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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing an edge list into Titan 0.9.0-M1(Berkeley DB) w/TP3 Gremlin Server 3.0.0-M7 and Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from gizmo import AsyncGremlinClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_schema():\n",
    "    script = \"\"\"\n",
    "        mgmt = g.openManagement();\n",
    "        uniqueId = mgmt.makePropertyKey('uniqueId').dataType(Integer.class).make();\n",
    "        mgmt.buildIndex('byId', Vertex.class).addKey(uniqueId).unique().buildCompositeIndex();\n",
    "        mgmt.makeEdgeLabel('collabs').make();\n",
    "        mgmt.commit();\"\"\"\n",
    "    gc = AsyncGremlinClient()\n",
    "    t = gc.s(script,\n",
    "        consumer=lambda x: print(\"Commited tx with response code: {}\".format(x.status_code)))\n",
    "    t.execute()\n",
    "    \n",
    "\n",
    "def load_edges():\n",
    "    start = datetime.now()\n",
    "    script = \"\"\"     \n",
    "        getOrCreate = { id ->\n",
    "            def n = g.V().has('uniqueId', id)\n",
    "            if (n.hasNext()) {n.next()} else {g.addVertex(\"uniqueId\", id)}\n",
    "        }\n",
    "\n",
    "        new File('social_net.txt').eachLine {  \n",
    "            (source, target) = it.split('\\t').collect(getOrCreate)\n",
    "            source.addEdge('collabs', target)\n",
    "        }\n",
    "\n",
    "        g.tx().commit()\"\"\"\n",
    "    gc = AsyncGremlinClient()\n",
    "    t = gc.s(script,\n",
    "        consumer=lambda x: print(\"Commited tx with response code: {}\".format(x.status_code)))\n",
    "    t.execute()\n",
    "    print(\"Loaded in {}\".format(datetime.now() - start))\n",
    "\n",
    "\n",
    "def count_nodes(gc):\n",
    "    t = gc.s(\"g.V().count()\", collect=False, consumer=lambda x: print(x))\n",
    "    t.execute()\n",
    "\n",
    "\n",
    "def count_edges(gc):\n",
    "    t = gc.s(\"g.E().count()\", collect=False, consumer=lambda x: print(x))\n",
    "    t.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Commited tx with response code: 200\n"
     ]
    }
   ],
   "source": [
    "build_schema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Commited tx with response code: 200\n",
      "Loaded in 0:00:17.949163\n"
     ]
    }
   ],
   "source": [
    "load_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gc = AsyncGremlinClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16264]\n"
     ]
    }
   ],
   "source": [
    "count_nodes(gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[47594]\n"
     ]
    }
   ],
   "source": [
    "count_edges(gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2651.62404590785 edges per second\n"
     ]
    }
   ],
   "source": [
    "print(\"{} edges per second\".format(47594 / 17.949))"
   ]
  }
 ],
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