hadoop map reduce secondary sorting

Grouping Comparator

Once the data reaches a reducer, all data is grouped by key. Since we have a composite key, we need to make sure records are grouped solely by the natural key. This is accomplished by writing a custom GroupPartitioner. We have a Comparator object only considering the yearMonth field of the TemperaturePair class for the purposes of grouping the records together.

public class YearMonthGroupingComparator extends WritableComparator {

    public YearMonthGroupingComparator() {
        super(TemperaturePair.class, true);
    }

    @Override
    public int compare(WritableComparable tp1, WritableComparable tp2) {
        TemperaturePair temperaturePair = (TemperaturePair) tp1;
        TemperaturePair temperaturePair2 = (TemperaturePair) tp2;
        return temperaturePair.getYearMonth().compareTo(temperaturePair2.getYearMonth());
    }
}

Here are the results of running our secondary sort job:

new-host-2:sbin bbejeck$ hdfs dfs -cat secondary-sort/part-r-00000

190101 -206

190102 -333

190103 -272

190104 -61

190105 -33

190106 44

190107 72

190108 44

190109 17

190110 -33

190111 -217

190112 -300

While sorting data by value may not be a common need, it’s a nice tool to have in your back pocket when needed. Also, we have been able to take a deeper look at the inner workings of Hadoop by working with custom partitioners and group partitioners. Refer this link also..What is the use of grouping comparator in hadoop map reduce

I find it easy to understand certain concepts with help of diagrams and this is certainly one of them.

Lets assume that our secondary sorting is on a composite key made out of Last Name and First Name.

With the composite key out of the way, now lets look at the secondary sorting mechanism

The partitioner and the group comparator use only natural key, the partitioner uses it to channel all records with the same natural key to a single reducer. This partitioning happens in the Map Phase, data from various Map tasks are received by reducers where they are grouped and then sent to the reduce method. This grouping is where the group comparator comes into picture, if we would not have specified a custom group comparator then Hadoop would have used the default implementation which would have considered the entire composite key, which would have lead to incorrect results.

Overview of MR steps