added results for updated tests

results_statistics.py

@@ -2,27 +2,70 @@ from statistics import median, stdev
 
 results = {}
 
+# Data size is 250mb
+
 # Read is how we call the process of reading all the data to calculate the
 # sum of a column.
-hot_cache_read = {'name': 'Hot cache - read'}
-hot_cache_read['data'] = [0.262673139572, 0.277920007706, 0.335688829422, 0.263830900192, 0.265268802643]
 
-cold_cache_read = {'name': 'Cold cache - read'}
-#cold_cache_read['data'] = [0.843955993652, 0.802474975586, 0.990427970886, 0.795747995377, 0.807102918625]
-cold_cache_read['data'] = [1.63526105881, 2.28860187531, 1.74772405624, 1.64203691483, 1.85263895988]
+# --------- FORMAT 0 ---------
+
+# --------- Pandas -----------
+hot_cache_read_0_pandas = {'name': 'Format 0 - Pandas - Hot cache - read'}
+hot_cache_read_0_pandas['data'] = [0.268836975098, 0.280472040176, 0.289081096649, 0.297116041183, 0.273966789246]
+
+cold_cache_read_0_pandas = {'name': 'Format 0 - Cold cache - read'}
+cold_cache_read_0_pandas['data'] = [63.5034000874, 66.1114599705, 64.4692680836, 62.5943498611, 57.514384985]
+
+# --------- NumPy -----------
+
+hot_cache_read_0_numpy = {'name': 'Format 0 - NumPy - Hot cache - read'}
+hot_cache_read_0_numpy['data'] = [0.0165619850159, 0.0179328918457, 0.0160620212555, 0.0203459262848, 0.017126083374]
+
+cold_cache_read_0_numpy = {'name': 'Format 0 - NumPy - Cold cache - read'}
+cold_cache_read_0_numpy['data'] = [57.818944931, 66.3065798283, 67.2626810074, 68.1461188793, 64.0724961758]
+
+# --------- FORMAT 1 ---------
+
+# --------- Pandas -----------
+hot_cache_read_1_pandas = {'name': 'Format 1 - Pandas - Hot cache - read'}
+hot_cache_read_1_pandas['data'] = [0.196627855301, 0.205650091171, 0.209545850754, 0.288589000702, 0.215779066086]
+
+cold_cache_read_1_pandas = {'name': 'Format 1 - Cold cache - read'}
+cold_cache_read_1_pandas['data'] = [65.1305730343, 65.0622851849, 63.2642118931, 62.5943498611, 67.4575021267]
+
+# --------- NumPy -----------
+
+hot_cache_read_1_numpy = {'name': 'Format 1 - NumPy - Hot cache - read'}
+hot_cache_read_1_numpy['data'] = [0.0176479816437, 0.016725063324, 0.0190989971161, 0.0153241157532, 0.0148088932037]
+
+cold_cache_read_1_numpy = {'name': 'Format 1 - NumPy -Cold cache - read'}
+cold_cache_read_1_numpy['data'] = [62.6579291821, 66.3065798283, 66.8456640244, 62.6478569508, 61.6572990417]
+
+# -------- MariaDB ----------
 
 mysql_hot_cache_read = {'name': 'MySQL hot cache - read'}
-mysql_hot_cache_read['data'] = [0.15, 0.14, 0.15, 0.15, 0.15]
+mysql_hot_cache_read['data'] = [0.42, 0.50, 0.44, 0.48, 0.47]
 
 mysql_cold_cache_read = {'name': 'MySQL cold cache - read'}
-mysql_cold_cache_read['data'] = [0.53, 0.57, 0.39, 0.42, 0.46]
+mysql_cold_cache_read['data'] = [2.67, 2.47, 1.39, 3.04, 2.03]
 
 # Write is how much time wendelin.core spend to write all data into the storage.
 # It's not affected by cache.
-write = {'name': 'Write'}
-write['data'] = [25.7491948605, 25.300052166, 25.1220777035, 27.6381318569, 25.0087022781]
 
-samples = [hot_cache_read, cold_cache_read, mysql_hot_cache_read, mysql_cold_cache_read, write]
+# --------- FORMAT 0 ---------
+write_0 = {'name': 'Format 0 - Write'}
+write_0['data'] = [72.602396965, 65.1651449203, 70.1477439404, 64.4070370197, 65.0965659618]
+
+# --------- FORMAT 1 ---------
+write_1 = {'name': 'Format 1 - Write'}
+write_1['data'] = [69.884073019, 64.7676539421, 66.6788258553, 65.2601249218, 63.1648089886]
+
+samples = [hot_cache_read_0_numpy, hot_cache_read_0_pandas,
+           hot_cache_read_1_numpy, hot_cache_read_1_pandas,
+           cold_cache_read_0_numpy, cold_cache_read_0_pandas,
+           cold_cache_read_1_numpy, cold_cache_read_1_pandas,
+           mysql_hot_cache_read, mysql_cold_cache_read,
+           write_0, write_1]
 
 for key, statistic_function in [['average', median], ['stdev', stdev], ['max', max], ['min', min]]:
   for sample in samples: