notebook_examples/pyspark-data_flow_studio-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "a4c0d057",
   "metadata": {},
   "source": [
    "@notebook{pyspark-data_flow_studio-introduction.ipynb,\n",
    "    title: Introduction to the Oracle Cloud Infrastructure Data Flow Studio,\n",
    "    summary: Run interactive Spark workloads on a long lasting Oracle Cloud Infrastructure Data Flow Spark cluster through Apache Livy integration. Data Flow Spark Magic is used for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. It includes a set of magic commands for interactively running Spark code.,\n",
    "    developed_on: pyspark32_p38_cpu_v2,\n",
    "    keywords: pyspark, data flow,\n",
    "    license: Universal Permissive License v 1.0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57fec86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade Oracle ADS to pick up latest features and maintain compatibility with Oracle Cloud Infrastructure.\n",
    "!pip install -U oracle-ads"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "a4b86ad6",
   "metadata": {},
   "source": [
    "Oracle Data Science service sample notebook.\n",
    "\n",
    "Copyright (c) 2022 Oracle, Inc. All rights reserved. Licensed under the [Universal Permissive License v 1.0](https://oss.oracle.com/licenses/upl).\n",
    "\n",
    "***\n",
    "\n",
    "# <font color=\"red\">Introduction to the Oracle Cloud Infrastructure Data Flow Studio</font>\n",
    "<p style=\"margin-left:10%; margin-right:10%;\">by the <font color=\"teal\">Oracle Cloud Infrastructure Data Science Service.</font></p>\n",
    "\n",
    "---\n",
    "# Overview:\n",
    "\n",
    "This notebook demonstrates how to run interactive Spark workloads on a long lasting [Oracle Cloud Infrastructure Data Flow](https://docs.oracle.com/en-us/iaas/data-flow/using/home.htm) cluster through [Apache Livy](https://livy.apache.org/) integration. **Data Flow Spark Magic** is used for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. It includes a set of magic commands for interactively running Spark code.\n",
    "\n",
    "Comnatible conda pack: [PySpark 3.2 and Data Flow](https://docs.oracle.com/iaas/data-science/using/conda-pyspark-fam.htm) for CPU on Python 3.8\n",
    "\n",
    "![Livy_Dataflow.png](attachment:12dd460d-f9c6-4a8b-b84b-d9f40f55990d.png)\n",
    "\n",
    "---\n",
    "\n",
    "## Contents:\n",
    "\n",
    "- <a href='#pre-requisites'>1. Pre-requisites</a>\n",
    "    - <a href='#policies'>1.2 Policies</a>\n",
    "    - <a href='#prerequisites_helpers'>1.3 Helpers</a>\n",
    "    - <a href='#prerequisites_authentication'>1.4 Authentication</a>\n",
    "    - <a href='#prerequisites_variables'>1.5 Variables</a>    \n",
    "- <a href='#dataflow_magic'>2. Data Flow Spark Magic</a>\n",
    "    - <a href='#load_extension'>2.1. Load Extension</a>\n",
    "    - <a href='#create_session'>2.2. Create Session</a>\n",
    "        - <a href='#create_session_flex_shape'>2.2.1. Example command for Flex shapes</a>\n",
    "        - <a href='#create_session_dynamic_allocation'>2.2.2. Example command for Spark dynamic allocation</a>\n",
    "        - <a href='#create_session_third_party_libraries'>2.2.3. Example command with third-party libraries</a>\n",
    "        - <a href='#create_session_hive_metastore'>2.2.4. Example command with the Data Catalog Hive Metastore</a>\n",
    "        - <a href='#create_session_custom_conda'>2.2.5. Example command with the published conda environment</a>\n",
    "    - <a href='#update_session'>2.3. Update Session</a>\n",
    "    - <a href='#configure_session'>2.4. Configure Session</a>\n",
    "    - <a href='#stop_session'>2.5. Stop Session</a>\n",
    "    - <a href='#activate_session'>2.6. Activate Session</a>\n",
    "    - <a href='#using_existing_session'>2.7. Use existing Session</a>\n",
    "- <a href='#basic_examples'>3. Basic Spark Usage Examples</a>\n",
    "    - <a href='#examples_pyspark'>3.1. PySpark</a>\n",
    "    - <a href='#examples_sparksql'>3.2. Spark SQL</a>\n",
    "    - <a href='#examples_autoviz'>3.3. Autovisualization Widget</a>\n",
    "- <a href='#cleanup'>4. Clean Up</a> \n",
    "- <a href='#ref'>5. References</a>   \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f82aa6",
   "metadata": {},
   "source": [
    "<a id='pre-requisites'></a>\n",
    "# 1. Pre-requisites \n",
    "\n",
    "Data Flow Sessions are accessible through the following conda environment: \n",
    "\n",
    "* **PySpark 3.2 and Data Flow 2.0 (pyspark32_p38_cpu_v2)**\n",
    "\n",
    "You can customize `pypspark32_p38_cpu_v2`, publish it, and use it as a runtime environment for a Data Flow Session. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c78a9ca",
   "metadata": {},
   "source": [
    "<a id='policies'></a>\n",
    "## 1.2. Policies\n",
    "This section covers the creation of dynamic groups and policies needed to use the service.\n",
    "\n",
    "* [Data Flow Policies](https://docs.oracle.com/iaas/data-flow/using/policies.htm/)\n",
    "* [Getting Started with Data Flow](https://docs.oracle.com/iaas/data-flow/using/dfs_getting_started.htm)\n",
    "* [About Data Science Policies](https://docs.oracle.com/iaas/data-science/using/policies.htm)\n",
    "* [Data Catalog Metastore](https://docs.oracle.com/en-us/iaas/data-catalog/using/metastore.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6e749f",
   "metadata": {},
   "source": [
    "<a id=\"prerequisites_helpers\"></a>\n",
    "## 1.3 Helpers\n",
    "This section provides a helper method used across the notebook to prepare arguments for the magic commands. This function is particularly useful when you want to pass Python variables as arguments to the spark magic commands "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82ab468",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "def prepare_command(command: dict) -> str:\n",
    "    \"\"\"Converts dictionary command to the string formatted commands.\"\"\"\n",
    "    return f\"'{json.dumps(command)}'\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a245f86",
   "metadata": {},
   "source": [
    "<a id=\"prerequisites_authentication\"></a>\n",
    "## 1.4. Authentication\n",
    "The [Oracle Accelerated Data Science SDK (ADS)](https://docs.oracle.com/iaas/tools/ads-sdk/latest/index.html) controls the authentication mechanism with the Data Flow Session Spark cluster.<br> \n",
    "To setup authentication use the ```ads.set_auth(\"resource_principal\")``` or ```ads.set_auth(\"api_key\")```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71a6dc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ads\n",
    "\n",
    "ads.set_auth(\"resource_principal\")  # Supported values: resource_principal, api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1188f67b",
   "metadata": {},
   "source": [
    "<a id=\"prerequisites_variables\"></a>\n",
    "## 1.5. Variables\n",
    "To run this notebook, you must provide some information about your tenancy configuration. To connect to the HIVE metastore, replace `<metastore_id>` with the OCID for the HIVE metastore. Connecting to the metastore is optional.  \n",
    "\n",
    "To create and run a Data Flow session, you must specify a `<compartment_id>` and bucket `<logs_bucket_uri>` for storing logs. These resources must be in the same compartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad3f157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "compartment_id = os.environ.get(\"NB_SESSION_COMPARTMENT_OCID\")\n",
    "logs_bucket_uri = \"oci://<bucket_name>@<namespace>/<prefix>\"\n",
    "metastore_id = \"<metastore_id>\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c268b60",
   "metadata": {},
   "source": [
    "<a id=\"dataflow_magic\"></a>\n",
    "# 2. Data Flow Spark Magic\n",
    "Data Flow Spark Magic commands allow you to interactively work with Data Flow Spark clusters (sessions) in Jupyter notebooks through the Livy REST API. It provides a set of Jupyter Notebook cell magic commands to turn Jupyter into an integrated Spark development environment for remote clusters. \n",
    "\n",
    "**Data Flow Magic allows you to:**\n",
    "\n",
    "* Run Spark code against Data Flow remote Spark cluster\n",
    "* Create a Data Flow Spark Session with SparkContext and HiveContext against Data Flow remote Spark cluster\n",
    "* Capture the output of Spark queries as a local Pandas data frame to interact easily with other Python libraries (e.g. matplotlib)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c5d7ff",
   "metadata": {},
   "source": [
    "<a id=\"load_extension\"></a>\n",
    "### 2.1. Load Spark Magic Commands and Getting Help\n",
    "Data Flow Spark Magic is a JupyterLab extension that you need to activate in your notebook using the `%load_ext dataflow.magics` magic command.<br>\n",
    "After the extension is activated, the `%help` command can be used to get the list of supported commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f3de853",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext dataflow.magics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233763e3",
   "metadata": {},
   "source": [
    "The `%help` command gives you a list of all the available commands, along with a list of their arguments and example calls. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c5ab91",
   "metadata": {},
   "outputs": [],
   "source": [
    "%help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832b0175",
   "metadata": {},
   "source": [
    "If you want to access the docstrings of any magic command and figure out what arguments to provide, simply add `?` at then end of the command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e56f88ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "?%create_session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22369cde",
   "metadata": {},
   "source": [
    "### 2.2. Create Session\n",
    "To create a new Data Flow cluster session use the `%create_session` magic command."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e0dca1",
   "metadata": {},
   "source": [
    "<a id=\"create_session_flex_shape\"></a>\n",
    "#### 2.2.1. Example command for Flex shapes\n",
    "In the example below the new Data Flow cluster session is started with the basic configuration on the flexible shapes for driver and executor.<br>The [VM.Standard.E4.Flex](https://docs.oracle.com/en-us/iaas/Content/Compute/References/computeshapes.htm) is used for both driver and executor shapes. For each one, we independently set the number of OCPUs and the amount of primary memory. The cluster is active for 1440 minutes. \n",
    "\n",
    "Note that the parameter `fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com` is required for running the Spark samples below. The parameter `fs.oci.client.hostname` specifies the region endpoint where the data is stored on Object Storage. It is required if your cluster is running in a different region than the object storage bucket. \n",
    "\n",
    "In the code samples below, the dataset is publicly available but hosted in an Object Storage bucket in the Ashburn region. By specifying the region of the bucket, we can ensure that the cluster will access the data no matter what the cluster region is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "055afabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": compartment_id,\n",
    "        \"displayName\": \"TestDataFLowSessionFlexShapes\",\n",
    "        \"language\": \"PYTHON\",\n",
    "        \"sparkVersion\": \"3.2.1\",\n",
    "        \"numExecutors\": 1,\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"type\": \"SESSION\",\n",
    "        \"logsBucketUri\": logs_bucket_uri,\n",
    "        \"configuration\": {\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\"\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%create_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e7ebad",
   "metadata": {},
   "source": [
    "<a id=\"create_session_dynamic_allocation\"></a>\n",
    "#### 2.2.2. Example command for Spark dynamic allocation (aka auto-scaling)\n",
    "To help you save resources and reduce time on management, Spark [dynamic allocation](https://docs.oracle.com/iaas/data-flow/using/dynamic-alloc-about.htm#dynamic-alloc-about) is now enabled in Data Flow.\n",
    "\n",
    "Resource planning for data processing is a complex task. Resource usage is a function of the volume of the data. Day-to-day volumes of data can vary, meaning the computational resource required changes, too.\n",
    "\n",
    "You can define a Data Flow cluster based on a range of executors, instead of just a fixed number of executors. Spark provides a mechanism to dynamically adjust the resources application occupies based on the workload. The application might relinquish resources if they are no longer used and request them again later when there is demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "125a161e",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": compartment_id,\n",
    "        \"displayName\": \"TestDataFLowSessionDynamicAllocation\",\n",
    "        \"language\": \"PYTHON\",\n",
    "        \"sparkVersion\": \"3.2.1\",\n",
    "        \"numExecutors\": 2,\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"configuration\": {\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\",\n",
    "            \"spark.dynamicAllocation.enabled\": \"true\",\n",
    "            \"spark.dynamicAllocation.shuffleTracking.enabled\": \"true\",\n",
    "            \"spark.dynamicAllocation.minExecutors\": \"1\",\n",
    "            \"spark.dynamicAllocation.maxExecutors\": \"4\",\n",
    "            \"spark.dynamicAllocation.executorIdleTimeout\": \"60\",\n",
    "            \"spark.dynamicAllocation.schedulerBacklogTimeout\": \"60\",\n",
    "            \"spark.dataflow.dynamicAllocation.quotaPolicy\": \"min\",\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%create_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0aeaf43",
   "metadata": {},
   "source": [
    "<a id=\"create_session_third_party_libraries\"></a>    \n",
    "#### 2.2.3. Example command with third-party libraries\n",
    "            \n",
    "The Data Flow Sessions support [custom dependencies](https://docs.oracle.com/iaas/data-flow/using/third-party-libraries.htm) in the form of Python wheels or virtual environments. You might want to make native code or other assets available within your Spark runtime. The dependencies can be attached by using `archiveUri` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b916556",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": compartment_id,\n",
    "        \"displayName\": \"TestDataFLowSessionThirdPartyDependencies\",\n",
    "        \"language\": \"PYTHON\",\n",
    "        \"sparkVersion\": \"3.2.1\",\n",
    "        \"numExecutors\": 1,\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"type\": \"SESSION\",\n",
    "        \"logsBucketUri\": logs_bucket_uri,\n",
    "        \"archiveUri\": \"oci://<bucket_name>@<namespace>/<zip_archive>\",\n",
    "        \"configuration\": {\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\"\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%create_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649e85bc",
   "metadata": {},
   "source": [
    "<a id=\"create_session_hive_metastore\"></a>\n",
    "#### 2.2.4. Example command with the Data Catalog Hive Metastore\n",
    "            \n",
    "The [Data Catalog Hive Metastore](https://docs.oracle.com/iaas/data-catalog/using/metastore.htm) provides schema definitions for objects in structured and unstructured data assets. The Metastore is the central metadata repository to understand tables backed by files on Object Storage. As a Data Flow user, you can access the Data Catalog Metastore to securely store and retrieve schema definitions for data assets. For integration with Data Flow, the Metastore provides an invocation endpoint to Data Flow. This endpoint exposes the Hive Metastore interface. [Apache Hive](https://hive.apache.org/) is a data warehousing framework that facilitates read, write, or manage operations on large datasets residing in distributed systems. A Hive Metastore is the central repository of metadata for a Hive cluster. It stores metadata for data structures such as databases, tables, and partitions in a relational database, backed by files on Object Storage. Apache Spark SQL makes use of a Hive Metastore for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4bb06d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": compartment_id,\n",
    "        \"displayName\": \"TestDataFLowSessionDataCatalogHiveMetastore\",\n",
    "        \"language\": \"PYTHON\",\n",
    "        \"sparkVersion\": \"3.2.1\",\n",
    "        \"numExecutors\": 1,\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"type\": \"SESSION\",\n",
    "        \"logsBucketUri\": logs_bucket_uri,\n",
    "        \"metastoreId\": \"<ocid1.datacatalogmetastore...>\",\n",
    "        \"configuration\": {\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\"\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%create_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136472d0",
   "metadata": {},
   "source": [
    "<a id=\"create_session_custom_conda\"></a>\n",
    "#### 2.2.5. Example command with the published conda environment\n",
    "            \n",
    "You can use a published conda environment as a Data Flow runtime environment. \n",
    "* [Creating a Custom Conda Environment](https://docs.oracle.com/en-us/iaas/data-science/using/conda_create_conda_env.htm)\n",
    "* [How to create a new conda environment in OCI Data Science](https://blogs.oracle.com/ai-and-datascience/post/creating-a-new-conda-environment-from-scratch-in-oci-data-science)\n",
    "* [Publishing a Conda Environment to an Object Storage Bucket in Your Tenancy](https://docs.oracle.com/en-us/iaas/data-science/using/conda_publishs_object.htm#:~:text=You%20can%20publish%20a%20conda%20environment%20that%20you%20have%20installed,persist%20them%20across%20notebook%20sessions.)\n",
    "\n",
    "The path to the published conda environment can be copied from the [Environment Explorer](https://docs.oracle.com/en-us/iaas/data-science/using/conda_viewing.htm). <br>\n",
    "\n",
    "Example path : `oci://<your-bucket>@<your-tenancy-namespace>/conda_environments/cpu/PySpark 3.2 and Data Flow/2.0/pyspark32_p38_cpu_v2#conda`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5569c340",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_conda_environment_uri = \"oci://<your-bucket>@<your-tenancy-namespace>/<your-path-to-the-conda-environment>#conda\"\n",
    "\n",
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": compartment_id,\n",
    "        \"displayName\": \"TestDataFLowSessionDataCatalogHiveMetastore\",\n",
    "        \"language\": \"PYTHON\",\n",
    "        \"sparkVersion\": \"3.2.1\",\n",
    "        \"numExecutors\": 1,\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 1, \"memoryInGBs\": 16},\n",
    "        \"type\": \"SESSION\",\n",
    "        \"logsBucketUri\": logs_bucket_uri,\n",
    "        \"configuration\": {\n",
    "            \"spark.archives\": custom_conda_environment_uri,\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\",\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%create_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88aa72f",
   "metadata": {},
   "source": [
    "Use the `%status` magic command to check the status of the current session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b539da67",
   "metadata": {},
   "outputs": [],
   "source": [
    "%status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0745f6b5",
   "metadata": {},
   "source": [
    "Use the `%config` magic command to see the configuration of the current session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8cbc537",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5e1a4e",
   "metadata": {},
   "source": [
    "<a id=\"update_session\"></a>\n",
    "### 2.3. Update Session\n",
    "You can modify the configuration of your running session using the `%update_session` command. For example, Data Flow Sessions can last up to 7 days or 10080 mins (168 hours) (**maxDurationInMinutes**) and have default idle timeout value of 480 mins (8 hours)(**idleTimeoutInMinutes**). Here's how you would update your session and configure for a max session time:<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ae4c752",
   "metadata": {},
   "outputs": [],
   "source": [
    "%update_session -i '{\"maxDurationInMinutes\": 1440, \"idleTimeoutInMinutes\": 420}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "462046b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8993f4f7",
   "metadata": {},
   "source": [
    "<a id=\"configure_session\"></a>\n",
    "### 2.4. Configure Session\n",
    "Configure session allows you to create a cluster configuration that can be later used to create a session. There is a force flag `-f` for immediate effect of the configuration change, in that case the running cluster session will be dropped and recreated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9357cc5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"driverShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"executorShape\": \"VM.Standard.E4.Flex\",\n",
    "        \"driverShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"executorShapeConfig\": {\"ocpus\": 2, \"memoryInGBs\": 32},\n",
    "        \"numExecutors\": 2,\n",
    "        \"configuration\": {\n",
    "            \"fs.oci.client.hostname\": \"https://objectstorage.us-ashburn-1.oraclecloud.com\"\n",
    "        },\n",
    "    }\n",
    ")\n",
    "\n",
    "%configure_session -i $command -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fc18746",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24697a91",
   "metadata": {},
   "source": [
    "<a id=\"stop_session\"></a>\n",
    "### 2.5. Stop Session\n",
    "To stop the current session use the `%stop_session` magic command. You don't need to provide any arguments for this command. The current active cluster will be stopped. All data in memory will be lost. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f36ad4",
   "metadata": {},
   "source": [
    "<a id=\"activate_session\"></a>\n",
    "### 2.6. Activate Session\n",
    "To re-activate the existing session use the `%activate_session` magic command. The `application_id` can be taken from the console UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3019870e",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = prepare_command(\n",
    "    {\n",
    "        \"compartmentId\": \"<compartment_id>\",\n",
    "        \"displayName\": \"<display_name>\",\n",
    "        \"applicationId\": \"<application_id>\",\n",
    "    }\n",
    ")\n",
    "\n",
    "%activate_session -l python -c $command"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e02117",
   "metadata": {},
   "source": [
    "<a id=\"using_existing_session\"></a>\n",
    "### 2.7. Use existing Session\n",
    "To connect to the existing session use the `%use_session` magic command. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d0ca78",
   "metadata": {},
   "outputs": [],
   "source": [
    "%use_session -s <application_id>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8655df7f",
   "metadata": {},
   "source": [
    "<a id=\"basic_examples\"></a>\n",
    "# 3. Basic Spark Usage Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28691cea",
   "metadata": {},
   "source": [
    "A SparkContext (`sc`) and HiveContext (`sqlContext`) are automatically created in the session cluster. The magic commands include the `%%spark` command to run Spark commands in the cluster. You can access information about the Spark application, define a dataframe where results are to be stored, modify the configuration, and so on.\n",
    "\n",
    "The `%%spark` magic command comes with a number of parameters that allow you to interact with the Data Flow Spark cluster. **Any cell content that starts with the `%%spark` command will be executed in the remote Spark cluster.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4abd9d23",
   "metadata": {},
   "source": [
    "<a id=\"examples_pyspark\"></a>\n",
    "### 3.1. PySpark\n",
    "The `sc` variable represents the Spark context and it's available when the `%%spark` magic command is used. The next cell is a toy example of how to use `sc` in a Data Flow Spark Magic cell. The cell calls the [`.parallelize()`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.SparkContext.parallelize.html) method, which creates an [RDD](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.RDD.html), `numbers`, from a list of numbers. Information about the RDD is printed. The [`.toDebugString()`](https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.RDD.toDebugString.html) method returns a description of the RDD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83f755ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark\n",
    "print(sc.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b53c86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark\n",
    "numbers = sc.parallelize([4, 3, 2, 1])\n",
    "print(f\"First element of numbers is {numbers.first()}\")\n",
    "print(f\"The RDD, numbers, has the following description\\n{numbers.toDebugString()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed6d5be",
   "metadata": {},
   "source": [
    "<a id=\"examples_sparksql\"></a>\n",
    "### 3.2. Spark SQL\n",
    "Using the `-c sql` option allows you to run Spark SQL commands in a cell. In this section, the [NYC Taxi and Limousine Commission (TLC) Data](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) dataset is used. The size of the dataset is around **35GB**. \n",
    "\n",
    "The next cell reads the dataset into a Spark dataframe, and then saves it as a view used to demonstrate Spark SQL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fad3ee1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark\n",
    "df_nyc_tlc = spark.read.parquet(\"oci://hosted-ds-datasets@bigdatadatasciencelarge/nyc_tlc/201[1,2,3,4,5,6,7,8]/**/data.parquet\", header=False, inferSchema=True)\n",
    "df_nyc_tlc.show()\n",
    "\n",
    "df_nyc_tlc.createOrReplaceTempView(\"nyc_tlc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05df8f2",
   "metadata": {},
   "source": [
    "The following cell uses the `-c sql` option to tell Data Flow Spark Magic that the contents of the cell is SparkSQL. The `-o <variable>` option takes the results of the Spark SQL operation and stores it in the defined variable. In this case, the `df_nyc_tlc` will be a Pandas dataframe that is available to be used in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f05f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark -c sql -o df_nyc_tlc\n",
    "SELECT vendor_id, passenger_count, trip_distance, payment_type FROM nyc_tlc LIMIT 1000;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489de6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df_nyc_tlc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7638f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nyc_tlc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6124065c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark -c sql -o df_tables\n",
    "SHOW TABLES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cfd76f",
   "metadata": {},
   "source": [
    "Similarly you can use `sqlContext` to query the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f13859ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%spark\n",
    "df_nyc_tlc = sqlContext.sql(\"SELECT * FROM nyc_tlc LIMIT 1000\")\n",
    "df_nyc_tlc.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b55f62",
   "metadata": {},
   "source": [
    "<a id=\"examples_autoviz\"></a>\n",
    "### 3.3. Autovisualization Widget\n",
    "Data Flow Spark Magic comes with [autovizwidget](https://github.com/jupyter-incubator/sparkmagic/tree/master/autovizwidget) and enables the visualization of Pandas dataframes. The `display_dataframe()` function takes a Pandas dataframe as a parameter and generates an interactive GUI in the notebook. It has tabs that allow the visualization of the data in various forms such as tabular, pie charts, scatter plots, and area and bar graphs.\n",
    "\n",
    "The following cell calls `display_dataframe()` with the `df_nyc_tlc` dataframe created in the <a href='#sparkmagic_examples_sparksql'>Spark SQL</a> section of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a86ccb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from autovizwidget.widget.utils import display_dataframe\n",
    "\n",
    "display_dataframe(df_nyc_tlc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9af2c7",
   "metadata": {},
   "source": [
    "<a id='cleanup'></a>\n",
    "# 4. Clean Up\n",
    "Use the `%stop_session` magic command to stop your active Data Flow session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc947b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "%stop_session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393a7ccb",
   "metadata": {},
   "source": [
    "<a id='ref'></a>\n",
    "# 5. References\n",
    "\n",
    "- [ADS Library Documentation](https://accelerated-data-science.readthedocs.io/en/latest/index.html)\n",
    "- [Data Science YouTube Videos](https://www.youtube.com/playlist?list=PLKCk3OyNwIzv6CWMhvqSB_8MLJIZdO80L)\n",
    "- [OCI Data Science Documentation](https://docs.cloud.oracle.com/en-us/iaas/data-science/using/data-science.htm)\n",
    "- [Oracle Data & AI Blog](https://blogs.oracle.com/datascience/)\n",
    "- [Data Flow Policies](https://docs.oracle.com/iaas/data-flow/using/policies.htm/)\n",
    "- [Getting Started with Data Flow](https://docs.oracle.com/iaas/data-flow/using/dfs_getting_started.htm)\n",
    "- [About Data Science Policies](https://docs.oracle.com/iaas/data-science/using/policies.htm)\n",
    "- [Data Catalog Metastore](https://docs.oracle.com/en-us/iaas/data-catalog/using/metastore.htm)"
   ]
  }
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