{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Name\n",
    "Data preparation using Apache Pig on YARN with Cloud Dataproc\n",
    "\n",
    "# Label\n",
    "Cloud Dataproc, GCP, Cloud Storage, YARN, Pig, Apache, Kubeflow, pipelines, components\n",
    "\n",
    "\n",
    "# Summary\n",
    "A Kubeflow Pipeline component to prepare data by submitting an Apache Pig job on YARN to Cloud Dataproc.\n",
    "\n",
    "\n",
    "# Details\n",
    "## Intended use\n",
    "Use the component to run an Apache Pig job as one preprocessing step in a Kubeflow Pipeline.\n",
    "\n",
    "## Runtime arguments\n",
    "| Argument | Description | Optional | Data type | Accepted values | Default |\n",
    "|----------|-------------|----------|-----------|-----------------|---------|\n",
    "| project_id | The ID of the Google Cloud Platform (GCP) project that the cluster belongs to. | No | GCPProjectID |  |  |\n",
    "| region | The Cloud Dataproc region to handle the request. | No | GCPRegion |  |  |\n",
    "| cluster_name | The name of the cluster to run the job. | No | String |  |  |\n",
    "| queries | The queries to execute the Pig job. Specify multiple queries in one string by separating them with semicolons. You do not need to terminate queries with semicolons. | Yes | List |  | None |\n",
    "| query_file_uri | The HCFS URI of the script that contains the Pig queries. | Yes | GCSPath |  | None |\n",
    "| script_variables | Mapping of the query’s variable names to their values (equivalent to the Pig command: SET name=\"value\";). | Yes | Dict |  | None |\n",
    "| pig_job | The payload of a [PigJob](https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob). | Yes | Dict |  | None |\n",
    "| job | The payload of a [Dataproc job](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs). | Yes | Dict |  | None |\n",
    "| wait_interval | The number of seconds to pause between polling the operation. | Yes | Integer |  | 30 |\n",
    "\n",
    "## Output\n",
    "Name | Description | Type\n",
    ":--- | :---------- | :---\n",
    "job_id | The ID of the created job. | String\n",
    "\n",
    "## Cautions & requirements\n",
    "\n",
    "To use the component, you must:\n",
    "*   Set up a GCP project by following this [guide](https://cloud.google.com/dataproc/docs/guides/setup-project).\n",
    "*   [Create a new cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster).\n",
    "*   The component can authenticate to GCP. Refer to [Authenticating Pipelines to GCP](https://www.kubeflow.org/docs/gke/authentication-pipelines/) for details.\n",
    "*   Grant the Kubeflow user service account the role `roles/dataproc.editor` on the project.\n",
    "\n",
    "## Detailed description\n",
    "This component creates a Pig job from [Dataproc submit job REST API](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs/submit).\n",
    "\n",
    "Follow these steps to use the component in a pipeline:\n",
    "1.  Install the Kubeflow Pipeline SDK:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "\n",
    "!pip3 install kfp --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Load the component using KFP SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.components as comp\n",
    "\n",
    "dataproc_submit_pig_job_op = comp.load_component_from_url(\n",
    "    'https://raw.githubusercontent.com/kubeflow/pipelines/1.7.0-rc.3/components/gcp/dataproc/submit_pig_job/component.yaml')\n",
    "help(dataproc_submit_pig_job_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample\n",
    "\n",
    "Note: The following sample code works in an IPython notebook or directly in Python code. See the sample code below to learn how to execute the template.\n",
    "\n",
    "\n",
    "#### Setup a Dataproc cluster\n",
    "\n",
    "[Create a new Dataproc cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster) (or reuse an existing one) before running the sample code.\n",
    "\n",
    "\n",
    "#### Prepare a Pig query\n",
    "\n",
    "Either put your Pig queries in the `queries` list, or upload your Pig queries into a file to a Cloud Storage bucket and then enter the Cloud Storage bucket’s path in `query_file_uri`. In this sample, we will use a hard coded query in the `queries` list to select data from a local `passwd` file.\n",
    "\n",
    "For more details on Apache Pig, see the [Pig documentation.](http://pig.apache.org/docs/latest/)\n",
    "\n",
    "#### Set sample parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = '<Please put your project ID here>'\n",
    "CLUSTER_NAME = '<Please put your existing cluster name here>'\n",
    "\n",
    "REGION = 'us-central1'\n",
    "QUERY = '''\n",
    "natality_csv = load 'gs://public-datasets/natality/csv' using PigStorage(':');\n",
    "top_natality_csv = LIMIT natality_csv 10; \n",
    "dump natality_csv;'''\n",
    "EXPERIMENT_NAME = 'Dataproc - Submit Pig Job'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example pipeline that uses the component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.dsl as dsl\n",
    "import json\n",
    "@dsl.pipeline(\n",
    "    name='Dataproc submit Pig job pipeline',\n",
    "    description='Dataproc submit Pig job pipeline'\n",
    ")\n",
    "def dataproc_submit_pig_job_pipeline(\n",
    "    project_id = PROJECT_ID, \n",
    "    region = REGION,\n",
    "    cluster_name = CLUSTER_NAME,\n",
    "    queries = json.dumps([QUERY]),\n",
    "    query_file_uri = '',\n",
    "    script_variables = '', \n",
    "    pig_job='', \n",
    "    job='', \n",
    "    wait_interval='30'\n",
    "):\n",
    "    dataproc_submit_pig_job_op(\n",
    "        project_id=project_id, \n",
    "        region=region, \n",
    "        cluster_name=cluster_name, \n",
    "        queries=queries, \n",
    "        query_file_uri=query_file_uri,\n",
    "        script_variables=script_variables, \n",
    "        pig_job=pig_job, \n",
    "        job=job, \n",
    "        wait_interval=wait_interval)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_func = dataproc_submit_pig_job_pipeline\n",
    "pipeline_filename = pipeline_func.__name__ + '.zip'\n",
    "import kfp.compiler as compiler\n",
    "compiler.Compiler().compile(pipeline_func, pipeline_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit the pipeline for execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify pipeline argument values\n",
    "arguments = {}\n",
    "\n",
    "#Get or create an experiment and submit a pipeline run\n",
    "import kfp\n",
    "client = kfp.Client()\n",
    "experiment = client.create_experiment(EXPERIMENT_NAME)\n",
    "\n",
    "#Submit a pipeline run\n",
    "run_name = pipeline_func.__name__ + ' run'\n",
    "run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "*   [Create a new Dataproc cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster) \n",
    "*   [Pig documentation](http://pig.apache.org/docs/latest/)\n",
    "*   [Dataproc job](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs)\n",
    "*   [PigJob](https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob)\n",
    "\n",
    "## License\n",
    "By deploying or using this software you agree to comply with the [AI Hub Terms of Service](https://aihub.cloud.google.com/u/0/aihub-tos) and the [Google APIs Terms of Service](https://developers.google.com/terms/). To the extent of a direct conflict of terms, the AI Hub Terms of Service will control."
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}