{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store JSON documents into Aerospike and query using Spark SQL\n",
"#### Tested with Spark connector 3.2.0, Java 8, Apache Spark 3.0.2, Python 3.7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <font color=blue>The purpose of this notebook is to walk you through how to store data from a JSON source into Aerospike and subsequently query it using Spark SQL. JSON documents are stored as [CDT](https://docs.aerospike.com/docs/guide/cdt.html) in the Aeorspike Database by the Spark connector </font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ensure Database Is Running\n",
"This notebook requires that Aerospike datbase is running."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aerospike database is running!\n"
]
}
],
"source": [
"!asd >& /dev/null\n",
"!pgrep -x asd >/dev/null && echo \"Aerospike database is running!\" || echo \"**Aerospike database is not running!**\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set Aerospike, Spark, and Spark Connector Paths and Parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Directorie where spark related components are installed\n",
"SPARK_NB_DIR = '/opt/spark-nb'\n",
"SPARK_HOME = SPARK_NB_DIR + '/spark-3.0.3-bin-hadoop3.2'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# IP Address or DNS name for one host in your Aerospike cluster\n",
"AS_HOST =\"localhost\"\n",
"# Name of one of your namespaces. Type 'show namespaces' at the aql prompt if you are not sure\n",
"AS_NAMESPACE = \"test\" \n",
"AEROSPIKE_SPARK_JAR_VERSION=\"3.2.0\"\n",
"AS_PORT = 3000 # Usually 3000, but change here if not\n",
"AS_CONNECTION_STRING = AS_HOST + \":\"+ str(AS_PORT)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Aerospike Spark Connector settings\n",
"import os \n",
"AEROSPIKE_JAR_PATH = SPARK_NB_DIR + '/' + \"aerospike-spark-assembly-\" + AEROSPIKE_SPARK_JAR_VERSION + \".jar\"\n",
"os.environ[\"PYSPARK_SUBMIT_ARGS\"] = '--jars ' + AEROSPIKE_JAR_PATH + ' pyspark-shell'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Alternative Setup for Running Notebook in Different Environment\n",
"Please follow the instructions below **instead of the setup above** if you are running this notebook in a different environment from the one provided by the Aerospike Intro-Notebooks container.\n",
"```\n",
"# IP Address or DNS name for one host in your Aerospike cluster\n",
"AS_HOST = \"<seed-host-ip>\"\n",
"# Name of one of your namespaces. Type 'show namespaces' at the aql prompt \n",
"# if you are not sure\n",
"AS_NAMESPACE = \"<namespace>\" \n",
"AEROSPIKE_SPARK_JAR_VERSION=\"<spark-connector-version>\"\n",
"AS_PORT = 3000 # Usually 3000, but change here if not\n",
"AS_CONNECTION_STRING = AS_HOST + \":\"+ str(AS_PORT)\n",
"\n",
"# Set SPARK_HOME path.\n",
"SPARK_HOME = '<spark-home-dir>'\n",
"\n",
"# Please download the appropriate Aeropsike Connect for Spark from the [download page\n",
"# (https://enterprise.aerospike.com/enterprise/download/connectors/aerospike-spark/notes.html) \n",
"# Set `AEROSPIKE_JAR_PATH` with path to the downloaded binary\n",
"import os \n",
"AEROSPIKE_JAR_PATH= \"<aerospike-jar-dir>/aerospike-spark-assembly-\"+AEROSPIKE_SPARK_JAR_VERSION+\".jar\"\n",
"os.environ[\"PYSPARK_SUBMIT_ARGS\"] = '--jars ' + AEROSPIKE_JAR_PATH + ' pyspark-shell'\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spark Initialization"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Next we locate the Spark installation - this will be found using the SPARK_HOME environment variable that you will have set \n",
"\n",
"import findspark\n",
"findspark.init(SPARK_HOME)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pyspark\n",
"from pyspark.context import SparkContext\n",
"from pyspark.sql.context import SQLContext\n",
"from pyspark.sql.session import SparkSession\n",
"from pyspark.sql.types import StringType, StructField, StructType, ArrayType, IntegerType, MapType, LongType, DoubleType"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Configure Aerospike properties in the Spark Session object. Please visit [Configuring Aerospike Connect for Spark](https://docs.aerospike.com/docs/connect/processing/spark/configuration.html) for more information about the properties used on this page."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"sc = SparkContext.getOrCreate()\n",
"conf=sc._conf.setAll([(\"aerospike.namespace\",AS_NAMESPACE),(\"aerospike.seedhost\",AS_CONNECTION_STRING)])\n",
"sc.stop()\n",
"sc = pyspark.SparkContext(conf=conf)\n",
"spark = SparkSession(sc)\n",
"sqlContext = SQLContext(sc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View the JSON Documents that you plan to use for this test"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# JSON data location. For this test you can locate this in the same directory as the Spark connector JAR\n",
"complex_data_json=\"nested_data.json\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"name\": {\"first_name\": \"Megan\", \"last_name\": \"Chang\", \"aliases\": [{\"first_name\": \"Robert\", \"last_name\": \"Green\"}, {\"first_name\": \"William\", \"last_name\": \"Sullivan\"}, {\"first_name\": \"Kristen\", \"last_name\": \"Turner\"}, {\"first_name\": \"Thomas\", \"last_name\": \"Silva\"}, {\"first_name\": \"Rebecca\", \"last_name\": \"Wagner\"}]}, \"SSN\": \"289-18-1554\", \"home_address\": [{\"zip\": 81551, \"street\": {\"street_name\": \"Archer Mountain\", \"apt_number\": 924}, \"city\": \"North Melissaborough\"}, {\"zip\": 73876, \"street\": {\"street_name\": \"Ryan Plain\", \"apt_number\": 877}, \"city\": \"Greenfort\"}, {\"zip\": 72420, \"street\": {\"street_name\": \"Davis Streets\", \"apt_number\": 97}, \"city\": \"Cookchester\"}, {\"zip\": 92728, \"street\": {\"street_name\": \"Lee Parks\", \"apt_number\": 28711}, \"city\": \"Goldenshire\"}, {\"zip\": 64632, \"street\": {\"street_name\": \"Andrea River\", \"apt_number\": 8398}, \"city\": \"Seanstad\"}], \"work_history\": [{\"company_name\": \"Johnston-Roberts\", \"company_address\": {\"zip\": 25324, \"street\": {\"street_name\": \"Johnson Wall\", \"apt_number\": 11220}, \"city\": \"Villanuevaside\"}, \"worked_from\": \"14.04.2020\"}, {\"company_name\": \"Massey, Warren and Boyd\", \"company_address\": {\"zip\": 31368, \"street\": {\"street_name\": \"Jacobson Path\", \"apt_number\": 947}, \"city\": \"New Isabella\"}, \"worked_from\": \"10.03.2020\"}, {\"company_name\": \"Salazar LLC\", \"company_address\": {\"zip\": 83095, \"street\": {\"street_name\": \"James Bridge\", \"apt_number\": 35256}, \"city\": \"Garcialand\"}, \"worked_from\": \"15.05.2020\"}, {\"company_name\": \"Montoya Group\", \"company_address\": {\"zip\": 70519, \"street\": {\"street_name\": \"Jones Coves\", \"apt_number\": 91615}, \"city\": \"Wilsonstad\"}, \"worked_from\": \"07.02.2020\"}, {\"company_name\": \"Lopez, Martinez and Clark\", \"company_address\": {\"zip\": 8507, \"street\": {\"street_name\": \"Johnson Landing\", \"apt_number\": 41314}, \"city\": \"East William\"}, \"worked_from\": \"08.01.2020\"}]}\n"
]
}
],
"source": [
"import json\n",
"\n",
"with open('nested_data.json') as f:\n",
" for line in range(1):\n",
" print(f.readline(),end='')\n",
" \n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the StructType class to create a custom schema. Add columns by providing the column name, data type, and nullable option."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Schema specification\n",
"aliases_type = StructType([\n",
" StructField(\"first_name\",StringType(),False),\n",
" StructField(\"last_name\",StringType(),False)\n",
"])\n",
"\n",
"id_type = StructType([\n",
" StructField(\"first_name\",StringType(),False), \n",
" StructField(\"last_name\",StringType(),False), \n",
" StructField(\"aliases\",ArrayType(aliases_type),False)\n",
"])\n",
"\n",
"street_adress_type = StructType([\n",
" StructField(\"street_name\",StringType(),False), \n",
" StructField(\"apt_number\",IntegerType(),False)\n",
"])\n",
"\n",
"address_type = StructType([\n",
" StructField(\"zip\",LongType(),False), \n",
" StructField(\"street\",street_adress_type,False), \n",
" StructField(\"city\",StringType(),False)\n",
"])\n",
"\n",
"workHistory_type = StructType([\n",
" StructField (\"company_name\",StringType(),False),\n",
" StructField( \"company_address\",address_type,False),\n",
" StructField(\"worked_from\",StringType(),False)\n",
"])\n",
"\n",
"person_type = StructType([\n",
" StructField(\"name\",id_type,False),\n",
" StructField(\"SSN\",StringType(),False),\n",
" StructField(\"home_address\",ArrayType(address_type),False),\n",
" StructField(\"work_history\",ArrayType(workHistory_type),False)\n",
"])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Write JSON documents into Aerospike by specifying the aforementioned schema."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Load the JSON file into a DF with the schema\n",
"cmplx_data_with_schema=spark.read.schema(person_type).json(complex_data_json)\n",
"\n",
"# Save data to Aerospike\n",
"cmplx_data_with_schema \\\n",
".write \\\n",
".mode('overwrite') \\\n",
".format(\"aerospike\") \\\n",
".option(\"aerospike.writeset\", \"complex_input_data\") \\\n",
".option(\"aerospike.updateByKey\", \"name.first_name\") \\\n",
".save()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- name: struct (nullable = true)\n",
" | |-- first_name: string (nullable = true)\n",
" | |-- last_name: string (nullable = true)\n",
" | |-- aliases: array (nullable = true)\n",
" | | |-- element: struct (containsNull = true)\n",
" | | | |-- first_name: string (nullable = true)\n",
" | | | |-- last_name: string (nullable = true)\n",
" |-- SSN: string (nullable = true)\n",
" |-- home_address: array (nullable = true)\n",
" | |-- element: struct (containsNull = true)\n",
" | | |-- zip: long (nullable = true)\n",
" | | |-- street: struct (nullable = true)\n",
" | | | |-- street_name: string (nullable = true)\n",
" | | | |-- apt_number: integer (nullable = true)\n",
" | | |-- city: string (nullable = true)\n",
" |-- work_history: array (nullable = true)\n",
" | |-- element: struct (containsNull = true)\n",
" | | |-- company_name: string (nullable = true)\n",
" | | |-- company_address: struct (nullable = true)\n",
" | | | |-- zip: long (nullable = true)\n",
" | | | |-- street: struct (nullable = true)\n",
" | | | | |-- street_name: string (nullable = true)\n",
" | | | | |-- apt_number: integer (nullable = true)\n",
" | | | |-- city: string (nullable = true)\n",
" | | |-- worked_from: string (nullable = true)\n",
"\n"
]
}
],
"source": [
"cmplx_data_with_schema.printSchema()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Notice that JSON data is stored as CDT in Aerospike "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"aql> show bins \\\n",
"+-------+----------------+-------+-----------------+\\\n",
"| quota | bin | count | namespace |\\\n",
"+-------+----------------+-------+-----------------+\\\n",
"| 65535 | \"home_address\" | 4 | \"testNameSpace\" |\\\n",
"| 65535 | \"name\" | 4 | \"testNameSpace\" |\\\n",
"| 65535 | \"SSN\" | 4 | \"testNameSpace\" |\\\n",
"| 65535 | \"work_history\" | 4 | \"testNameSpace\" |\\\n",
"+-------+----------------+-------+-----------------+\n",
"\n",
"aql> select * from testNameSpace.complex_input_data \n",
"+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"| home_address | name | SSN | work_history |\n",
"+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"\n",
"| LIST('[{\"zip\":33927, \"street\":{\"apt_number\":97293, \"street_name\":\"Davenport Way\"}, \"city\":\"Freemanbury\"}, {\"zip\":30072, \"street\":{\"apt_number\":82109, \"street_name\":\"Fisher Bridge\"}, \"city\":\"New Jon\"}, {\"zip\":34764, \"street\":{\"apt_number\":5944, \"street_na | KEY_ORDERED_MAP('{\"last_name\":\"Deleon\", \"first_name\":\"Kendra\", \"aliases\":[{\"last_name\":\"Reed\", \"first_name\":\"Tammy\"}, {\"last_name\":\"George\", \"first_name\":\"Amanda\"}, {\"last_name\":\"King\", \"first_name\":\"Michael\"}, {\"last_name\":\"Peterson\", \"first_name\":\"Mark | \"472-01-0475\" | LIST('[{\"company_name\":\"Chapman and Sons\", \"company_address\":{\"zip\":43184, \"street\":{\"apt_number\":14913, \"street_name\":\"Sanchez Forks\"}, \"city\":\"Samanthaburgh\"}, \"worked_from\":\"26.04.2020\"}, {\"company_name\":\"Sparks LLC\", \"company_address\":{\"zip\":35836, \" |\n",
"| LIST('[{\"zip\":26201, \"street\":{\"apt_number\":7445, \"street_name\":\"Bradley Islands\"}, \"city\":\"West Jessicaview\"}, {\"zip\":64674, \"street\":{\"apt_number\":905, \"street_name\":\"Stephanie Islands\"}, \"city\":\"Thomasburgh\"}, {\"zip\":87688, \"street\":{\"apt_number\":6942 | KEY_ORDERED_MAP('{\"last_name\":\"Anderson\", \"first_name\":\"Jeff\", \"aliases\":[{\"last_name\":\"Bell\", \"first_name\":\"Nicholas\"}, {\"last_name\":\"Garcia\", \"first_name\":\"Danielle\"}, {\"last_name\":\"Gutierrez\", \"first_name\":\"Jonathan\"}, {\"last_name\":\"Rosales\", \"first_n | \"191-86-2935\" | LIST('[{\"company_name\":\"Mercer Inc\", \"company_address\":{\"zip\":51771, \"street\":{\"apt_number\":76392, \"street_name\":\"Johnson Ways\"}, \"city\":\"East Christopher\"}, \"worked_from\":\"05.05.2020\"}, {\"company_name\":\"Garza Inc\", \"company_address\":{\"zip\":17587, \"stree |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load data from Aerospike CDT into a Spark DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+--------------------+-----------+--------------------+--------------------+\n",
"| name| SSN| home_address| work_history|\n",
"+--------------------+-----------+--------------------+--------------------+\n",
"|[Maria, Bates, [[...|165-16-6030|[[2399, [Ebony Un...|[[Adams-Guzman, [...|\n",
"|[Brenda, Gonzales...|396-98-0954|[[63320, [Diane O...|[[Powell Group, [...|\n",
"|[Bryan, Davis, [[...|682-39-2482|[[47508, [Cooper ...|[[Rivera-Ruiz, [1...|\n",
"|[Tami, Jordan, [[...|001-49-0685|[[23288, [Clark V...|[[Roberts PLC, [4...|\n",
"|[Connie, Joyce, [...|369-38-9885|[[27216, [Goodman...|[[Pugh, Walsh and...|\n",
"+--------------------+-----------+--------------------+--------------------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"loadedComplexDFWithSchema=spark \\\n",
".read \\\n",
".format(\"aerospike\") \\\n",
".option(\"aerospike.set\", \"complex_input_data\") \\\n",
".schema(person_type) \\\n",
".load() \n",
"loadedComplexDFWithSchema.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a view so that you can query Aerospike CDT"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"loadedComplexDFWithSchema.registerTempTable(\"mytable\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Extract distinct company names from the work-history element"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+--------------------+\n",
"| Company|\n",
"+--------------------+\n",
"|[Chapman and Sons...|\n",
"|[Johnson and Sons...|\n",
"|[Mclean Ltd, Kerr...|\n",
"|[Edwards, Rogers ...|\n",
"|[Marshall, Cox an...|\n",
"|[Wolf, Kennedy an...|\n",
"|[Williams Ltd, Jo...|\n",
"|[Smith-Cook, Patt...|\n",
"|[Martin Group, Sp...|\n",
"|[Sutton-Long, Ada...|\n",
"|[Washington Inc, ...|\n",
"|[Valenzuela PLC, ...|\n",
"|[Porter and Sons,...|\n",
"|[Hudson Group, Br...|\n",
"|[Guzman Group, Cu...|\n",
"|[Bowers LLC, Wats...|\n",
"|[Robbins, Harris ...|\n",
"|[Wilson Inc, Pete...|\n",
"|[Elliott-Fuller, ...|\n",
"|[Campbell-Lee, An...|\n",
"+--------------------+\n",
"only showing top 20 rows\n",
"\n"
]
}
],
"source": [
"sqlContext.sql(\"select distinct work_history.company_name as Company from mytable\").show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Look up a record using SSN"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------+-----------+--------------------+\n",
"| first| SSN| Street|\n",
"+------+-----------+--------------------+\n",
"|Brenda|396-98-0954|[Diane Overpass, ...|\n",
"+------+-----------+--------------------+\n",
"\n"
]
}
],
"source": [
"\n",
"sdf = spark.sql(\"select name.first_name as first, SSN, home_address.street.street_name as Street from mytable \\\n",
"where SSN=\\\"396-98-0954\\\"\")\n",
"\n",
"sdf.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Access nested fields"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----+--------------------+--------------------+\n",
"| zip| street| city|\n",
"+-----+--------------------+--------------------+\n",
"| 2399| [Ebony Union, 22]| Robertohaven|\n",
"|70689| [Scott Skyway, 755]| Mclaughlinton|\n",
"|58472|[Tiffany Course, ...| Lake Shannon|\n",
"|89243| [Tapia Rapids, 854]| Karenton|\n",
"|63320|[Diane Overpass, 12]| New Nicholas|\n",
"|60950| [Julie Lock, 52396]| Contrerasville|\n",
"|47508|[Cooper Vista, 59...| Port Tanya|\n",
"|10918| [Jones Plaza, 5430]| Jonesmouth|\n",
"|23288|[Clark Village, 9...| Frankport|\n",
"|79837| [Megan Rest, 561]| Williamsside|\n",
"|36853|[Kayla Orchard, 491]|North Michaelborough|\n",
"|68729| [Hunt Port, 595]| West Jeremy|\n",
"|27216| [Goodman Isle, 73]| Lake Wendy|\n",
"|69643|[Brown Spring, 7872]| North Kristin|\n",
"|93147|[Ryan Freeway, 4316]| South Krystalport|\n",
"|49305| [Ward Bypass, 9262]| South Joyland|\n",
"|99893|[Knight Courts, 1...| Lake Williamfort|\n",
"|89962|[Meghan Highway, ...| Port Garyton|\n",
"|38066|[Richard Stream, ...| New Holly|\n",
"|36042| [Eric Haven, 1741]| Patriciatown|\n",
"+-----+--------------------+--------------------+\n",
"only showing top 20 rows\n",
"\n"
]
}
],
"source": [
"sdf1 = spark.sql(\"select home_address as address from mytable\")\n",
"explode_df = sdf1.selectExpr(\"explode(address) AS structCol\").selectExpr(\"structCol.*\")\n",
"explode_df.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resources:\n",
"1. [Query JSON in Python using Spark SQL](https://medium.com/@clementselvaraj/https-medium-com-querying-json-in-python-using-spark-sql-a08761946dd2)\n",
"2. [An introduction to JSON support in Spark SQL](https://databricks.com/blog/2015/02/02/an-introduction-to-json-support-in-spark-sql.html)\n",
"3. [Working with JSON in Apache Spark](https://medium.com/expedia-group-tech/working-with-json-in-apache-spark-1ecf553c2a8c)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.8.6"
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"nbformat": 4,
"nbformat_minor": 4
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