examples/etl/ETL_02_one_cycle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ETL one cycle\n",
    "> Normally ETL is processed by 3 steps, E, T, L :) but we could do it by one cycle, ETL.\n",
    "\n",
    "We are going to use the 3 configs from `ETL_how_to_run.ipynb` and merge it to one config file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🌌 1. prepare config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "from dataverse.config import Config \n",
    "from omegaconf import OmegaConf\n",
    "\n",
    "# E = Extract, T = Transform, L = Load\n",
    "main_path = Path(os.path.abspath('../..'))\n",
    "ETL_path = main_path / \"./dataverse/config/etl/sample/ETL___one_cycle.yaml\"\n",
    "\n",
    "ETL_config = Config.load(ETL_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wait! If you haven't clone the repository, run the shell script below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ETL_config = OmegaConf.create({\n",
    "    'spark': { \n",
    "        'appname': 'dataverse_etl_sample',\n",
    "        'driver': {'memory': '16g'}  \n",
    "    },\n",
    "    'etl': [\n",
    "        {\n",
    "            'name': 'data_ingestion___test___generate_fake_ufl'\n",
    "        },\n",
    "        {\n",
    "            'name': 'utils___sampling___random',\n",
    "            'args': {'sample_n_or_frac': 0.1}\n",
    "        },\n",
    "        {\n",
    "            'name': 'deduplication___minhash___lsh_jaccard'\n",
    "        },\n",
    "        {\n",
    "            'name': 'data_save___huggingface___ufl2hf_obj'\n",
    "        }\n",
    "    ]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🌠 ETL Config\n",
    "> One cycle from raw to huggingface dataset\n",
    "\n",
    "- load fake generation UFL data\n",
    "- sample 10% of total data to reduce the size of dataset\n",
    "- deduplicate by `text` column, 15-gram minhash jaccard similarity\n",
    "- convert to huggingface dataset and return the object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spark:\n",
      "  appname: dataverse_etl_sample\n",
      "  driver:\n",
      "    memory: 16g\n",
      "etl:\n",
      "- name: data_ingestion___test___generate_fake_ufl\n",
      "- name: utils___sampling___random\n",
      "  args:\n",
      "    sample_n_or_frac: 0.1\n",
      "- name: deduplication___minhash___lsh_jaccard\n",
      "- name: data_save___huggingface___ufl2hf_obj\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(OmegaConf.to_yaml(ETL_config))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🌌 2. put config to ETLPipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataverse.etl import ETLPipeline\n",
    "\n",
    "etl_pipeline = ETLPipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ No AWS Credentials Found] - Failed to set spark conf for S3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "24/04/15 22:26:20 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
      "24/04/15 22:26:20 WARN SparkConf: Note that spark.local.dir will be overridden by the value set by the cluster manager (via SPARK_LOCAL_DIRS in mesos/standalone/kubernetes and LOCAL_DIRS in YARN).\n",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# raw -> hf_obj\n",
    "spark, dataset = etl_pipeline.run(ETL_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'meta', 'name', 'text'],\n",
       "    num_rows: 14\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '32ff39e5-2a88-45dc-a69e-b59b05f51216',\n",
       " 'meta': '{\"name\": \"Laura White\", \"age\": 49, \"address\": \"126 Javier Islands Apt. 925\\\\nPort Jasonshire, UT 60978\", \"job\": \"Mining engineer\"}',\n",
       " 'name': 'test_fake_ufl',\n",
       " 'text': 'Your whose admit ask herself. Public mission far program tough.\\nEconomic talk few minute. Budget face yeah along difference. Evening heart throughout.'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  }
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