Data Munging with Perl by David Cross

By David Cross

The Perl language is easily suited to use with "data munging" projects: those who contain reworking and massaging facts. whereas Perl is usually used for such projects, there was no publication concerned with the subject of munging. This publication covers the fundamental paradigms of programming and discusses the various thoughts which are particular to Perl. It additionally examines typical info codecs similar to textual content, binary, HTML, and XML ahead of giving tips about developing and parsing new based info codecs. resource code downloads and technical help from the authors can be found on publisher's site.

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Example text

They are known as filters as they read their input from STDIN, filter the data in a particular way, and write what is left to STDOUT. This is a concept that we can make good use of in our data munging programs. If we write our programs so that they make no assumptions about the files that they are reading and writing (or, indeed, whether they are even reading from and writing to files) then we will have written a useful generic tool, which can be used in a number of different circumstances. Example: I/O independence Suppose, for example, that we had written a program called data_munger which munged data from one system into data suitable for use in another.

Suppose that we are combining data from several systems into one database. In this case our different data sources may well provide us with data in very different formats, but they all need to be converted into the same format to be passed on to our data sink. Our lives will be made much easier if we can write one output routine that handles writing the output from all of our data inputs. In order for this to be possible, the data structures in which we store our data just before we call the combined output routines will need to be in the same format.

This can lead to a couple of problems: ■ ■ It is possible that not every programmer who writes these programs has exactly the same understanding of the rules. Therefore, each program may have subtly different interpretations of the rules. At some point in the future these rules may be changed. When this happens, the same changes in logic will need to be made to each of the programs that use the existing business rules. This may be a large job, and the more times the changes have to be made, the higher the chance that errors will creep in.

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