Executing pig scripts on large data sets, usually TAKES a LONG time. To tackle this, developers RUN pig scripts on sample data but there is possibility that the sample data selected, might not execute your pig script PROPERLY.

For instance, if the script has a join operator there should be at least a few records in the sample data that have the same key, otherwise the join operation will not return any results. To tackle these kind of issues, illustrate is used. illustrate takes a sample from the data and whenever it comes across operators like join or filter that remove data, it ensures that only some records pass through and some do not, by making modifications to the records such that they meet the condition. illustrate just shows the output of each stage but does not run any MapReduce task.

Executing pig scripts on large data sets, usually takes a long time. To tackle this, developers run pig scripts on sample data but there is possibility that the sample data selected, might not execute your pig script properly.

For instance, if the script has a join operator there should be at least a few records in the sample data that have the same key, otherwise the join operation will not return any results. To tackle these kind of issues, illustrate is used. illustrate takes a sample from the data and whenever it comes across operators like join or filter that remove data, it ensures that only some records pass through and some do not, by making modifications to the records such that they meet the condition. illustrate just shows the output of each stage but does not run any MapReduce task.