Name

       condor_transform_ads  Transform  -  ClassAds  according to specified rules, and output the
       transformed ClassAds.

Synopsis

       condor_transform_ads [-help [rules]]

       condor_transform_ads-rules  rules-file[-in[:<form>]   infile]   [-out[:<form>[,   nosort]]
       outfile]  [<key>=<value>] [-long] [-json] [-xml] [-verbose] [-terse] [-debug] [-unit-test]
       [-testing] [-convertoldroutes] [infile1 ...infileN]

       Note that exactly one rules file, and at least one input file, must be  specified.  If  no
       output file is specified, output will be written to  stdout .

Description

       condor_transform_adsreads ClassAds from a set of input files, transforms them according to
       rules defined in a rules file, and outputs the resulting transformed ClassAds.

       See     https://htcondor-wiki.cs.wisc.edu/index.cgi/wiki?p=TjsAdTransformLanguagefor     a
       description of the transform language.

Options

       -help [rules]

          Display usage information and exit. -help rulesdisplays information about the available
          transformation rules.

       -rules rules-file

          Specifies the file containing definitions of the transformation rules.

       -in[:<form>] infile

          Specifies an input file containing ClassAd(s) to be transformed. <form>, if  specified,
          is one of:

             * long: traditional long form (default)

             * xml: XML form

             * json: JSON ClassAd form

             * new: "new" ClassAd form without newlines

             *  auto:  guess  format by reading the input If  - is specified for infile, input is
             read from  stdin .

       -out[:<form>[, nosort] outfile

          Specifies an output file to receive the transformed ClassAd(s). <form>,  if  specified,
          is one of:

             * long: traditional long form (default)

             * xml: XML form

             * json: JSON ClassAd form

             * new: "new" ClassAd form without newlines

             *  auto:  use  the  same format as the first input ClassAds are storted by attribute
             unless nosortis specified.

       [<key>=<value>]

          Assign key/value pairs before rules file is parsed; can be used to  pass  arguments  to
          rules. (More detail needed here.)

       -long

          Use long form for both input and output ClassAd(s). (This is the default.)

       -json

          Use JSON form for both input and output ClassAd(s).

       -xml

          Use XML form for both input and output ClassAd(s).

       -verbose

          Verbose mode, echo transform rules as they are executed.

       -terse

          Disable the -verboseoption.

       -debug

          More information needed here.

       -unit-test

          More information needed here.

       -testing

          More information needed here.

       -convertoldroutes

          More information needed here.

Exit Status

       condor_transform_adswill  exit  with  a status value of 0 (zero) upon success, and it will
       exit with the value 1 (one) upon failure.

Examples

       Here's a simple example that transforms the given input ClassAds according  to  the  given
       rules:

        # File: my_input
        ResidentSetSize = 500
        DiskUsage = 2500000
        NumCkpts = 0
        TransferrErr = false
        Err = "/dev/null"

        # File: my_rules
        EVALSET MemoryUsage ( ResidentSetSize / 100 )
        EVALMACRO WantDisk = ( DiskUsage * 2 )
        SET RequestDisk ( $(WantDisk) / 1024 )
        RENAME NumCkpts NumCheckPoints
        DELETE /(.+)Err/

        # Command:
        condor_transform_ads  -rules my_rules -in my_input

        # Output:
        DiskUsage = 2500000
        Err = "/dev/null"
        MemoryUsage = 5
        NumCheckPoints = 0
        RequestDisk = ( 5000000 / 1024 )
        ResidentSetSize = 500

Author

       Center for High Throughput Computing, University of Wisconsin-Madison

Copyright

       Copyright   (C)   1990-2016  Center  for  High  Throughput  Computing,  Computer  Sciences
       Department, University of Wisconsin-Madison, Madison, WI. All  Rights  Reserved.  Licensed
       under the Apache License, Version 2.0.

                                            April 2018                    condor_transform_ads(1)