOLIN. The default MODE for ICETOOL is set to STOP. If an error occurs in any operation, the MODE SCAN can be set initially to evaluate where the error is. There are three modes available that can be set or reset: - STOP mode (default) stops subsequent operations if an error is detected. - CONTINUE mode continues subsequent operations if an error is detected. - SCAN mode allows verification of ICETOOL declarations without performing any operation.

Journey into the realm of summarization with the STATS operator as your guide. Here, data transcends numbers to reveal the heartbeat of your datasets. Craft summaries that distill the essence of your findings—counts, totals, averages, and beyond. Let STATS be your compass in deciphering the tales woven within your sorted data tapestry.

Symbol maps are declared in the SYMNAMES and SYMNOUT statements, the former activates symbol processing and defines the DataSet of symbols to be used in the step, a sequential DataSet, a member of a partitioning, or a DD * can be used; the latter can be used to define the DataSet in which the original symbols and those generated by the step are displayed. SYMNAMES DataSets must be RECFM=F or RECFM=FB and LRECL=80.

The options SKIPREC and STOPAFT are always very important when testing our SORT steps, in order to reduce the load and waiting times between tests: SKIPREC We determine with it, how many of the initial records of the input file, we will not treat directly in our SORT, skipping them directly. STOPAFT We determine with it, how many records we want to remain in the output of the main SORT, keeping the first n records that pass through the INCLUDE/OMIT's, let's look at the internal execution order of the DFSORT to understand it better.

Most of the matching programs could be replaced by ICETOOLs or JOINKEYS, often this is not done due to lack of fluency in these tools by our mainframe staff, but with training and peer reviews of the developments and tests, we can ensure that our developers evolve in their knowledge, and thus the development and maintenance times of the many matching programs that exist in our applications, are significantly reduced. So, I personally recommend that while learning about ICETOOL you learn about JOINKEYS, in fact, JOINKEYS can be performed within ICETOOLs, with the option COPY JKFROM.

I've used SPLICE on multiple occasions, it works like magic every single time. For ex, a lengthy csd extract can be grouped and spliced to match the resources with its parameters.