{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYICEBERG_MAX_WORKERS=300\n"
     ]
    }
   ],
   "source": [
    "%env PYICEBERG_MAX_WORKERS=300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install \"pyiceberg[s3fs]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiceberg.catalog.rest import RestCatalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog = RestCatalog(\"public\", ** {\n",
    "    \"uri\": f\"http://localhost:8181\",\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog.create_namespace(\"public\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiceberg.schema import Schema\n",
    "from pyiceberg.types import (\n",
    "    NestedField,\n",
    "    LongType,\n",
    "    TimestampType,\n",
    "    DoubleType,\n",
    "    StringType,\n",
    ")\n",
    "\n",
    "schema = Schema(\n",
    "    NestedField(field_id=1, name=\"VendorID\", field_type=LongType(), required=False),\n",
    "    NestedField(field_id=2, name=\"tpep_pickup_datetime\", field_type=TimestampType(), required=False),\n",
    "    NestedField(field_id=3, name=\"tpep_dropoff_datetime\", field_type=TimestampType(), required=False),\n",
    "    NestedField(field_id=4, name=\"passenger_count\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=5, name=\"trip_distance\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=6, name=\"RatecodeID\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=7, name=\"store_and_fwd_flag\", field_type=StringType(), required=False),\n",
    "    NestedField(field_id=8, name=\"PULocationID\", field_type=LongType(), required=False),\n",
    "    NestedField(field_id=9, name=\"DOLocationID\", field_type=LongType(), required=False),\n",
    "    NestedField(field_id=10, name=\"payment_type\", field_type=LongType(), required=False),\n",
    "    NestedField(field_id=11, name=\"fare_amount\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=12, name=\"extra\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=13, name=\"mta_tax\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=14, name=\"tip_amount\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=15, name=\"tolls_amount\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=16, name=\"improvement_surcharge\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=17, name=\"total_amount\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=18, name=\"congestion_surcharge\", field_type=DoubleType(), required=False),\n",
    "    NestedField(field_id=19, name=\"airport_fee\", field_type=DoubleType(), required=False),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiceberg.partitioning import PartitionSpec, PartitionField\n",
    "from pyiceberg.transforms import DayTransform, MonthTransform, YearTransform, BucketTransform\n",
    "\n",
    "partition_spec = PartitionSpec(\n",
    "    PartitionField(source_id=2, field_id=1001, transform=MonthTransform(), name=\"tpep_pickup_datetime_month\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiceberg.table.sorting import SortOrder, SortField\n",
    "from pyiceberg.transforms import IdentityTransform\n",
    "\n",
    "sort_order = SortOrder(\n",
    "    SortField(source_id=4, transform=IdentityTransform())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = catalog.create_table(\n",
    "    identifier=\"public.nyc_taxi\",\n",
    "    schema=schema,\n",
    "    partition_spec=partition_spec,\n",
    "    sort_order=sort_order,\n",
    "    properties={\n",
    "        \"write.format.default\": \"parquet\",\n",
    "        \"write.parquet.compression-codec\": \"zstd\",\n",
    "        \"write.target-file-size-bytes\": \"536870912\",\n",
    "        \"s3.connect-timeout\": \"10000\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Appending files: 100%|██████████| 26/26 [10:19<00:00, 23.83s/it, Appended yellow_tripdata_2021-09.parquet]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows in the table: 73531304\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import requests\n",
    "import io\n",
    "import pyarrow.parquet as pq\n",
    "from tqdm import tqdm\n",
    "\n",
    "# GitHub repository information\n",
    "repo_owner = \"buster-so\"\n",
    "repo_name = \"sample-data\"\n",
    "folder_path = \"nyc_taxi\"\n",
    "\n",
    "# GitHub API endpoint to get repository contents\n",
    "api_url = f\"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{folder_path}\"\n",
    "\n",
    "# Fetch the list of files in the repository\n",
    "response = requests.get(api_url)\n",
    "if response.status_code != 200:\n",
    "    raise Exception(f\"Failed to fetch repository contents: {response.status_code}\")\n",
    "\n",
    "files = [item for item in response.json() if item['name'].endswith('.parquet')]\n",
    "\n",
    "# Create a progress bar\n",
    "with tqdm(total=len(files), desc=\"Appending files\") as pbar:\n",
    "    for file in files:\n",
    "        # Download the file content\n",
    "        file_url = file['download_url']\n",
    "        file_response = requests.get(file_url)\n",
    "        if file_response.status_code != 200:\n",
    "            print(f\"Failed to download {file['name']}: {file_response.status_code}\")\n",
    "            continue\n",
    "        \n",
    "        # Read the Parquet file from the response content\n",
    "        file_content = io.BytesIO(file_response.content)\n",
    "        df = pq.read_table(file_content)\n",
    "        \n",
    "        # Append to the Iceberg table\n",
    "        table.append(df)\n",
    "        \n",
    "        pbar.update(1)\n",
    "        pbar.set_postfix_str(f\"Appended {file['name']}\")\n",
    "\n",
    "# Print the total number of rows in the table after appending all files\n",
    "print(f\"Total rows in the table: {len(table.scan().to_arrow())}\")"
   ]
  }
 ],
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