gistfile1.txt

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   "cell_type": "markdown",
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   "source": [
    "<h1>Splunk Magic</h1>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"nb_heading\">\n",
    "<tr>\n",
    "<td>\n",
    "    Project: Savvi @ NBN<br/>\n",
    "    Written: 12/12/2015<br/>\n",
    "    Author: Serge Rogov<br/>\n",
    "    Security: <span class=\"nb_sec nb_public\">Public</span><br/>\n",
    "</td>\n",
    "<td style=\"width: 50%\">\n",
    "    \n",
    "    <div class=\"savvi-logo\" style=\"height: 4em; background-position: right 0px\"></div>\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "    no-repeat\n",
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       "}    \n",
       "\n",
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      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import io\n",
    "import httplib2\n",
    "from IPython.display import display, HTML \n",
    "import splunklib.results as results\n",
    "import splunklib.client\n",
    "import json\n",
    "import IPython.display\n",
    "from IPython.core.magic import (register_line_magic, \n",
    "                                register_cell_magic)\n",
    "import qgrid2 as qgrid\n",
    "\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "with open('custom_html.html', 'r') as f:\n",
    "    custom_html = f.read()\n",
    "display(HTML(custom_html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from splunk_nb import *\n",
    "if sys.version_info[0] < 3:\n",
    "    from StringIO import StringIO\n",
    "else:\n",
    "    from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('splunk-auth-yong', 'r') as f:\n",
    "    #TODO SR: encrypt splunk auth? \n",
    "    splunk_auth = json.loads(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "service = splunklib.client.connect(autologin=True, **splunk_auth) #app=\"apm_snpm\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def replace_splunk_time(df):\n",
    "    '''Converts `nb_epoch` into `_time` as datetime64\n",
    "        `nb_epoch=_time` must be supplied by the query\n",
    "\n",
    "    '''\n",
    "    if ('nb_epoch' not in df.columns):\n",
    "        return\n",
    "    \n",
    "    df['_time'] = pd.to_numeric(df['nb_epoch'], errors='coerce').astype('datetime64[s]')\n",
    "    df.drop('nb_epoch', 1, inplace=True)\n",
    "\n",
    "class SplunkQuery:\n",
    "    job = None\n",
    "    search_string = None\n",
    "    def __init__(self, search_string):\n",
    "        if not search_string.startswith('search '):\n",
    "            search_string = 'search ' + search_string + ' | eval nb_epoch=_time | fields - _time'\n",
    "        self.search_string = search_string\n",
    "        \n",
    "    def _dispatch_query(self):\n",
    "        self.job = service.jobs.create(self.search_string, **{\"exec_mode\": \"normal\", \n",
    "                                      \"earliest_time\": '-10y', \n",
    "                                      \"latest_time\": '-0min',\n",
    "                                      \"output_mode\": \"csv\",\n",
    "                                      \"preview\": True,\n",
    "                                      \"maxEvents\": 0})\n",
    "    \n",
    "    def _await(self):\n",
    "        while True:\n",
    "            self.job.refresh()\n",
    "            if self.job[\"isDone\"] == \"1\":\n",
    "                break\n",
    "            time.sleep(1)\n",
    "    \n",
    "    def _report_progress(self):\n",
    "        #publish progress (stdout, NB)\n",
    "        pass\n",
    "    \n",
    "    def _await_with_progress(self):\n",
    "        print \"waiting\"\n",
    "        while True:\n",
    "            self.job.refresh()\n",
    "            if self.job[\"isDone\"] == \"1\":\n",
    "                break\n",
    "            time.sleep(1)\n",
    "        print \"done\"\n",
    "        \n",
    "    def _df_postprocess(self, df):\n",
    "        replace_splunk_time(df)\n",
    "        return df\n",
    "        \n",
    "    def _get_results_legacy(self):\n",
    "        \"\"\"Fetches one page of results using the offically recommended\n",
    "        approach. This method is SLOW.\n",
    "         \n",
    "            Args:\n",
    "                offset: start offset\n",
    "                count: number of results to return\n",
    "        \"\"\"\n",
    "        job = self.job\n",
    "        resultCount = job[\"resultCount\"]  # Number of results this job returned\n",
    "        offset = 0;                       # Start at result 0\n",
    "        count = 100;                       # Get sets of 10 results at a time\n",
    "        items = []\n",
    "        \n",
    "        while (offset < int(resultCount)):\n",
    "            kwargs_paginate = {\"count\": count,\n",
    "                        \"offset\": offset}\n",
    "            # Get the search results and display them\n",
    "            blocksearch_results = job.preview(output_mode=\"csv\", **kwargs_paginate)\n",
    "\n",
    "            for result in results.ResultsReader(blocksearch_results):\n",
    "                items.append(result)\n",
    "            offset += count\n",
    "        df = pd.DataFrame(items)\n",
    "        df = self._df_postprocess(df)\n",
    "        return df\n",
    "    \n",
    "    def get_preview(self):\n",
    "        self.job.refresh()\n",
    "        buf = StringIO()\n",
    "        job = self.job\n",
    "        self.buf=buf\n",
    "        \n",
    "        if (self.job['dispatchState']=='PARSING'):\n",
    "            return None #haven't received a resultPreviewCount yet TODO: backport to other cases\n",
    "        resultCount = int(self.job['resultPreviewCount'])\n",
    "        if (resultCount==0):\n",
    "            return None #no preview yet\n",
    "        \n",
    "        offset = 0\n",
    "        page_count = 1000\n",
    "        \n",
    "        while (offset < resultCount):\n",
    "            kwargs_paginate = {\"count\": page_count,\n",
    "                        \"offset\": offset}\n",
    "            \n",
    "            searchresults = job.preview(output_mode=\"csv\", **kwargs_paginate).read()  \n",
    "            \n",
    "            #suppress the CSV header on pages other than the first\n",
    "            if (offset == 0):\n",
    "                 buf.write(searchresults)\n",
    "            else:\n",
    "                 buf.write(searchresults[searchresults.find('\\n'):])\n",
    "            offset+=page_count\n",
    "        \n",
    "        buf.seek(0)\n",
    "        \n",
    "        df = pd.read_csv(buf)\n",
    "        df = self._df_postprocess(df)\n",
    "        return df\n",
    "    \n",
    "    def display_messages(self):\n",
    "        classes = {'info': 'nb_info', 'fatal': 'nb_error', 'error': 'nb_error'}\n",
    "        html = ''\n",
    "        for k, v in self.job.messages.iteritems():\n",
    "            line = '<div class=\"nb_message {classname}\">{message}</div>'.format(classname=classes[k], message=v[0])\n",
    "            html = html + line\n",
    "        display(HTML(html))\n",
    "    \n",
    "    def _get_results_page_fast(self, offset, count):\n",
    "        \"\"\"Fetches one page of results\n",
    "         \n",
    "            Args:\n",
    "                offset: start offset\n",
    "                count: number of results to return\n",
    "        \"\"\"\n",
    "        #TODO: uses `service` which is in global scope. Refactor\n",
    "        buf = StringIO()\n",
    "        self.buf=buf\n",
    "        sid = self.job['sid']\n",
    "        myhttp = httplib2.Http(disable_ssl_certificate_validation=True)\n",
    "        myhttp.add_credentials(service.username, service.password)\n",
    "        url = '/services/search/jobs/{0}/results?output_mode=csv&&offset={1}&count={2}'.format(sid, offset, count)\n",
    "        baseurl = str(service.authority)\n",
    "        searchresults = myhttp.request(baseurl + url, 'GET')[1]        \n",
    "        buf.write(searchresults)\n",
    "        \n",
    "        buf.seek(0)\n",
    "        df = pd.read_csv(buf)\n",
    "        df = self._df_postprocess(df)\n",
    "        return df\n",
    "    \n",
    "    def _get_results_fast_full(self, page_count=50000):\n",
    "        \"\"\"Fetches entire result set quickly\n",
    "         \n",
    "            Args:\n",
    "                offset: start offset\n",
    "                page_count: maximum number of results per page (default splunk limit is 50k)\n",
    "            \n",
    "            Notes:\n",
    "                Not sure which Splunk setting dictates maximum number of\n",
    "                results returned (page count). Ideally should identify it\n",
    "                and dynamically read via SDK\n",
    "        \"\"\"\n",
    "        #TODO: uses `service` which is in global scope. Refactor\n",
    "        buf = StringIO()\n",
    "        self.buf=buf\n",
    "        sid = self.job['sid']\n",
    "        resultCount = int(self.job['resultCount'])\n",
    "        \n",
    "        myhttp = httplib2.Http(disable_ssl_certificate_validation=True)\n",
    "        myhttp.add_credentials(service.username, service.password)\n",
    "        \n",
    "        offset = 0\n",
    "        \n",
    "        while (offset < resultCount):\n",
    "            url = '/services/search/jobs/{0}/results?output_mode=csv&&offset={1}&count={2}'.format(\n",
    "                sid, offset, page_count)\n",
    "            \n",
    "            baseurl = str(service.authority)\n",
    "            searchresults = myhttp.request(baseurl + url, 'GET')[1]   \n",
    "            \n",
    "            #suppress the CSV header on pages other than the first\n",
    "            if (offset == 0):\n",
    "                 buf.write(searchresults)\n",
    "            else:\n",
    "                 buf.write(searchresults[searchresults.find('\\n'):])\n",
    "            offset+=page_count\n",
    "        \n",
    "        buf.seek(0)\n",
    "        df = pd.read_csv(buf)\n",
    "        df = self._df_postprocess(df)\n",
    "        return df\n",
    "    \n",
    "        \n",
    "        \n",
    "    def execute(self, **kwargs):\n",
    "        \"\"\"Executes the query\n",
    "         \n",
    "            Args:\n",
    "                TODO: add args\n",
    "        \"\"\"\n",
    "        self._dispatch_query()\n",
    "        #self._await_with_progress()\n",
    "        return\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_blocking(query):\n",
    "    global last_job\n",
    "    x = SplunkQuery(query)\n",
    "    last_job = x\n",
    "    x.execute()\n",
    "    x._await()\n",
    "    return x._get_results_fast_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preview_kernel(df):\n",
    "    chart=df.set_index('_time')['count'].astype('float')\n",
    "    plt.gca().cla() \n",
    "    chart.plot()\n",
    "    IPython.display.clear_output(wait=True)\n",
    "    IPython.display.display(plt.gcf()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_preview(query):\n",
    "    global last_job\n",
    "    x = SplunkQuery(query)\n",
    "    last_job = x\n",
    "    x.execute()\n",
    "    while(x.job.is_done() == False):\n",
    "        \n",
    "        d = x.get_preview()\n",
    "        if (d is None):\n",
    "            IPython.display.clear_output(wait=True)\n",
    "            print \"waiting\"\n",
    "            sys.stdout.flush()\n",
    "            continue\n",
    "        \n",
    "        preview_kernel(d)\n",
    "        \n",
    "        time.sleep(1.0)\n",
    "    \n",
    "    IPython.display.clear_output(wait=True)\n",
    "    x.display_messages()\n",
    "    print \"Done!\"\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_preview(query):\n",
    "    global last_job\n",
    "    x = SplunkQuery(query)\n",
    "    last_job = x\n",
    "    first_results = True\n",
    "    x.execute()\n",
    "    while(x.job.is_done() == False):\n",
    "        \n",
    "        d = x.get_preview()\n",
    "        if (d is None):\n",
    "            IPython.display.clear_output(wait=True)\n",
    "            print x.job['dispatchState']\n",
    "            sys.stdout.flush()\n",
    "            time.sleep(1.0)\n",
    "            continue\n",
    "        else:\n",
    "            if (first_results):\n",
    "                grid = qgrid.QGridWidget(df=d)\n",
    "                display(grid)\n",
    "            first_results = False\n",
    "        \n",
    "        grid.df = d\n",
    "    \n",
    "        #preview_kernel(d)\n",
    "        \n",
    "        time.sleep(1.0)\n",
    "    \n",
    "    IPython.display.clear_output(wait=False)\n",
    "    x.display_messages()\n",
    "    print \"Done!\"\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@register_cell_magic\n",
    "def splunk(line, cell):\n",
    "    query = cell\n",
    "    if('preview=True' in line):\n",
    "        run_preview(query)\n",
    "    else:\n",
    "        return run_blocking(query)\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = run_preview('source=\"megadump_60.tgz:*\" earliest=\"11/30/2015:20:00:00\" | timechart span=4h avg(max_latency) as count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%splunk preview=True\n",
    "source=\"megadump_60.tgz:*\" earliest=\"12/04/2015:20:00:00\" | timechart span=4h avg(max_latency) as count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=last_job._get_results_fast_full()\n",
    "df.set_index('_time')['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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