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Learn how to use the JMeter load testing tool. It comes with a built-in graph listener, which allows you to watch JMeter do, well... something. While this gives a basic view of response time and throughput, it doesn't show failures, nor how the server responds as load increases. And let's face it, it's just plain ugly. Enter Matplotlib, a beautiful (though complex) plotting tool written in Python.
If you've ever used JMeter, you know it's an awesome load testing tool. It also comes with a built-in graph listener, which allows you to watch JMeter do, well... something.
While this gives a basic view of response time and throughput, it doesn't show failures, nor how the server responds as load increases. And let's face it, it's just plain ugly.
Enter Matplotlib, a beautiful (though complex) plotting tool written in Python.
Box plots for response time are shown in green, throughput is in blue, and 50x errors are plotted as red X's. The script assumes a few things:
- You have a series of CSV files sampled with different thread counts.
- The input files are named
N-blah-blah.csv, where N is the number of threads. The file names are taken as command-line arguments. - Your CSV report contains the follow fields at a minimum: label, elapsed, and timeStamp. The results are grouped by label (a name you assign to each JMeter sampler), so each sampler produces a separate plot.
- And of course, that you have python and Matplotlib. If you are on OS X, the easiest way to install it is via MacPorts.
Stay tuned for the next article on the JMX file.
Sample plots
Click an image for a larger view.
Source code
#!/opt/local/bin/python2.6 # No copyright - https://creativecommons.org/publicdomain/zero/1.0/ from pylab import * import numpy as na import matplotlib.font_manager import csv import sys elapsed = {} timestamps = {} starttimes = {} errors = {} # Parse the CSV files for file in sys.argv[1:]: threads = int(file.split('-')[0]) for row in csv.DictReader(open(file)): if (not row['label'] in elapsed): elapsed[row['label']] = {} timestamps[row['label']] = {} starttimes[row['label']] = {} errors[row['label']] = {} if (not threads in elapsed[row['label']]): elapsed[row['label']][threads] = [] timestamps[row['label']][threads] = [] starttimes[row['label']][threads] = [] errors[row['label']][threads] = [] elapsed[row['label']][threads].append(int(row['elapsed'])) timestamps[row['label']][threads].append(int(row['timeStamp'])) starttimes[row['label']][threads].append(int(row['timeStamp']) - int(row['elapsed'])) if (row['success'] != 'true'): errors[row['label']][threads].append(int(row['elapsed'])) # Draw a separate figure for each label found in the results. for label in elapsed: # Transform the lists for plotting plot_data = [] throughput_data = [None] error_x = [] error_y = [] plot_labels = [] column = 1 for thread_count in sort(elapsed[label].keys()): plot_data.append(elapsed[label][thread_count]) plot_labels.append(thread_count) test_start = min(starttimes[label][thread_count]) test_end = max(timestamps[label][thread_count]) test_length = (test_end - test_start) / 1000 num_requests = len(timestamps[label][thread_count]) - len(errors[label][thread_count]) if (test_length > 0): throughput_data.append(num_requests / float(test_length)) else: throughput_data.append(0) for error in errors[label][thread_count]: error_x.append(column) error_y.append(error) column += 1 # Start a new figure fig = figure(figsize=(9, 6)) # Pick some colors palegreen = matplotlib.colors.colorConverter.to_rgb('#8CFF6F') paleblue = matplotlib.colors.colorConverter.to_rgb('#708DFF') # Plot response time ax1 = fig.add_subplot(111) ax1.set_yscale('log') bp = boxplot(plot_data, notch=0, sym='+', vert=1, whis=1.5) # Tweak colors on the boxplot plt.setp(bp['boxes'], color='g') plt.setp(bp['whiskers'], color='g') plt.setp(bp['medians'], color='black') plt.setp(bp['fliers'], color=palegreen, marker='+') # Now fill the boxes with desired colors numBoxes = len(plot_data) medians = range(numBoxes) for i in range(numBoxes): box = bp['boxes'][i] boxX = [] boxY = [] for j in range(5): boxX.append(box.get_xdata()[j]) boxY.append(box.get_ydata()[j]) boxCoords = zip(boxX,boxY) boxPolygon = Polygon(boxCoords, facecolor=palegreen) ax1.add_patch(boxPolygon) # Plot the errors if (len(error_x) > 0): ax1.scatter(error_x, error_y, color='r', marker='x', zorder=3) # Plot throughput ax2 = ax1.twinx() ax2.plot(throughput_data, 'o-', color=paleblue, linewidth=2, markersize=8) # Label the axis ax1.set_title(label) ax1.set_xlabel('Number of concurrent requests') ax2.set_ylabel('Requests per second') ax1.set_ylabel('Milliseconds') ax1.set_xticks(range(1, len(plot_labels) + 1, 2)) ax1.set_xticklabels(plot_labels[0::2]) fig.subplots_adjust(top=0.9, bottom=0.15, right=0.85, left=0.15) # Turn off scientific notation for Y axis ax1.yaxis.set_major_formatter(ScalarFormatter(False)) # Set the lower y limit to the match the first column ax1.set_ylim(ymin=bp['boxes'][0].get_ydata()[0]) # Draw some tick lines ax1.yaxis.grid(True, linestyle='-', which='major', color='grey') ax1.yaxis.grid(True, linestyle='-', which='minor', color='lightgrey') # Hide these grid behind plot objects ax1.set_axisbelow(True) # Add a legend line1 = Line2D([], [], marker='s', color=palegreen, markersize=10, linewidth=0) line2 = Line2D([], [], marker='o', color=paleblue, markersize=8, linewidth=2) line3 = Line2D([], [], marker='x', color='r', linewidth=0, markeredgewidth=2) prop = matplotlib.font_manager.FontProperties(size='small') figlegend((line1, line2, line3), ('Response Time', 'Throughput', 'Failures (50x)'), 'lower center', prop=prop, ncol=3) # Write the PNG file savefig(label)