Store Avro with compressions.ipynb

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   "source": [
    "Spark-submit needs option\n",
    "\n",
    "```\n",
    "--packages com.databricks:spark-avro_2.10:2.0.1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "# load a dataframe from Avro files\n",
    "df = sqlContext.read.format(\"com.databricks.spark.avro\").load(\"/cms/wmarchive/test/avro/2016/01/01/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "757.7 M  2.2 G  /cms/wmarchive/test/avro/2016/01/01\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "hadoop fs -du -h -s /cms/wmarchive/test/avro/2016/01/01/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sqlContext.setConf(\"spark.sql.avro.compression.codec\", \"snappy\")\n",
    "df.write.format(\"com.databricks.spark.avro\").save(\"wmarchive/test-avro-snappy-20160101\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188.0 M  564.1 M  wmarchive/test-avro-snappy-20160101\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "hadoop fs -du -h -s wmarchive/test-avro-snappy-20160101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfSnappy = sqlContext.read.format(\"com.databricks.spark.avro\").load(\"wmarchive/test-avro-snappy-20160101\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 ms, sys: 1 ms, total: 6 ms\n",
      "Wall time: 8.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "CPU times: user 2 ms, sys: 2 ms, total: 4 ms\n",
      "Wall time: 7.02 s\n"
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       "200000"
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "%%time\n",
    "dfSnappy.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GZIP\n",
    "sqlContext.setConf(\"spark.sql.avro.compression.codec\", \"deflate\")\n",
    "df.write.format(\"com.databricks.spark.avro\").save(\"wmarchive/test-avro-deflate-20160101\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125.9 M  377.8 M  wmarchive/test-avro-deflate-20160101\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "hadoop fs -du -h -s wmarchive/test-avro-deflate-20160101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfDeflate = sqlContext.read.format(\"com.databricks.spark.avro\").load(\"wmarchive/test-avro-deflate-20160101\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 ms, sys: 2 ms, total: 5 ms\n",
      "Wall time: 7.95 s\n"
     ]
    },
    {
     "data": {
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       "200000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "dfDeflate.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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