ARROW-3246 Benchmarks

AfterArrow3246.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pandas as pd\n",
    "from pandas.util.testing import rands\n",
    "        \n",
    "NUNIQUE = 1000\n",
    "STRING_SIZE = 50\n",
    "LENGTH = 10_000_000\n",
    "REPEATS = LENGTH // NUNIQUE\n",
    "\n",
    "uniques = np.array([rands(STRING_SIZE) for i in range(NUNIQUE)], dtype='O')\n",
    "indices = np.random.randint(0, NUNIQUE, size=LENGTH).astype('i4')        \n",
    "data = uniques.take(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "class memory_use:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.start_use = pa.total_allocated_bytes()        \n",
    "        self.pool = pa.default_memory_pool()\n",
    "        self.start_peak_use = self.pool.max_memory()\n",
    "        \n",
    "    def __enter__(self):\n",
    "        return\n",
    "    \n",
    "    def __exit__(self, type, value, traceback):\n",
    "        gc.collect()\n",
    "        print(\"Change in memory use: {}\"\n",
    "              .format(pa.total_allocated_bytes() - self.start_use))\n",
    "        print(\"Change in peak use: {}\"\n",
    "              .format(self.pool.max_memory() - self.start_peak_use))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_data = pa.DictionaryArray.from_arrays(indices, uniques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72320"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa.default_memory_pool().max_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in memory use: 16777216\n",
      "Change in peak use: 53475584\n"
     ]
    }
   ],
   "source": [
    "table = pa.table([dict_data], names=['f0'])\n",
    "with memory_use():\n",
    "    out_stream = pa.BufferOutputStream()\n",
    "    pq.write_table(table, out_stream)\n",
    "    contents = out_stream.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204 ms ± 3.62 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "out_stream = pa.BufferOutputStream()\n",
    "pq.write_table(table, out_stream)\n",
    "contents = out_stream.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12574992"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.2 ms ± 2.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit returned_table = pq.read_table(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.9 ms ± 2.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit returned_table = pq.read_table(contents, read_dictionary=['f0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_data = dict_data.cast(pa.utf8())\n",
    "table = pa.table([dense_data], names=['f0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329 ms ± 3.84 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "out_stream = pa.BufferOutputStream()\n",
    "pq.write_table(table, out_stream)\n",
    "contents = out_stream.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_stream = pa.BufferOutputStream()\n",
    "pq.write_table(table, out_stream)\n",
    "contents = out_stream.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "373 ms ± 11.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "returned_table = pq.read_table(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyarrow.Table\n",
       "f0: string"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pq.read_table(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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BeforeArrow3246.ipynb