Added Jupyter notebook for release 1.1.0

examples/release-1.1-examples.ipynb

@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e57c4da7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the following to install the MarkLogic Python client.\n",
+    "# %pip install marklogic_python_client\n",
+    "\n",
+    "# Create an instance of the MarkLogic Python client, pointing at the out-of-the-box Documents database.\n",
+    "\n",
+    "from marklogic import Client\n",
+    "client = Client(\"http://localhost:8000\", digest=(\"python-user\", \"pyth0n\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3872a3ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Insert a MarkLogic TDE view to project rows from documents in the \"employee\" collection.\n",
+    "\n",
+    "from marklogic.documents import Document\n",
+    "tde_view = {\n",
+    "    \"template\": {\n",
+    "        \"context\": \"/\",\n",
+    "        \"collections\": [\"employee\"],\n",
+    "        \"rows\": [{\n",
+    "            \"schemaName\": \"example\",\n",
+    "            \"viewName\": \"employee\",\n",
+    "            \"columns\": [\n",
+    "                {\"name\": \"lastName\", \"scalarType\": \"string\", \"val\": \"Surname\"},\n",
+    "                {\"name\": \"firstName\", \"scalarType\": \"string\", \"val\": \"GivenName\"},\n",
+    "                {\"name\": \"state\", \"scalarType\": \"string\", \"val\": \"State\"},\n",
+    "                {\"name\": \"department\", \"scalarType\": \"string\", \"val\": \"Department\"},\n",
+    "                {\"name\": \"salary\", \"scalarType\": \"int\", \"val\": \"BaseSalary\"}\n",
+    "            ]\n",
+    "        }]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "client.documents.write(\n",
+    "    Document(\n",
+    "        \"/tde/employees.json\", tde_view, \n",
+    "        permissions={\"rest-reader\": [\"read\", \"update\"]}, \n",
+    "        collections=[\"http://marklogic.com/xdmp/tde\"]\n",
+    "    ),\n",
+    "    params={\"database\": \"Schemas\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c72a2506",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load 500 JSON documents into the \"employee\" collection.\n",
+    "\n",
+    "from marklogic.documents import Document, DefaultMetadata\n",
+    "import requests\n",
+    "import json\n",
+    "r = requests.get('https://raw.githubusercontent.com/marklogic/marklogic-spark-connector/master/src/test/resources/500-employees.json')\n",
+    "\n",
+    "docs = [\n",
+    "    DefaultMetadata(permissions={\"rest-reader\": [\"read\", \"update\"]}, collections=[\"employee\"])\n",
+    "]\n",
+    "\n",
+    "for employee in json.loads(r.text):\n",
+    "    docs.append(Document(employee['uri'], json.dumps(employee['value'])))\n",
+    "\n",
+    "client.documents.write(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef22f938",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can use MarkLogic's Optic query language with the view.\n",
+    "\n",
+    "client.rows.query(\"op.fromView('example', 'employee', '').limit(3)\")[\"rows\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae929676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can use SQL queries with the view.\n",
+    "\n",
+    "client.rows.query(sql=\"select * from example.employee order by lastName limit 3\")[\"rows\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1905651c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can use GraphQL queries with the view.\n",
+    "\n",
+    "client.rows.query(graphql=\"query myQuery { example_employee { lastName firstName } }\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb93d17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can return data as CSV for integration with pandas.\n",
+    "\n",
+    "import io\n",
+    "import pandas\n",
+    "\n",
+    "csv_data = client.rows.query(\"op.fromView('example', 'employee', '')\", format=\"csv\")\n",
+    "df = pandas.read_csv(io.StringIO(csv_data))\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "711caba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install matplotlib to visualize data.\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43532cff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple bar chart showing the count of each department.\n",
+    "\n",
+    "df['department'].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "473000f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can use MarkLogic Spark connector with Python.\n",
+    "# First create a Spark session that has access to the MarkLogic Spark connector jar file.\n",
+    "\n",
+    "import os\n",
+    "os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"/Users/rudin/marklogic-spark-connector-2.2.0.jar\" pyspark-shell'\n",
+    "\n",
+    "%pip install pyspark\n",
+    "from pyspark.sql import SparkSession\n",
+    "spark = SparkSession.builder.master(\"local[*]\").appName('My Notebook').getOrCreate()\n",
+    "spark.sparkContext.setLogLevel(\"WARN\")\n",
+    "spark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfdd727d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a Spark DataFrame via a MarkLogic Optic query.\n",
+    "\n",
+    "df = spark.read.format(\"marklogic\") \\\n",
+    "    .option(\"spark.marklogic.client.uri\", \"python-user:pyth0n@localhost:8000\") \\\n",
+    "    .option(\"spark.marklogic.read.opticQuery\", \"op.fromView('example', 'employee', '')\") \\\n",
+    "    .load()\n",
+    "\n",
+    "df.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}