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Korn Shell Arrays - Getting Creative - Part III

For the past two weeks you've been shown how to assign and access values to individual positions within an array.  What if you wanted to store and then retrieve/manipulate multiple attributes of a large number of similar objects without giving up the performance achieved from using arrays?  This tip will present one possible option.

Consider what could be accomplished when using two or more single dimension arrays and a "master" subscript/index to tie the same element position in each array to one object that belongs to a large group of similar items.  Clear as mud?  Maybe an example will help.

The following data set was selected to illustrate this concept without complicating things.  In reality, the data you use will most likely be more technical and obviously larger in quantity. 

Peter     M    22    brown    green
Paul     M    27    blonde    blue
Mary     F    20    red    green

We'll pick up after five individual arrays (name, gender, age, hair, and eyes) have been populated with the above data.  This may have been accomplished by using a while loop to extract the data from one or more files and then store it in the appropriate array.

In this first snippet of code, the master index is set to the first object (person in this case), and then the attributes for each person is displayed:

$ while [ ${MASTER_INDEX} -lt ${#name[*]} ]
> do
> print ${name[${MASTER_INDEX}]}
> print ${gender[${MASTER_INDEX}]}
> print ${age[${MASTER_INDEX}]}
> print ${hair[${MASTER_INDEX}]}
> print ${eyes[${MASTER_INDEX}]}
> done

Each time through the loop, the master index (subscript) is incremented by 1 until all objects have had each of their attributes displayed.

This second loop will calculate the total age of the group, which is then averaged and displayed:

$ while [ ${MASTER_INDEX} -lt ${#name[*]} ]
> do
> done
$ (( AVERAGE_AGE=${TOTAL_AGE}/${#name[*]} ))
$ print ${AVERAGE_AGE}

Although these are relatively basic examples of how to use this technique, you should consider how it might be used to rapidly manipulate large quantities of data without incurring the overhead of disk I/O.