snowflake_trec_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:21:41.310151Z",
     "start_time": "2019-02-26T18:21:40.905070Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import zstandard as zstd\n",
    "from io import TextIOWrapper\n",
    "\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:21:41.330110Z",
     "start_time": "2019-02-26T18:21:41.311603Z"
    }
   },
   "outputs": [],
   "source": [
    "def melt_snowflake(snowflake_id):\n",
    "    \"\"\"return tuple of snowflake components given a tweet id\"\"\"\n",
    "    timestamp_ms = ((snowflake_id >> 22) + 1288834974657)\n",
    "    datacenter_id = (snowflake_id >> 17) & 0b11111\n",
    "    worker_id = (snowflake_id >> 12) & 0b11111\n",
    "    sequence_id = snowflake_id & 0b111111111111\n",
    "    # this is a combination of worker_id id and datacenter id\n",
    "    machine_id = (snowflake_id >> 12) & 0b1111111111\n",
    "    return (timestamp_ms, datacenter_id, worker_id, sequence_id, machine_id)\n",
    "\n",
    "def plt_counter(c):\n",
    "    labels, values = zip(*c.items())\n",
    "    indexes = np.arange(len(labels))\n",
    "    width = 0.95\n",
    "    plt.figure(figsize=(15,5))\n",
    "    plt.bar(indexes, values, width)\n",
    "    plt.xticks(indexes + width * 0.5, labels)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:21:41.346300Z",
     "start_time": "2019-02-26T18:21:41.333560Z"
    }
   },
   "outputs": [],
   "source": [
    "# defaultdict is faster than Counter()\n",
    "datacenters = defaultdict(int)\n",
    "workers = defaultdict(int)\n",
    "sequences = defaultdict(int)\n",
    "machines = defaultdict(int)\n",
    "total_tweets = defaultdict(int)\n",
    "\n",
    "def count_components(tweet_id):\n",
    "    parts = melt_snowflake(tweet_id)\n",
    "    datacenters[parts[1]] += 1\n",
    "    workers[parts[2]] += 1\n",
    "    sequences[parts[3]] += 1\n",
    "    machines[parts[4]] += 1\n",
    "    total_tweets['ids'] += 1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:50.768668Z",
     "start_time": "2019-02-26T18:21:41.348518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 43956390 tweets\n"
     ]
    }
   ],
   "source": [
    "path = 'TREC2015-tweetids.txt.zst'\n",
    "\n",
    "with open(path, 'rb') as fh:\n",
    "    decompressor = zstd.ZstdDecompressor()\n",
    "    with decompressor.stream_reader(fh, read_size=64192) as reader: #\n",
    "        with TextIOWrapper(reader, encoding='UTF-8', newline='\\n', line_buffering=True) as line_reader:\n",
    "            for line in line_reader:\n",
    "                count_components(int(line)) \n",
    "\n",
    "print(\"Processed\", total_tweets['ids'], \"tweets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:50.778351Z",
     "start_time": "2019-02-26T18:22:50.770998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[320,\n",
       " 321,\n",
       " 323,\n",
       " 326,\n",
       " 327,\n",
       " 329,\n",
       " 331,\n",
       " 332,\n",
       " 336,\n",
       " 337,\n",
       " 353,\n",
       " 354,\n",
       " 357,\n",
       " 359,\n",
       " 360,\n",
       " 362,\n",
       " 363,\n",
       " 364,\n",
       " 367,\n",
       " 368]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All Machine IDs in TREC Tweets\n",
    "sorted(machines.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.306941Z",
     "start_time": "2019-02-26T18:22:50.781374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f0c6f2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_counter(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.316456Z",
     "start_time": "2019-02-26T18:22:51.309764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {0: 34006666,\n",
       "             1: 7208821,\n",
       "             2: 1161513,\n",
       "             3: 163420,\n",
       "             4: 999521,\n",
       "             5: 311015,\n",
       "             6: 60669,\n",
       "             7: 9671,\n",
       "             8: 23058,\n",
       "             9: 8681,\n",
       "             10: 1964,\n",
       "             11: 396,\n",
       "             12: 512,\n",
       "             13: 235,\n",
       "             14: 69,\n",
       "             15: 28,\n",
       "             16: 31,\n",
       "             17: 16,\n",
       "             18: 14,\n",
       "             19: 10,\n",
       "             20: 9,\n",
       "             21: 5,\n",
       "             22: 4,\n",
       "             23: 3,\n",
       "             24: 3,\n",
       "             25: 3,\n",
       "             26: 1,\n",
       "             27: 4,\n",
       "             28: 2,\n",
       "             29: 2,\n",
       "             31: 3,\n",
       "             32: 1,\n",
       "             33: 1,\n",
       "             34: 2,\n",
       "             35: 1,\n",
       "             36: 2,\n",
       "             37: 3,\n",
       "             38: 1,\n",
       "             39: 2,\n",
       "             40: 1,\n",
       "             41: 1,\n",
       "             42: 1,\n",
       "             43: 1,\n",
       "             45: 1,\n",
       "             46: 1,\n",
       "             47: 1,\n",
       "             48: 3,\n",
       "             50: 3,\n",
       "             51: 2,\n",
       "             52: 2,\n",
       "             54: 1,\n",
       "             55: 1,\n",
       "             56: 1,\n",
       "             57: 1,\n",
       "             58: 1,\n",
       "             59: 1,\n",
       "             60: 1,\n",
       "             63: 1,\n",
       "             80: 1,\n",
       "             87: 1,\n",
       "             88: 1})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.332695Z",
     "start_time": "2019-02-26T18:22:51.319399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int, {10: 23029374, 11: 20927016})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datacenters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.546211Z",
     "start_time": "2019-02-26T18:22:51.335245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f0989f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_counter(workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.787088Z",
     "start_time": "2019-02-26T18:22:51.548732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f0989f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_counter(machines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-26T18:22:51.793579Z",
     "start_time": "2019-02-26T18:22:51.789315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {320: 2302480,\n",
       "             321: 2302821,\n",
       "             323: 2302907,\n",
       "             326: 2302221,\n",
       "             327: 2307031,\n",
       "             329: 2302184,\n",
       "             331: 2303465,\n",
       "             332: 2303599,\n",
       "             336: 2300150,\n",
       "             337: 2302516,\n",
       "             353: 2092357,\n",
       "             354: 2092798,\n",
       "             357: 2095881,\n",
       "             359: 2092744,\n",
       "             360: 2092296,\n",
       "             362: 2092223,\n",
       "             363: 2096692,\n",
       "             364: 2090417,\n",
       "             367: 2087621,\n",
       "             368: 2093987})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}