[VOL-3348] Collecting CPU and Memory infos from running containers
Change-Id: Ib36e8edfb4e615e360628091ca6bf9520760d2c3
(cherry picked from commit 3ed8987187e1b7aa52a1315987223ddf6ae286d9)
diff --git a/requirements.txt b/requirements.txt
index 12d06fb..1744c4f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,3 +11,4 @@
git+https://github.com/zdw/robotframework-importresource@b81b87aabaee0594e966687b41e3674b866f28ee
cord-robot==2.1.1
virtualenv
+matplotlib==3.0.3
diff --git a/tests/scale/collect-result.py b/tests/scale/collect-result.py
index 1e15ab1..0624e3d 100644
--- a/tests/scale/collect-result.py
+++ b/tests/scale/collect-result.py
@@ -24,9 +24,11 @@
def cut_string(str):
return (str[:48] + "..") if len(str) > 50 else str
+
def format_key(str):
return str.replace("plot-", "").replace("-", " ")
+
def read_file(file, plot_folder):
# create element tree object
tree = ET.parse(file)
@@ -36,8 +38,7 @@
results = {}
-
- startTimer = 0
+ start_timer = 0
print(double_dash)
print("{:<50}{:>10}{:>15}".format("Test Name", "Status", "Duration (s)"))
print(double_dash)
@@ -49,7 +50,7 @@
s = datetime.strptime(start[:-4], "%Y%m%d %H:%M:%S")
e = datetime.strptime(end[:-4], "%Y%m%d %H:%M:%S")
diff = e - s
- time = startTimer + diff.seconds
+ time = start_timer + diff.seconds
print("{:<50}{:>10}{:>15}".format(cut_string(name), status.attrib["status"], time))
print(dash)
@@ -58,7 +59,7 @@
for tag in test.findall("./tags/tag"):
if "plot-" in tag.text:
results[tag.text] = time
- startTimer = time
+ start_timer = time
if not os.path.isdir(plot_folder):
os.mkdir(plot_folder)
diff --git a/tests/scale/sizing.py b/tests/scale/sizing.py
new file mode 100644
index 0000000..389bb1c
--- /dev/null
+++ b/tests/scale/sizing.py
@@ -0,0 +1,237 @@
+# Copyright 2017-present Open Networking Foundation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This tool collects CPU and Memory informations for each container in the VOLTHA stack
+
+# NOTE
+# Collecting the info for all containers in the same chart can be confusing,
+# we may want to create subcharts for the different groups, eg: infra, ONOS, core, adapters
+
+import argparse
+import requests
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from datetime import datetime
+import time
+
+EXCLUDED_POD_NAMES = [
+ "kube", "coredns", "kind", "grafana",
+ "prometheus", "tiller", "control-plane",
+ "calico", "nginx", "registry"
+]
+
+DATE_FORMATTER_FN = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
+
+
+def main(address, out_folder, since):
+ """
+ Query Prometheus and generate .pdf files for CPU and Memory consumption for each POD
+ :param address: string The address of the Prometheus instance to query
+ :param out_folder: string The output folder (where to save the .pdf files)
+ :param since: int When to start collection data (minutes in the past)
+ :return: void
+ """
+ time_delta = int(since) * 60
+ container_mem_query = "container_memory_usage_bytes{image!=''}[%sm]" % since
+ container_cpu_query = "rate(container_cpu_user_seconds_total{image!=''}[%sm]) * 100" % since
+
+ now = time.time()
+ cpu_params = {
+ "query": container_cpu_query,
+ "start": now - time_delta,
+ "end": now,
+ "step": "30",
+ }
+ r = requests.get("http://%s/api/v1/query_range" % address, cpu_params)
+ print("Downloading CPU info from: %s" % r.url)
+ container_cpu = r.json()["data"]["result"]
+ plot_cpu_consumption(remove_unwanted_containers(container_cpu),
+ output="%s/cpu.pdf" % out_folder)
+
+ r = requests.get("http://%s/api/v1/query" % address, {"query": container_mem_query})
+ print("Downloading Memory info from: %s" % r.url)
+ container_mem = r.json()["data"]["result"]
+ plot_memory_consumption(remove_unwanted_containers(container_mem),
+ output="%s/memory.pdf" % out_folder)
+
+
+def plot_cpu_consumption(containers, output=None):
+
+ plt.figure('cpu')
+ fig, ax = plt.subplots()
+ ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
+ ax.xaxis_date()
+ fig.autofmt_xdate()
+
+ plt.title("CPU Usage per POD")
+ plt.xlabel("Timestamp")
+ plt.ylabel("% used")
+
+ for c in containers:
+ name = c["metric"]["pod_name"]
+ data = c["values"]
+
+ dates = [datetime.fromtimestamp(x[0]) for x in data]
+
+ values = [float(x[1]) for x in data]
+
+ plt.plot(dates, values, label=name, lw=2, color=get_line_color(name))
+ # plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))
+
+ plt.legend(loc='upper left')
+
+ fig = plt.gcf()
+ fig.set_size_inches(20, 11)
+
+ plt.savefig(output)
+
+
+def plot_memory_consumption(containers, output=None):
+ plt.figure("memory")
+ fig, ax = plt.subplots()
+ ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
+ ax.xaxis_date()
+ fig.autofmt_xdate()
+ plt.title("Memory Usage")
+ plt.xlabel("Timestamp")
+ plt.ylabel("MB")
+
+ for c in containers:
+ name = c["metric"]["pod_name"]
+ data = c["values"]
+
+ dates = [datetime.fromtimestamp(x[0]) for x in data]
+ values = [bytesto(float(x[1]), "m") for x in data]
+
+ plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))
+
+ plt.legend(loc='upper left')
+
+ fig = plt.gcf()
+ fig.set_size_inches(20, 11)
+
+ plt.savefig(output)
+
+
+def remove_unwanted_containers(cpus):
+ res = []
+ for c in cpus:
+ if "pod_name" in c["metric"]:
+
+ pod_name = c["metric"]["pod_name"]
+ container_name = c["metric"]["name"]
+
+ if any(x in pod_name for x in EXCLUDED_POD_NAMES):
+ continue
+
+ if "k8s_POD" in container_name:
+ # this is the kubernetes POD controller, we don't care about it
+ continue
+
+ # if "_0" not in container_name:
+ # # this is something with the ONOS chart that is weird (each POD is reported 3 times)
+ # continue
+
+ res.append(c)
+ else:
+ continue
+
+ return res
+
+
+def get_line_color(container_name):
+ colors = {
+ "bbsim0": "#884EA0",
+ "bbsim1": "#9B59B6",
+ "bbsim-sadis-server": "#D2B4DE",
+ "onos-atomix-0": "#85C1E9",
+ "onos-atomix-1": "#7FB3D5",
+ "onos-atomix-2": "#3498DB",
+ "onos-onos-classic-0": "#1A5276",
+ "onos-onos-classic-1": "#1B4F72",
+ "onos-onos-classic-2": "#154360",
+ "etcd-0": "#7D6608",
+ "etcd-1": "#9A7D0A",
+ "etcd-2": "#B7950B",
+ "open-olt-voltha-adapter-openolt": "#7E5109",
+ "open-onu-voltha-adapter-openonu-0": "#6E2C00",
+ "open-onu-voltha-adapter-openonu-1": "#873600",
+ "open-onu-voltha-adapter-openonu-2": "#A04000",
+ "open-onu-voltha-adapter-openonu-3": "#BA4A00",
+ "open-onu-voltha-adapter-openonu-4": "#D35400",
+ "open-onu-voltha-adapter-openonu-5": "#D35400",
+ "open-onu-voltha-adapter-openonu-6": "#E59866",
+ "open-onu-voltha-adapter-openonu-7": "#EDBB99",
+ "kafka-0": "#4D5656",
+ "kafka-1": "#5F6A6A",
+ "kafka-2": "#717D7E",
+ "kafka-zookeeper-0": "#839192",
+ "kafka-zookeeper-1": "#95A5A6",
+ "kafka-zookeeper-2": "#717D7E",
+ "radius": "#82E0AA",
+ "voltha-voltha-ofagent": "#641E16",
+ "voltha-voltha-rw-core": "#7B241C",
+ }
+
+ if container_name in colors:
+ return colors[container_name]
+ elif "openolt" in container_name:
+ return colors["open-olt-voltha-adapter-openolt"]
+ elif "ofagent" in container_name:
+ return colors["voltha-voltha-ofagent"]
+ elif "rw-core" in container_name:
+ return colors["voltha-voltha-rw-core"]
+ elif "bbsim0" in container_name:
+ return colors["bbsim0"]
+ elif "bbsim1" in container_name:
+ return colors["bbsim1"]
+ elif "bbsim-sadis-server" in container_name:
+ return colors["bbsim-sadis-server"]
+ elif "radius" in container_name:
+ return colors["radius"]
+ else:
+ return "black"
+
+
+def get_diff(data):
+ return [x - data[i - 1] for i, x in enumerate(data)][1:]
+
+
+def bytesto(b, to, bsize=1024):
+ """convert bytes to megabytes, etc.
+ sample code:
+ print('mb= ' + str(bytesto(314575262000000, 'm')))
+ sample output:
+ mb= 300002347.946
+ """
+
+ a = {'k': 1, 'm': 2, 'g': 3, 't': 4, 'p': 5, 'e': 6}
+ r = float(b)
+ for i in range(a[to]):
+ r = r / bsize
+
+ return r
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(prog="sizing")
+ parser.add_argument("-a", "--address", help="The address of the Prometheus instance we're targeting",
+ default="127.0.0.1:31301")
+ parser.add_argument("-o", "--output", help="Where to output the generated files",
+ default="plots")
+ parser.add_argument("-s", "--since", help="When to start sampling the data (in minutes before now)",
+ default=10)
+
+ args = parser.parse_args()
+ main(args.address, args.output, args.since)