# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
## 7 Discussion and Conclusions  
A total number of 12 tests were executed against the compared tools BigchainDB and QLDB. Table 10 and Figures 30 and 31 show the results of the experiments. Within this table, the maximum values have been highlighted in green and the minimum values in blue. Figure 28: 10,000 reads memory usage—BigchainDB nodes. Figure 29: 10,000 reads memory usage—QLDB. Figure 31: Memory usage (MB). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**BigchainDB**} & \multicolumn{1}{c}{**QLDB**} \\ \cline{3-11}  & & \multicolumn{3}{c}{**Node 1**} & \multicolumn{3}{c}{**Node 2**} & \multicolumn{3}{c}{**Node 3**} & \multicolumn{3}{c}{**Node 4**} & \multicolumn{3}{c}{**Node 4**} \\ \cline{3-11}  & & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** \\ \hline \multirow{3}{*}{1000 inserts} & CPU usage (\%) & 0.0900 & 0.1000 & 0.0500 & 0.0600 & 0.0600 & 0.0700 & 0.0600 & 0.0700 & 0.0200 & 0.0210 \\  & Memory usage (MB) & 300.0 & 325.0 & 200.0 & 225.0 & 225.0 & 275.0 & 225.0 & 275.0 & 45.0 & 45.5 \\ \hline \multirow{3}{*}{5000 inserts} & CPU usage (\%) & 0.0900 & 0.1000 & 0.0500 & 0.0600 & 0.0600 & 0.0700 & 0.0600 & 0.0700 & 0.0180 & 0.0220 \\  & Memory usage (MB) & 300.0 & 325.0 & 200.0 & 225.0 & 225.0 & 275.0 & 225.0 & 275.0 & 45.0 & 50.0 \\ \hline \multirow{3}{*}{10,000 inserts} & CPU usage (\%) & 0.0900 & 0.1100 & 0.0500 & 0.0600 & 0.0700 & 0.0800 & 0.0700 & 0.0800 & 0.0175 & 0.0225 \\  & Memory usage (MB) & 450.0 & 550.0 & 275.0 & 350.0 & 275.0 & 375.0 & 275.0 & 375.0 & 45.0 & 50.0 \\ \hline \multirow{3}{*}{1000 reads} & CPU usage (\%) & 0.0600 & 0.0630 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0120 & 0.0125 \\  & Memory usage (MB) & 430.0 & 450.0 & 300.0 & 335.0 & 300.0 & 335.0 & 300.0 & 335.0 & 45.0 & 50.0 \\ \hline \multirow{3}{*}{5000 reads} & CPU usage (\%) & 0.0600 & 0.0600 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0120 & 0.0127 \\  & Memory usage (MB) & 430.0 & 480.0 & 300.0 & 335.0 & 300.0 & 335.0 & 300.0 & 335.0 & 45.0 & 50.0 \\ \hline \multirow{3}{*}{10,000 reads} & CPU usage (\%) & 0.0400 & 0.0700 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0100 & 0.0200 & 0.0120 & 0.0132 \\  & Memory usage (MB) & 430.0 & 480.0 & 300.0 & 335.0 & 300.0 & 335.0 & 300.0 & 335.0 & 45.0 & 50.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of the experiments. Figure 30: CPU usage (%). In Case 1, with 1000 inserts, the QLDB uses 78% less CPU and 85% less memory than Node 1 of BigchainDB (the most consuming node), and 63.3% less CPU and 82% less memory than the least consuming node. In Case 2, with 5000 inserts, QLDB has 80% less CPU and 76% less memory than Node 1 of BigchainDB (the most consuming node), and 63.6% less CPU and 74% less memory than the other nodes. Case 3 (10,000 inserts) demonstrates the peak performance for the QLDB inserts. In the worst-case scenario for BigchainDB, QLDB uses 80% less CPU and 90.6% less memory than Node 1 of BigchainDB, and 63.6 less CPU and 85.5% memory, in comparison with the other three nodes. In Case 4, with 1000 reads, QLDB has 80% less CPU and 90% less memory than Node 1 of BigchainDB and 7.7% less CPU and 85.3% less memory than the least consuming node. In Case 5 (5000 reads), QLDB uses 80% less CPU and 90.1% less memory than Node 1 of BigchainDB (the most consuming node), and 4% less CPU and 85.5% less memory than the other nodes. Running Case 6 (10,000 reads) reveals that QLDB has 80.3% less CPU and 90.5% less memory than Node 1 of BigchainDB (the most consuming node), and 4.1% less CPU and 85.8% less memory than the other nodes. As far as each insert case is concerned, QLDB excels in both memory usage and CPU utilization. At its maximum, the CPU utilization is of 0.0255%, while the memory usage is below 50 MB. It is a fact that BigchainDB would be more resource-consuming, due to its distributed nature, because it has to write the same data multiple times, depending on how many nodes the network comprises.