# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 6 Experimental Campaign
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(Env_{1}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 96,230 & 85 & 5275 & 3,664,468 & 1,322,651 & 350,531 & 5,439,240 \\ \(Conf_{2}\) & 112,044 & 91 & 918 & 5,244,081 & 1,337,190 & 399,251 & 7,093,575 \\ \(Conf_{3}\) & 198,203 & 48 & 633 & 6,795,418 & 417,317 & 79,061 & 7,490,680 \\ \(Conf_{4}\) & 203,945 & 122 & 813 & 26,326,300 & 1,024,791 & 329,195 & 27,885,166 \\ \(Conf_{5}\) & 97,713 & 80 & 1028 & 31,858,330 & 8,478,428 & 369,502 & 40,805,081 \\ \(Conf_{6}\) & 72,608 & 48 & 539 & 49,408,525 & 260,787 & 140,283 & 49,882,790 \\ \(Conf_{7}\) & 67,373 & 41 & 788 & 21,268,230 & 932,382 & 247,718 & 22,516,532 \\ \(Conf_{8}\) & 121,333 & 75 & 1305 & 39,294,169 & 9,024,700 & 529,506 & 48,971,088 \\ \(Conf_{9}\) & 157,255 & 83 & 882 & 61,548,359 & 376,671 & 115,980 & 62,199,230 \\ \hline \(Env_{2}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 18,623 & 56 & 1002 & 901,137 & 280,039 & 344,775 & 1,545,632 \\ \(Conf_{2}\) & 19,998 & 68 & 199 & 968,779 & 291,047 & 356,721 & 1,636,812 \\ \(Conf_{3}\) & 39,058 & 46 & 158 & 1,330,173 & 93,359 & 63,953 & 1,526,747 \\ \(Conf_{4}\) & 41,487 & 83 & 173 & 4,392,227 & 183,535 & 307,911 & 4,925,416 \\ \(Conf_{5}\) & 19,370 & 62 & 231 & 6,228,201 & 1,479,875 & 349,425 & 8,077,164 \\ \(Conf_{6}\) & 16,938 & 29 & 98 & 10,130,264 & 60,038 & 111,021 & 10,318,388 \\ \(Conf_{7}\) & 13,047 & 41 & 189 & 4,684,841 & 191,352 & 214,286 & 5,103,756 \\ \(Conf_{8}\) & 25,700 & 61 & 296 & 7,254,463 & 1,704,038 & 411,768 & 9,396,326 \\ \(Conf_{9}\) & 27,347 & 75 & 203 & 11,092,073 & 82,351 & 110,652 & 11,312,701 \\ \hline \(Env_{3}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 9452 & 63 & 1050 & 446,641 & 129,776 & 174,026 & 761,008 \\ \(Conf_{2}\) & 9011 & 79 & 219 & 529,375 & 117,673 & 188,706 & 845,063 \\ \(Conf_{3}\) & 18,057 & 49 & 163 & 759,816 & 47,116 & 21,842 & 847,043 \\ \(Conf_{4}\) & 20,138 & 84 & 180 & 2,159,509 & 70,524 & 176,915 & 2,427,350 \\ \(Conf_{5}\) & 9906 & 72 & 249 & 2,701,601 & 880,321 & 175,523 & 3,767,672 \\ \(Conf_{6}\) & 9404 & 30 & 104 & 4,610,965 & 31,309 & 56,121 & 4,707,933 \\ \(Conf_{7}\) & 5712 & 46 & 190 & 2,661,610 & 71,846 & 105,054 & 2,844,458 \\ \(Conf_{8}\) & 13,173 & 66 & 324 & 4,132,891 & 771,158 & 245,783 & 5,163,395 \\ \(Conf_{9}\) & 11,117 & 88 & 208 & 6,224,426 & 30,077 & 66,208 & 6,332,124 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Execution times for the corpus of 10,000 data sets (first Map-Reduce version). Finally, Table 8 reports the execution times obtained for the enhanced version of the implementation, based on _sdatasets_, executed on environment \(Env_{3}\); in the rest of the paper, we will denote this version as _Spark V2_. Again, each part of the table describes execution times obtained for 1000, 10,000 and 15,000 data sets, respectively. In all five tables, we tested the nine configurations described in Table 3 (from \(Conf_{1}\) to \(Conf_{9}\)); execution times of each single step of the retrieval process (see Section 3.3) are reported (columns **Step 1** to **Step 6**); column **Total** reports the total execution time for each configuration.