diff --git a/examples/release-1.1-examples.ipynb b/examples/release-1.1-examples.ipynb new file mode 100644 index 0000000..afb8956 --- /dev/null +++ b/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": 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