s3-performance

In [3]:
%matplotlib inline
import matplotlib.pyplot as plt # side-stepping mpl backend
import json
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In [4]:
f = open("/Users/chris/Projects/openstack/underlay_dev2/scripts/s3bench/output.1.json")
data = json.load(f)
In [15]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["download"])
Out[15]:
[<matplotlib.lines.Line2D at 0x10f0d6278>]
In [16]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["upload"])
Out[16]:
[<matplotlib.lines.Line2D at 0x10f2d9c50>]
In [18]:
sum(data["upload"])/len(data["upload"])/1000/1000
Out[18]:
91.1755588464
In [19]:
sum(data["download"])/len(data["download"])/1000/1000
Out[19]:
76.31880991468455
In [20]:
max(data["download"])/1000/1000
Out[20]:
8470.063259999999
In [21]:
max(data["upload"])/1000/1000
Out[21]:
28842.349028
In [22]:
f = open("/Users/chris/Projects/openstack/underlay_dev2/scripts/s3bench/output.2.json")
data = json.load(f)
In [23]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["download"])
Out[23]:
[<matplotlib.lines.Line2D at 0x10e89c160>]
In [24]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["upload"])
Out[24]:
[<matplotlib.lines.Line2D at 0x10f5a2b00>]
In [28]:
f = open("/Users/chris/Projects/openstack/underlay_dev2/scripts/s3bench/output.3.json")
data = json.load(f)
In [29]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["download"])
Out[29]:
[<matplotlib.lines.Line2D at 0x10f77f470>]
In [30]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["upload"])
Out[30]:
[<matplotlib.lines.Line2D at 0x10fa77c88>]
In [31]:
f = open("/Users/chris/Projects/openstack/underlay_dev2/scripts/s3bench/output.4.json")
data = json.load(f)
In [32]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["download"])
Out[32]:
[<matplotlib.lines.Line2D at 0x10f98e9e8>]
In [33]:
plt.xlabel("probe")
plt.ylabel("response / nanoseconds")
plt.plot(data["upload"])
Out[33]:
[<matplotlib.lines.Line2D at 0x10fd683c8>]
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