ERDDAP/ERDDAP_timing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some simple ERDDAP timing tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time how long it takes to get data via ERDDAP.  \n",
    "\n",
    "Here we are using the IOOS NERACOOS ERDDAP service at http://www.neracoos.org/erddap.  \n",
    "\n",
    "Lots of factors affect timing, as described at the end.  \n",
    "\n",
    "But for these tests it's pretty darn speedy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import urllib, json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try CSV response for 1 week of accelerometer (wave) data from NERACOOS Buoy B off Portland, ME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.csv?time,significant_wave_height&time>\"now-7days\"\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.csv?time,significant_wave_height&time>\"now-7days\"'\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 67 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "df_sb = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/+fNAfr5sX7pkxrp1QJ8+vvn6U1PN2LkTyMszITUVOHzYjKws58+H2WzGkiVA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\nHKu0+f13qci+8opcCl+8WNoUWrWy9Hv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luSfJpxc7KEmSJGl3vWYpQpJVwD3A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\nkqRe+D8S6VlDCQeaggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45a0bbfd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sb = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row \n",
    "df_sb.plot(figsize=(12,4),grid='on');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about 1 year of data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.csv?time,significant_wave_height&time>\"now-365days\"\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.csv?time,significant_wave_height&time>\"now-365days\"'\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 388 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "df_sb = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9a0zl+eKL85eXbPNgostkrge/fr36LrWKmJQk5bblloXLRyEoF3+vXHBZRpdl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\nRMrk/wEs+LOws3riigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45a0bbfe50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sb = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row \n",
    "df_sb.plot(figsize=(12,4),grid='on');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try JSON response -- might want to use this in a web app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.json?time,significant_wave_height&time>\"now-7days\"\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.neracoos.org/erddap/tabledap/B01_accelerometer_all.json?time,significant_wave_height&time>\"now-7days\"'\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 96.4 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "response = urllib.urlopen(url)\n",
    "data = json.loads(response.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response = urllib.urlopen(url)\n",
    "data = json.loads(response.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'table': {u'columnNames': [u'time', u'significant_wave_height'],\n",
       "  u'columnTypes': [u'String', u'float'],\n",
       "  u'columnUnits': [u'UTC', u'meters'],\n",
       "  u'rows': [[u'2017-01-18T17:00:00Z', 2.1363115],\n",
       "   [u'2017-01-18T17:30:00Z', 2.6868901],\n",
       "   [u'2017-01-18T18:00:00Z', 2.86435],\n",
       "   [u'2017-01-18T18:30:00Z', 3.0311515],\n",
       "   [u'2017-01-18T19:00:00Z', 2.9257133],\n",
       "   [u'2017-01-18T19:30:00Z', 2.902714],\n",
       "   [u'2017-01-18T20:00:00Z', 2.853798],\n",
       "   [u'2017-01-18T20:30:00Z', 3.1318264],\n",
       "   [u'2017-01-18T21:00:00Z', 3.4343703],\n",
       "   [u'2017-01-18T21:30:00Z', 3.4868062],\n",
       "   [u'2017-01-18T22:00:00Z', 2.6982548],\n",
       "   [u'2017-01-18T22:30:00Z', 3.3352795],\n",
       "   [u'2017-01-18T23:00:00Z', 3.1377385],\n",
       "   [u'2017-01-18T23:30:00Z', 2.984962],\n",
       "   [u'2017-01-19T00:00:00Z', 3.1228244],\n",
       "   [u'2017-01-19T00:30:00Z', 3.080198],\n",
       "   [u'2017-01-19T01:00:00Z', 2.9236872],\n",
       "   [u'2017-01-19T01:30:00Z', 3.1152282],\n",
       "   [u'2017-01-19T02:00:00Z', 3.0849724],\n",
       "   [u'2017-01-19T02:30:00Z', 2.8047462],\n",
       "   [u'2017-01-19T03:00:00Z', 2.7223408],\n",
       "   [u'2017-01-19T03:30:00Z', 2.8003414],\n",
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       "   [u'2017-01-25T00:30:00Z', 1.5193235],\n",
       "   [u'2017-01-25T01:00:00Z', 4.495218],\n",
       "   [u'2017-01-25T01:30:00Z', 4.7069545],\n",
       "   [u'2017-01-25T02:00:00Z', 4.723055],\n",
       "   [u'2017-01-25T02:30:00Z', 5.161412],\n",
       "   [u'2017-01-25T03:00:00Z', 4.509254],\n",
       "   [u'2017-01-25T03:30:00Z', 4.2276497],\n",
       "   [u'2017-01-25T04:00:00Z', 4.403115],\n",
       "   [u'2017-01-25T04:30:00Z', 4.653558],\n",
       "   [u'2017-01-25T05:00:00Z', 4.8003645],\n",
       "   [u'2017-01-25T05:30:00Z', 3.8106747],\n",
       "   [u'2017-01-25T06:00:00Z', 3.78945],\n",
       "   [u'2017-01-25T06:30:00Z', 4.0594463],\n",
       "   [u'2017-01-25T07:00:00Z', 3.5626378],\n",
       "   [u'2017-01-25T07:30:00Z', 4.112329],\n",
       "   [u'2017-01-25T08:00:00Z', 3.7344484],\n",
       "   [u'2017-01-25T08:30:00Z', 3.7410007],\n",
       "   [u'2017-01-25T09:00:00Z', 3.593731],\n",
       "   [u'2017-01-25T09:30:00Z', 3.477847],\n",
       "   [u'2017-01-25T10:00:00Z', 3.2032561],\n",
       "   [u'2017-01-25T10:30:00Z', 3.079367],\n",
       "   [u'2017-01-25T11:00:00Z', 3.0754032],\n",
       "   [u'2017-01-25T11:30:00Z', 3.1170216],\n",
       "   [u'2017-01-25T12:00:00Z', 2.7903783],\n",
       "   [u'2017-01-25T12:30:00Z', 3.0181916],\n",
       "   [u'2017-01-25T13:00:00Z', 2.5165727],\n",
       "   [u'2017-01-25T13:30:00Z', 2.508993],\n",
       "   [u'2017-01-25T14:00:00Z', 2.7889051],\n",
       "   [u'2017-01-25T14:30:00Z', 2.5887632],\n",
       "   [u'2017-01-25T15:00:00Z', 2.656679],\n",
       "   [u'2017-01-25T15:30:00Z', 2.6607733],\n",
       "   [u'2017-01-25T16:00:00Z', 2.4840262]]}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In general the time to retrieve data depends on:\n",
    "* the length of the time period requested\n",
    "* the sampling interval\n",
    "* how the data is stored/accessed (various binary file types vs various text file types vs database vs remote data servers, vs ...)\n",
    "* how the data is organized in the source files (one file per station? one file per station per month?)\n",
    "* whether the HTTP header of the request includes accept-encoding (\"gzip\", \"x-gzip\", or \"deflate\"). Browsers automatically include this. Compression is more work but less data to be transferred, especially for text formats like .csv and .json.\n",
    "* the response file type (some are streamable, some are cached, some are verbose, ...)\n",
    "* The load on the server, especially the RAID/drives.\n",
    "* The available bandwidth of the server's and the user's network.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bob Simons from NOAA adds: \"Some timing issues are counter-intuitive. For example, .nc files are binary so you would expect a request for a .nc file to be done very quickly. But .nc files can't be streamed. So they must be completely created on the server, then transmitted. In contrast, a .csv file can be streamed (ERDDAP reads from a file, writes a chunk to the user, reads another file, writes another chunk to the user). And although csv files are verbose, compression during transmission means that the number of bytes transmitted may actually be less than an uncompressed .nc file. So the actual results are hard to predict -- there are lots of factors.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this is encouraging, as this suggests ERDDAP would be fast enough to acts as a backend data system to build a dashboard to status a bunch of instruments"
   ]
  }
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