py/benches/encode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarrow.parquet as pq\n",
    "from pgpq import ArrowToPostgresBinaryEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import requests\n",
    "\n",
    "file = Path(\".\").resolve().parent.parent / \"yellow_tripdata_2022-01.parquet\"\n",
    "if not file.exists():\n",
    "    with requests.get(\"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet\", stream=True) as r:\n",
    "        r.raise_for_status()\n",
    "        with file.open(\"wb\") as f:\n",
    "            for chunk in r.iter_content(chunk_size=1024 * 1024):\n",
    "                f.write(chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrow_table = pq.read_table(file)\n",
    "\n",
    "batches = arrow_table.to_batches()\n",
    "small_batches = batches[:100]\n",
    "\n",
    "\n",
    "def batches_1_million_rows():\n",
    "    while True:\n",
    "        total = 0\n",
    "        for batch in batches:\n",
    "            total += len(batch)\n",
    "            yield batch\n",
    "            if total > 1_000_000:\n",
    "                return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(batches) -> None:\n",
    "    encoder = ArrowToPostgresBinaryEncoder(arrow_table.schema)\n",
    "    encoder.write_header()\n",
    "    for batch in batches:\n",
    "        encoder.write_batch(batch)\n",
    "    encoder.finish()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "803 ms ± 5.25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit encode(small_batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3498361110687256\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "start = time()\n",
    "encode(batches_1_million_rows())\n",
    "end = time()\n",
    "print(f\"{end-start}\")"
   ]
  }
 ],
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