diff options
author | Sridhar Rao <sridhar.rao@spirent.com> | 2019-08-07 08:39:04 +0000 |
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committer | Gerrit Code Review <gerrit@opnfv.org> | 2019-08-07 08:39:04 +0000 |
commit | 391e5ceb8958eff1f108cc2d589dd3a11c7c48a2 (patch) | |
tree | 9433aef428e9c4a27fdf16ad0f5edfda4fb8efdd /tools/docker/results/notebooks | |
parent | d835dbe3fd144c2144669cdf31a96263be21ab51 (diff) | |
parent | d691cc89e106d710f4d36bc3998501415588e2e1 (diff) |
Merge "Docker: VSPERF Results Container."
Diffstat (limited to 'tools/docker/results/notebooks')
-rw-r--r-- | tools/docker/results/notebooks/testresult-analysis.ipynb | 784 |
1 files changed, 784 insertions, 0 deletions
diff --git a/tools/docker/results/notebooks/testresult-analysis.ipynb b/tools/docker/results/notebooks/testresult-analysis.ipynb new file mode 100644 index 00000000..a7e9335c --- /dev/null +++ b/tools/docker/results/notebooks/testresult-analysis.ipynb @@ -0,0 +1,784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hide_input": true + }, + "source": [ + "# OPNFV VSPERF\n", + "# Beyond Performance Metrics: Towards Causation Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### sridhar.rao@spirent.com and acm@research.att.com" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from graphviz import Digraph\n", + "import collections\n", + "import glob\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the results to analyze: \n", + "Getting Latest one, if ``directory_to_download`` is empty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "import paramiko\n", + "import tarfile\n", + "import os\n", + "from stat import S_ISDIR\n", + "RECV_BYTES = 4096\n", + "hostname = '10.10.120.24'\n", + "port = 22\n", + "uname='opnfv'\n", + "pwd='opnfv' \n", + "stdout_data = []\n", + "stderr_data = []\n", + "client = paramiko.Transport((hostname, port))\n", + "client.connect(username=uname, password=pwd)\n", + "session = client.open_channel(kind='session')\n", + "directory_to_download = ''\n", + "\n", + "session.exec_command('ls /tmp | grep results')\n", + "if not directory_to_download:\n", + " while True:\n", + " if session.recv_ready():\n", + " stdout_data.append(session.recv(RECV_BYTES))\n", + " if session.recv_stderr_ready():\n", + " stderr_data.append(session.recv_stderr(RECV_BYTES))\n", + " if session.exit_status_ready():\n", + " break\n", + " if stdout_data:\n", + " line = stdout_data[0]\n", + " filenames = line.decode(\"utf-8\").rstrip('\\n').split('\\n')\n", + " filenames = sorted(filenames)\n", + " latest = filenames[-1]\n", + " directory_to_download = os.path.join('/tmp', latest).replace(\"\\\\\",\"/\")\n", + " print(directory_to_download)\n", + "stdout_data = []\n", + "stderr_data = []\n", + "if directory_to_download:\n", + " # zip the collectd results to make the download faster\n", + " zip_command = 'sudo -S tar -czvf '+ directory_to_download + '/collectd.tar.gz -C ' + directory_to_download + '/csv .'\n", + " session = client.open_channel(kind='session')\n", + " session.get_pty()\n", + " session.exec_command(zip_command)\n", + " while True:\n", + " if session.recv_ready():\n", + " stdout_data.append(session.recv(RECV_BYTES))\n", + " if session.recv_stderr_ready():\n", + " stderr_data.append(session.recv_stderr(RECV_BYTES))\n", + " if session.exit_status_ready():\n", + " break\n", + " if stderr_data:\n", + " print(stderr_data[0])\n", + " if stdout_data:\n", + " print(stdout_data[0])\n", + "\n", + " # Begin the actual downlaod\n", + " sftp = paramiko.SFTPClient.from_transport(client)\n", + " def sftp_walk(remotepath):\n", + " path=remotepath\n", + " files=[]\n", + " folders=[]\n", + " for f in sftp.listdir_attr(remotepath):\n", + " if S_ISDIR(f.st_mode):\n", + " folders.append(f.filename)\n", + " else:\n", + " files.append(f.filename)\n", + " if files:\n", + " yield path, files\n", + " # Filewise download happens here\n", + " for path,files in sftp_walk(directory_to_download):\n", + " for file in files:\n", + " remote = os.path.join(path,file).replace(\"\\\\\",\"/\")\n", + " local = os.path.join('./results', file).replace(\"\\/\",\"/\")\n", + " sftp.get(remote, local)\n", + "# Untar the collectd results if we got it.\n", + "path = os.path.join('./results', 'collectd.tar.gz')\n", + "if os.path.exists(path):\n", + " tar = tarfile.open(path)\n", + " tar.extractall()\n", + " tar.close()\n", + "# Ready to work with downloaded data, close the session and client.\n", + "session.close()\n", + "client.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "strings = ('* OS:', '* Kernel Version:', '* Board:', '* CPU:', '* CPU cores:',\n", + " '* Memory:', '* Virtual Switch Set-up:',\n", + " '* Traffic Generator:','* vSwitch:', '* DPDK Version:', '* VNF:')\n", + "filename = os.path.basename(glob.glob('./results/result*.rst')[0])\n", + "info_dict = {}\n", + "with open(os.path.join('./results', filename), 'r') as file:\n", + " for line in file:\n", + " if any(s in line for s in strings):\n", + " info_dict[line.split(':', 1)[0]] = line.split(':', 1)[1].rstrip()\n", + "df = pd.DataFrame.from_dict(info_dict, orient='index', columns=['Value'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understand the configuration used for the test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filename = os.path.basename(glob.glob('./results/vsperf*.conf')[0])\n", + "file = os.path.join('./results', filename)\n", + "with open(file, 'r') as f:\n", + " for line in f:\n", + " if line.startswith('TRAFFICGEN_DURATION'):\n", + " value = line.split('=')[1]\n", + " value = value.rstrip()\n", + " value = value.lstrip()\n", + " traffic_duration = int(value)\n", + " elif line.startswith('VSWITCH_PMD_CPU_MASK'):\n", + " value = line.split('=')[1]\n", + " value = value.rstrip()\n", + " pmd_cores_mask = value.lstrip()\n", + " elif line.startswith('GUEST_CORE_BINDING'):\n", + " value = line.split('=')[1]\n", + " value = value.rstrip()\n", + " value = value.lstrip()\n", + " guest_cores = value[1:-2]\n", + "\n", + "print(traffic_duration)\n", + "print(pmd_cores_mask)\n", + "print(guest_cores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OVS-Ports and Cores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "import collections\n", + "portcores = collections.OrderedDict()\n", + "chunks = []\n", + "current_chunk = []\n", + "file = os.path.join('./results', 'ovs-cores.log')\n", + "with open(file, 'r') as f:\n", + " for line in f:\n", + " if line.startswith('pmd') and current_chunk:\n", + " # if line starts with token and the current chunk is not empty\n", + " chunks.append(current_chunk[:]) # add not empty chunk to chunks\n", + " current_chunk = [] # make current chunk blank\n", + " # just append a line to the current chunk on each iteration\n", + " if \"port:\" in line or 'pmd' in line:\n", + " current_chunk.append(line)\n", + " chunks.append(current_chunk) # append the last chunk outside the loop\n", + "\n", + "core_ids = []\n", + "for ch in chunks:\n", + " port_id = ''\n", + " core_id = ''\n", + " for line in ch:\n", + " if 'pmd' in line:\n", + " core_id = line.split()[-1][:-1]\n", + " if core_id not in core_ids:\n", + " core_ids.append(core_id)\n", + " elif 'port:' in line:\n", + " port_id = line.split()[1]\n", + " if port_id and core_id:\n", + " if port_id not in portcores:\n", + " portcores[port_id] = core_id\n", + "\n", + "# import graphviz\n", + "from graphviz import Digraph\n", + "ps = Digraph(name='ovs-ports-cores', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n", + "with ps.subgraph(name=\"cluster_0\") as c:\n", + " c.node_attr.update(style='filled', color='green')\n", + " c.node('t0', 'TGen-Port-0')\n", + " c.node('t1', 'TGen-Port-1')\n", + " c.attr(label='TGEN')\n", + " c.attr(color='blue')\n", + "with ps.subgraph(name=\"cluster_1\") as c:\n", + " c.node_attr.update(style='filled', color='yellow')\n", + " c.node('v0', 'VNF-Port-0')\n", + " c.node('v1', 'VNF-Port-1')\n", + " c.attr(label='VNF')\n", + " c.attr(color='blue')\n", + " \n", + "with ps.subgraph(name='cluster_2') as c: \n", + " c.attr(label='OVS-DPDK')\n", + " c.attr(color='blue')\n", + " count = 0\n", + " for port, core in portcores.items():\n", + " id = 'o'+str(count)\n", + " c.node(id, port+'\\nCore-ID:'+ core)\n", + " count += 1\n", + " num = port[-1]\n", + " if 'dpdkvhost' in port:\n", + " ps.edge(id, 'v'+num)\n", + " else:\n", + " ps.edge(id, 't'+num)\n", + "\n", + "ps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dropped Packets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "portcores = collections.OrderedDict()\n", + "chunks = []\n", + "current_chunk = []\n", + "file = os.path.join('./results', 'ovs-cores.log')\n", + "with open(file, 'r') as f:\n", + " for line in f:\n", + " if line.startswith('pmd') and current_chunk:\n", + " # if line starts with token and the current chunk is not empty\n", + " chunks.append(current_chunk[:]) # add not empty chunk to chunks\n", + " current_chunk = [] # make current chunk blank\n", + " # just append a line to the current chunk on each iteration\n", + " if \"port:\" in line or 'pmd' in line:\n", + " current_chunk.append(line)\n", + " chunks.append(current_chunk) # append the last chunk outside the loop\n", + "\n", + "core_ids = []\n", + "for ch in chunks:\n", + " port_id = ''\n", + " core_id = ''\n", + " for line in ch:\n", + " if 'pmd' in line:\n", + " core_id = line.split()[-1][:-1]\n", + " if core_id not in core_ids:\n", + " core_ids.append(core_id)\n", + " elif 'port:' in line:\n", + " port_id = line.split()[1]\n", + " if port_id and core_id:\n", + " if port_id not in portcores:\n", + " portcores[port_id] = core_id\n", + "\n", + "ps = Digraph(name='ovs-dropped', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n", + "\n", + "def get_dropped(port_id):\n", + " # port_id = 'dpdk0'\n", + " if glob.glob('./pod12-node4/*'+port_id):\n", + " dirname = os.path.basename(glob.glob('./pod12-node4/*'+port_id)[0])\n", + " if dirname:\n", + " if glob.glob('./pod12-node4/'+dirname+ '/*dropped*'):\n", + " filename = os.path.basename(glob.glob('./pod12-node4/'+dirname+ '/*dropped*')[0])\n", + " if filename:\n", + " with open(os.path.join('./pod12-node4', dirname, filename), 'r') as f:\n", + " line = f.readlines()[-1]\n", + " fields = line.split(',')\n", + " return fields[1], fields[2]\n", + " return 'NA','NA'\n", + "\n", + "with ps.subgraph(name=\"cluster_0\") as c:\n", + " c.node_attr.update(style='filled', color='pink')\n", + " c.attr(label='OVS-DPDK')\n", + " c.attr(color='blue')\n", + " count = 0\n", + " for port, core in portcores.items():\n", + " id = 'o'+str(count)\n", + " rx,tx = get_dropped(port)\n", + " c.node(id, port+'\\nRX-Dropped:'+ rx + '\\nTX-Dropped:' + tx)\n", + " count += 1\n", + " num = port[-1]\n", + "ps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Live Results - T-Rex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "lines_seen = set() # holds lines already seen\n", + "outfile = open('./counts.dat', \"w\")\n", + "file = os.path.join('./results', 'trex-liveresults-counts.dat')\n", + "for line in open(file, \"r\"):\n", + " if line not in lines_seen: # not a duplicate\n", + " outfile.write(line)\n", + " lines_seen.add(line)\n", + "outfile.close()\n", + "tdf = pd.read_csv('./counts.dat')\n", + "print(tdf.columns)\n", + "ax = tdf.loc[(tdf.rx_port == 1)].plot(y='rx_pkts')\n", + "def highlight(indices,ax):\n", + " i=0\n", + " while i<len(indices):\n", + " ax.axvspan(indices[i][0], indices[i][1], facecolor='RED', edgecolor='BLUE', alpha=.2)\n", + " i+=1\n", + "\n", + "ind = 0\n", + "indv = tdf.ts[0]\n", + "ax.set_xlabel(\"Index\")\n", + "ax.set_ylabel('Count')\n", + "for i in range(len(tdf.ts)):\n", + " if tdf.ts[i] - indv > int(traffic_duration):\n", + " highlight([(ind, i)], ax)\n", + " ind = i\n", + " indv = tdf.ts[i]\n", + "highlight([(ind,i)], ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IRQ Latency Histogram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "file = os.path.join('./results', 'RUNirq.irq.log')\n", + "tdf = pd.read_csv(file)\n", + "tdf.columns\n", + "exclude = [' <1', ' < 5', ' < 10',' < 50', ' < 100', ' < 500', ' < 1000']\n", + "ax = tdf.loc[:, tdf.columns.difference(exclude)].plot(x=' number', xticks=tdf[' number'], figsize=(20,10))\n", + "ax.set_xlabel('Core #')\n", + "ax.set_ylabel('Count')\n", + "#tdf.plot(x='number')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample Collectd Metric Display - L3 Cache Occupancy in Bytes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "def cpumask2coreids(mask):\n", + " intmask = int(mask, 16)\n", + " i = 1\n", + " coreids = []\n", + " while (i < intmask):\n", + " if (i & intmask):\n", + " coreids.append(str(math.frexp(i)[-1]-1))\n", + " i = i << 1\n", + " return (coreids)\n", + "\n", + "vswitch_cpus = \"['2']\"\n", + "ps = Digraph(name='cpu-map', node_attr={'shape': 'box'}, edge_attr={'arrowhead':\"none\"})\n", + "with ps.subgraph(name=\"cluster_0\") as c:\n", + " c.node_attr.update(style='filled', color='pink')\n", + " c.attr(label='CPU-MAPPINGS')\n", + " c.attr(color='blue')\n", + " c.node('vscpus', 'vSwitch: \\n' + vswitch_cpus)\n", + " # vnf_cpus = cpumask2coreids(guest_cores)\n", + " c.node('vncpus', 'VNF: \\n' + guest_cores)\n", + " pmd_cpus = cpumask2coreids(pmd_cores_mask[1:-1])\n", + " c.node('pmcpus', 'PMDs: \\n' + str(pmd_cpus))\n", + "\n", + "ps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "# Path where collectd results are stored.\n", + "mypath = \"./pod12-node4\"\n", + "file_count = 0\n", + "cpu_names = []\n", + "for level1 in os.listdir(mypath):\n", + " if \"intel_rdt\" in level1:\n", + " l2path = os.path.join(mypath, level1)\n", + " for level2 in os.listdir(l2path):\n", + " if \"bytes\" in level2:\n", + " l3path = os.path.join(l2path, level2)\n", + " if file_count == 0:\n", + " file_count += 1\n", + " df = pd.read_csv(l3path)\n", + " nn = 'cpu-'+ level1[len('intel_rdt-'):]\n", + " # nn = 'cpu-'+ level1.split('-')[1]\n", + " cpu_names.append(nn)\n", + " # print(nn)\n", + " df.rename(columns={'value': nn}, inplace=True)\n", + " else:\n", + " file_count += 1\n", + " tdf = pd.read_csv(l3path)\n", + " nn = 'cpu-'+ level1[len('intel_rdt-'):]\n", + " cpu_names.append(nn)\n", + " tdf.rename(columns={'value': nn}, inplace=True)\n", + " df[nn] = tdf[nn] \n", + "\n", + "ax = df.plot(x='epoch', y=cpu_names)\n", + "ax.set_ylabel(\"MBytes\")\n", + "ax.set_xlabel('Time')\n", + "\n", + "\n", + " \n", + "# df = pd.read_csv()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Events " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "filename = os.path.basename(glob.glob('./results/vsperf-overall*.log')[0])\n", + "logfile = os.path.join('./results', filename)\n", + "linecnt = 0\n", + "times = {}\n", + "with open(logfile) as f:\n", + " for line in f:\n", + " line = line.strip('\\n')\n", + " if linecnt == 0:\n", + " times['Start-Test'] = line.split(\" : \")[0]\n", + " linecnt += 1\n", + " if 'Binding NICs' in line:\n", + " times['Binding-NICs'] = line.split(\" : \")[0]\n", + " if 'Starting traffic at' in line:\n", + " sline = line.split(\" : \")[1]\n", + " time = line.split(\" : \")[0]\n", + " speed = sline.split('at',1)[1]\n", + " times[speed] = time \n", + " elif 'Starting vswitchd' in line:\n", + " times['vSwitch-Start'] = line.split(\" : \")[0]\n", + " elif 'Starting ovs-vswitchd' in line:\n", + " times['ovsvswitch-start'] = line.split(\" : \")[0]\n", + " elif 'Adding Ports' in line:\n", + " times['Ports-Added'] = line.split(\" : \")[0]\n", + " elif 'Flows Added' in line:\n", + " times['Flows-Added'] = line.split(\" : \")[0]\n", + " elif 'send_traffic with' in line:\n", + " times['Traffic Start'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 1280' in line:\n", + " times['Traffic-Start-1280'] = line.split(\" : \")[0]\n", + " elif 'Starting qemu' in line:\n", + " times['VNF-Start'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 64' in line:\n", + " times['Traffic-Start-64'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 128' in line:\n", + " times['Traffic-Start-128'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 256' in line:\n", + " times['Traffic-Start-256'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 512' in line:\n", + " times['Traffic-Start-512'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 1024' in line:\n", + " times['Traffic-Start-1024'] = line.split(\" : \")[0]\n", + " elif 'l2 framesize 1518' in line:\n", + " times['Traffic-Start-1518'] = line.split(\" : \")[0]\n", + " elif 'dump flows' in line:\n", + " times['Traffic-End'] = line.split(\" : \")[0]\n", + " elif 'Wait for QEMU' in line:\n", + " times['VNF-Stop'] = line.split(\" : \")[0]\n", + " elif 'delete flow' in line:\n", + " times['flow-removed'] = line.split(\" : \")[0]\n", + " elif 'delete port' in line:\n", + " times['port-removed'] = line.split(\" : \")[0]\n", + " elif 'Killing ovs-vswitchd' in line:\n", + " times['vSwitch-Stop'] = line.split(\" : \")[0]\n", + "\n", + "times['Test-Stop'] = line.split(\" : \")[0]\n", + "#print(times)\n", + "ddf = pd.DataFrame.from_dict(times, orient='index', columns=['timestamp'])\n", + "names = ddf.index.values\n", + "dates = ddf['timestamp'].tolist()\n", + "datefmt=\"%Y-%m-%d %H:%M:%S,%f\"\n", + "dates = [datetime.strptime(ii, datefmt) for ii in dates]\n", + "# print(names)\n", + "# print(dates)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "import matplotlib.dates as mdates\n", + "from matplotlib import ticker\n", + "\n", + "levels = np.array([-5, 5, -3, 3, -1, 1])\n", + "fig, ax = plt.subplots(figsize=(40, 5))\n", + "\n", + "# Create the base line\n", + "start = min(dates)\n", + "stop = max(dates)\n", + "ax.plot((start, stop), (0, 0), 'k', alpha=.5)\n", + "\n", + "pos_list = np.arange(len(dates))\n", + "\n", + "# Iterate through releases annotating each one\n", + "for ii, (iname, idate) in enumerate(zip(names, dates)):\n", + " level = levels[ii % 6]\n", + " vert = 'top' if level < 0 else 'bottom'\n", + " ax.scatter(idate, 0, s=100, facecolor='w', edgecolor='k', zorder=9999)\n", + " # Plot a line up to the text\n", + " ax.plot((idate, idate), (0, level), c='r', alpha=.7)\n", + " # Give the text a faint background and align it properly\n", + " ax.text(idate, level, iname,\n", + " horizontalalignment='right', verticalalignment=vert, fontsize=14,\n", + " backgroundcolor=(1., 1., 1., .3))\n", + "ax.set(title=\"VSPERF Main Events\")\n", + "# Set the xticks formatting\n", + "ax.get_xaxis().set_major_locator(mdates.SecondLocator(interval=30))\n", + "ax.get_xaxis().set_major_formatter(mdates.DateFormatter(\"%M %S\"))\n", + "fig.autofmt_xdate()\n", + "plt.setp((ax.get_yticklabels() + ax.get_yticklines() +\n", + " list(ax.spines.values())), visible=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Current and old." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Current Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "import glob\n", + "filename = os.path.basename(glob.glob('./results/result*.csv')[0])\n", + "filename\n", + "tdf = pd.read_csv(os.path.join('./results', filename))\n", + "pkts = ['tx_frames', 'rx_frames']\n", + "fps = ['tx_rate_fps', 'throughput_rx_fps']\n", + "mbps = ['tx_rate_mbps', 'throughput_rx_mbps']\n", + "pcents = ['tx_rate_percent', 'throughput_rx_percent', 'frame_loss_percent']\n", + "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 12))\n", + "tdf.plot.bar(y= pkts,ax=axes[0,0])\n", + "tdf.plot.bar(y= fps,ax=axes[0,1])\n", + "tdf.plot.bar(y= mbps,ax=axes[1,0])\n", + "tdf.plot.bar(y= pcents,ax=axes[1,1])\n", + "current_pkt_size = str(tdf['packet_size'].iloc[-1])\n", + "current_rx_fps = str(tdf['throughput_rx_fps'].iloc[-1])\n", + "print(current_rx_fps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Current Result compares to Previous ones?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "import urllib\n", + "import json\n", + "import requests\n", + "#json_data = requests.get('http://testresults.opnfv.org/test/api/v1/results?project=vsperf').json()\n", + "json_data = requests.get('http://10.10.120.22:8000/api/v1/results?project=vsperf').json()\n", + "res = json_data['results']\n", + "df1 = pd.DataFrame(res)\n", + "sort_by_date = df1.sort_values('start_date')\n", + "details = df1['details'].apply(pd.Series)\n", + "details[current_pkt_size] = pd.to_numeric(pd.Series(details[current_pkt_size]))\n", + "# details.plot.bar(y = current_pkt_size)\n", + "details_cur_pkt = details[[current_pkt_size]].copy()\n", + "details_cur_pkt.loc[-1]= float(current_rx_fps)\n", + "details_cur_pkt.index = details_cur_pkt.index + 1 # shifting index\n", + "details_cur_pkt.sort_index(inplace=True) \n", + "ax = details_cur_pkt.plot.bar()\n", + "ax.set_ylabel(\"Frames per sec\")\n", + "ax.set_xlabel(\"Run Number\")\n", + "def highlight(indices,ax):\n", + " i=0\n", + " while i<len(indices):\n", + " ax.axvspan(indices[i]-0.5, indices[i]+0.5, facecolor='RED', edgecolor='none', alpha=.2)\n", + " i+=1\n", + "highlight([0], ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "array_of_dfs = []\n", + "for dirs in glob.glob('./pod12-node4/ovs_stats-vsperf*'):\n", + " dirname = os.path.basename(dirs)\n", + " if dirname:\n", + " port = dirname.split('.')[1]\n", + " if glob.glob('./pod12-node4/'+dirname+ '/*dropped*'):\n", + " full_path = glob.glob('./pod12-node4/'+dirname+ '/*dropped*')[0]\n", + " filename = os.path.basename(full_path)\n", + " if filename:\n", + " df = pd.read_csv(full_path)\n", + " df.rename(index=str, columns={\"rx\": port+\"-rx\" , \"tx\": port+\"-tx\"}, inplace=True)\n", + " df = df.drop(columns=['epoch'])\n", + " array_of_dfs.append(df)\n", + "master_df = pd.concat(array_of_dfs, axis=1, sort=True)\n", + "master_df.columns\n", + "\n", + "# get the correlation coefficient between the different columns\n", + "corr = master_df.iloc[:, 0:].corr()\n", + "arr_corr = corr.values\n", + "# mask out the top triangle\n", + "arr_corr[np.triu_indices_from(arr_corr)] = np.nan\n", + "fig, ax = plt.subplots(figsize=(18, 12))\n", + "sns.set(font_scale=3.0)\n", + "hm = sns.heatmap(arr_corr, cbar=True, vmin=-0.5, vmax=0.5,\n", + " fmt='.2f', annot_kws={'size': 20}, annot=True, \n", + " square=True, cmap=plt.cm.Reds)\n", + "ticks = np.arange(corr.shape[0]) + 0.5\n", + "ax.set_xticks(ticks)\n", + "ax.set_xticklabels(corr.columns, rotation=90, fontsize=20)\n", + "ax.set_yticks(ticks)\n", + "ax.set_yticklabels(corr.index, rotation=360, fontsize=20)\n", + "\n", + "ax.set_title('Heatmap')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "author": { + "@type": "Person", + "name": "Sridhar K. N. Rao", + "worksFor": { + "@type": "Organization", + "name": "Spirent Communications" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |