From 93250414c6ef77b69a0aca008ef28c778c48afe4 Mon Sep 17 00:00:00 2001
From: drebs <drebs@riseup.net>
Date: Wed, 13 Sep 2017 12:09:41 -0300
Subject: [test] add graph for sqlite blobs backend benchmark test

---
 .../benchmark/sqlite-blobs-backend/gen-graph.py    | 81 ++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100755 scripts/benchmark/sqlite-blobs-backend/gen-graph.py

(limited to 'scripts/benchmark/sqlite-blobs-backend/gen-graph.py')

diff --git a/scripts/benchmark/sqlite-blobs-backend/gen-graph.py b/scripts/benchmark/sqlite-blobs-backend/gen-graph.py
new file mode 100755
index 00000000..2bb948f1
--- /dev/null
+++ b/scripts/benchmark/sqlite-blobs-backend/gen-graph.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+
+# Plot bars comparing different implementations of mail pipeline.
+#
+# This script can be improved to account for arbitrary number of data sets, but
+# it is not doing it right now.
+
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+import re
+
+# make a prettier graph
+from mpltools import style
+style.use('ggplot')
+
+OUTPUT_FILENAME = 'blobs-sqlite-backend.png'
+
+graphs = [
+    '1_10000k',
+    '10_1000k',
+    '100_100k',
+    '1000_10k',
+]
+
+
+# the JSON structure returned by the following function is ugly, but the
+# original JSONs are even uglier, so this is here just to make the life of the
+# script easier.
+#
+# We want to have something like:
+#
+#   data[get/put][amount_size] = <stats>
+
+def get_data():
+    data = {}
+    for fname in ['get', 'put']:
+        data[fname] = {}
+        with open('data/%s.json' % fname) as f:
+            d = json.loads(f.read())
+            benchmarks = d['benchmarks']
+            for item in benchmarks:
+                name = re.sub('^[^1]+', '', item['name'])
+                data[fname][name] = item['stats']
+    return data
+
+
+def plot_data(data):
+
+    N = 4
+
+    get_means = tuple([data['get'][graph]['mean'] for graph in graphs])
+    put_means = tuple([data['put'][graph]['mean'] for graph in graphs])
+
+    ind = np.arange(N)  # the x locations for the groups
+    width = 0.40        # the width of the bars
+
+    fig, ax = plt.subplots()
+    rects1 = ax.bar(ind, get_means, width)
+    rects2 = ax.bar(ind + width, put_means, width)
+
+    # add some text for labels, title and axes ticks
+    ax.set_ylabel('Time for operation (s)')
+    ax.set_xlabel('Amount and size of blobs')
+    ax.set_title('Blobs storage and retrieval time')
+    ax.set_xticks(ind + (0.5 * width))
+    ax.set_xticklabels(
+        tuple(map(lambda name: name.replace('_', ' x '), graphs)))
+
+    ax.legend(
+        (rects1[0], rects2[0]),
+        ('retrieval time', 'storage time'))
+    # ax.grid()
+
+    plt.savefig(OUTPUT_FILENAME)
+    # plt.show()
+
+
+if __name__ == '__main__':
+    data = get_data()
+    plot_data(data)
-- 
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