MySQL Cookbook Free Open Book

MySQL Cookbook

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10.44 Epilog

Recall the scenario with which this chapter began:

Suppose you have a file named somedata.csv that contains 12 columns of data in comma-separated values (CSV) format. From this file you want to extract only columns 2, 11, 5, and 9, and use them to create database records in a MySQL table that contains name, birth, height, and weight columns. You need to make sure that the height and weight are positive integers, and convert the birth dates from MM/DD/YY format to CCYY-MM-DD format. How can you do this?

So ... how would you do that, based on the techniques discussed in this chapter?

Much of the work can be done using the utility programs developed here. You can convert the file to tab-delimited format with cvt_file.pl, extract the columns in the desired order with yank_col.pl, and rewrite the date column to ISO format with cvt_date.pl:

% cvt_file.pl --iformat=csv somedata.csv \
    | yank_col.pl --columns=2,11,5,9 \
    | cvt_date.pl --columns=2 --iformat=us --add-century > tmp

The resulting file, tmp, will have four columns representing the name, birth, height, and weight values, in that order. It needs only to have its height and weight columns checked to make sure they contain positive integers. Using the is_positive_integer( ) library function from the Cookbook_Utils.pm module file, that task can be achieved using a short special-purpose script that isn't much more than an input loop:

#! /usr/bin/perl -w
# validate_htwt.pl - height/weight validation example

# Assumes tab-delimited, linefeed-terminated input lines.

# Input columns and the actions to perform on them are as follows:
# 1: name; echo as given
# 2: birth; echo as given
# 3: height; validate as positive integer
# 4: weight; validate as positive integer

use strict;
use lib qw(/usr/local/apache/lib/perl);
use Cookbook_Utils;

while (<>)
{
    chomp;
    my ($name, $birth, $height, $weight) = split (/\t/, $_, 4);
    warn "line $.:height $height is not a positive integer\n"
                                if !is_positive_integer ($height);
    warn "line $.:weight $weight is not a positive integer\n"
                                if !is_positive_integer ($weight);
}

exit (0);

The validate_htwt.pl script doesn't produce any output (except for warning messages), because it doesn't need to reformat any of the input values. Assuming that tmp passes validation with no errors, it can be loaded into MySQL with a simple LOAD DATA statement:

mysql> LOAD DATA LOCAL INFILE 'tmp' INTO TABLE  tbl_name ; 
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         Main Menu
    Main Page
    Table of content
    Copyright
    Preface
    Chapter 1. Using the mysql Client Program
    Chapter 2. Writing MySQL-Based Programs
    Chapter 3. Record Selection Techniques
    Chapter 4. Working with Strings
    Chapter 5. Working with Dates and Times
    Chapter 6. Sorting Query Results
    Chapter 7. Generating Summaries
    Chapter 8. Modifying Tables with ALTER TABLE
    Chapter 9. Obtaining and Using Metadata
    Chapter 10. Importing and Exporting Data
    10.1 Introduction
    10.2 Importing Data with LOAD DATA and mysqlimport
    10.3 Specifying the Datafile Location
    10.4 Specifying the Datafile Format
    10.5 Dealing with Quotes and Special Characters
    10.6 Importing CSV Files
    10.7 Reading Files from Different Operating Systems
    10.8 Handling Duplicate Index Values
    10.9 Getting LOAD DATA to Cough Up More Information
    10.10 Don't Assume LOAD DATA Knows More than It Does
    10.11 Skipping Datafile Lines
    10.12 Specifying Input Column Order
    10.13 Skipping Datafile Columns
    10.14 Exporting Query Results from MySQL
    10.15 Exporting Tables as Raw Data
    10.16 Exporting Table Contents or Definitions in SQL Format
    10.17 Copying Tables or Databases to Another Server
    10.18 Writing Your Own Export Programs
    10.19 Converting Datafiles from One Format to Another
    10.20 Extracting and Rearranging Datafile Columns
    10.21 Validating and Transforming Data
    10.22 Validation by Direct Comparison
    10.23 Validation by Pattern Matching
    10.24 Using Patterns to Match Broad Content Types
    10.25 Using Patterns to Match Numeric Values
    10.26 Using Patterns to Match Dates or Times
    10.27 Using Patterns to Match Email Addresses and URLs
    10.28 Validation Using Table Metadata
    10.29 Validation Using a Lookup Table
    10.30 Converting Two-Digit Year Values to Four-Digit Form
    10.31 Performing Validity Checking on Date or Time Subparts
    10.32 Writing Date-Processing Utilities
    10.33 Using Dates with Missing Components
    10.34 Performing Date Conversion Using SQL
    10.35 Using Temporary Tables for Data Transformation
    10.36 Dealing with NULL Values
    10.37 Guessing Table Structure from a Datafile
    10.38 A LOAD DATA Diagnostic Utility
    10.39 Exchanging Data Between MySQL and Microsoft Access
    10.40 Exchanging Data Between MySQL and Microsoft Excel
    10.41 Exchanging Data Between MySQL and FileMaker Pro
    10.42 Exporting Query Results as XML
    10.43 Importing XML into MySQL
    10.44 Epilog
    Chapter 11. Generating and Using Sequences
    Chapter 12. Using Multiple Tables
    Chapter 13. Statistical Techniques
    Chapter 14. Handling Duplicates
    Chapter 15. Performing Transactions
    Chapter 16. Introduction to MySQL on the Web
    Chapter 17. Incorporating Query Resultsinto Web Pages
    Chapter 18. Processing Web Input with MySQL
    Chapter 19. Using MySQL-Based Web Session Management
    Appendix A. Obtaining MySQL Software
    Appendix B. JSP and Tomcat Primer
    Appendix C. References
    Colophone
    Index


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