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lmeyerov merged 1 commit into from
Nov 20, 2019
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demo(blazingsql) #138

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367 changes: 367 additions & 0 deletions demos/demos_databases_apis/gpu_rapids/part_iii_gpu_blazingsql.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BlazingSQL + Graphistry: Netflow analysis\n",
"\n",
"This tutorial shows running BlazingSQL (GPU-accelerated SQL) on raw parquet files and visually analyzing the result with Graphistry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_0_0.parquet\n",
"!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_1_0.parquet \n",
"!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_2_0.parquet\n",
"!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_3_0.parquet"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 434M\r\n",
"drwxr-xr-x 2 graphistry graphistry 4.0K Nov 19 23:26 .\r\n",
"drwxr-xr-x 1 graphistry root 4.0K Nov 19 23:37 ..\r\n",
"-rw-r--r-- 1 graphistry graphistry 108M Jul 30 22:02 1_0_0.parquet\r\n",
"-rw-r--r-- 1 graphistry graphistry 135M Jul 30 22:01 1_1_0.parquet\r\n",
"-rw-r--r-- 1 graphistry graphistry 85M Jul 30 22:01 1_2_0.parquet\r\n",
"-rw-r--r-- 1 graphistry graphistry 108M Jul 30 22:01 1_3_0.parquet\r\n"
]
}
],
"source": [
"!ls -alh data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data into table"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BlazingContext ready\n"
]
}
],
"source": [
"from blazingsql import BlazingContext \n",
"\n",
"bc = BlazingContext()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/home/graphistry']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"local_path = !pwd\n",
"local_path"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyblazing.apiv2.sql.Table at 0x7fd13cfa7cc0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bc.create_table('netflow', local_path[0] + '/data/*_0.parquet')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute IP<>IP flow summaries"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 311 ms, sys: 257 ms, total: 568 ms\n",
"Wall time: 4.75 s\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>source</th>\n",
" <th>destination</th>\n",
" <th>targetPorts</th>\n",
" <th>bytesOut</th>\n",
" <th>bytesIn</th>\n",
" <th>durationSeconds</th>\n",
" <th>firstFlowDate</th>\n",
" <th>lastFlowDate</th>\n",
" <th>attemptCount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10.0.0.13</td>\n",
" <td>46932</td>\n",
" <td>8</td>\n",
" <td>5064</td>\n",
" <td>41</td>\n",
" <td>1360</td>\n",
" <td>2013-04-01 08:57:31</td>\n",
" <td>2013-04-06 08:29:30</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0.0.12</td>\n",
" <td>2605</td>\n",
" <td>20</td>\n",
" <td>12660</td>\n",
" <td>100</td>\n",
" <td>3520</td>\n",
" <td>2013-04-01 09:29:15</td>\n",
" <td>2013-04-07 06:51:00</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0.0.7</td>\n",
" <td>29791</td>\n",
" <td>10</td>\n",
" <td>6320</td>\n",
" <td>50</td>\n",
" <td>1630</td>\n",
" <td>2013-04-01 10:54:31</td>\n",
" <td>2013-04-06 07:03:52</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source destination targetPorts bytesOut bytesIn durationSeconds \\\n",
"0 10.0.0.13 46932 8 5064 41 1360 \n",
"1 10.0.0.12 2605 20 12660 100 3520 \n",
"2 10.0.0.7 29791 10 6320 50 1630 \n",
"\n",
" firstFlowDate lastFlowDate attemptCount \n",
"0 2013-04-01 08:57:31 2013-04-06 08:29:30 8 \n",
"1 2013-04-01 09:29:15 2013-04-07 06:51:00 20 \n",
"2 2013-04-01 10:54:31 2013-04-06 07:03:52 10 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"result = bc.sql('''\n",
" SELECT\n",
" a.firstSeenSrcIp as source,\n",
" a.firstSeenDestIp as destination,\n",
" count(a.firstSeenDestPort) as targetPorts,\n",
" SUM(a.firstSeenSrcTotalBytes) as bytesOut,\n",
" SUM(a.firstSeenDestTotalBytes) as bytesIn,\n",
" SUM(a.durationSeconds) as durationSeconds,\n",
" MIN(parsedDate) as firstFlowDate,\n",
" MAX(parsedDate) as lastFlowDate,\n",
" COUNT(*) as attemptCount\n",
" FROM\n",
" netflow a\n",
" GROUP BY\n",
" a.firstSeenSrcIp,\n",
" a.firstSeenDestIp\n",
" ''').get()\n",
"\n",
"gdf = result.columns\n",
"\n",
"gdf.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize network"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import graphistry\n",
"\n",
"#upload protobuf instead of json\n",
"graphistry.register(api=2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1303786"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(gdf.to_pandas())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Large graph: |nodes| + |edges| = 1369523. Layout/rendering might be slow.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Uploading 19425 kB. This may take a while...\n"
]
},
{
"data": {
"text/html": [
"\n",
" <iframe id=\"f58748f2-17c5-45bc-b5d6-d1d352639007\" src=\"/graph/graph.html?dataset=d615fd18441c53f2bfbf099c8b86d5c5&type=jsonMeta&viztoken=d6ee78fd457c4aee8e06c02a3c3c3f59&usertag=1849d922-pygraphistry-0.9.69&splashAfter=1574207267&info=true\"\n",
" allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
" oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
" style=\"width:100%; height:500px; border: 1px solid #DDD\">\n",
" </iframe>\n",
" \n",
" <script>\n",
" $(\"#f58748f2-17c5-45bc-b5d6-d1d352639007\").bind('mousewheel', function(e) {\n",
" e.preventDefault();\n",
" });\n",
" </script>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graphistry.bind(source='source', destination='destination').plot(gdf.to_pandas())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7 (RAPIDS)",
"language": "python",
"name": "rapids"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}