another. Example, transforming table data to JSON structure.

-   Join Pattern: This pattern processes related data stored in the input files. The most popular type of Join in MapReduce is the Reduce side join. It works in all cases but can be slow as the size of the data increases.

       **Algorithm**        **Design Pattern**   **Data Size**
  ------------------------ -------------------- ---------------
        MinMaxCount           Summarisation          16GB
       Average Count          Summarisation          16GB
      Median and Std.         Summarisation          16GB
       Inverted Index         Summarisation          12GB
            Grep                Filtering            12GB
           Top X                Filtering            2.7GB
          Distinct              Filtering            16GB
   Structure to Hierarchy   Data Organisation      12GB,16GB
    Total Order Sorting     Data Organisation        2.6GB
         Shuffling          Data Organisation        2.7GB
         RSJ Inner                 Join            16,2.7GB
       RSJ Left Outer              Join            16,2.7GB
      RSJ Right Outer              Join            16,2.7GB
       RSJ Full Outer              Join            16,2.7GB

  : Workloads Used

\[table:algorithms\]

**Evaluation of Results**
-------------------------

For each of the two setups, the same experiments are repeated and the results are plotted and discussed side by side. As expected, the time taken by the single node cluster for each of the algorithms is mostly greater than the eight node cluster. Below, we discuss the rest of the results in details.

### Summarisation Pattern

The algorithms and the results obtained included are listed in \[table:summarisation\] and corresponding plot in Figure 