Python Spark MongoDB Connection: A Comprehensive Guide 101

Apache Spark is an advanced Computing Engine that focuses on speed, usability, and algorithmic capable analytics. Spark performs especially well when quick functioning is required.

MongoDB is a popular NoSQL database that is used for real-time data analysis on organizational data.

Also, Apache Spark expands core analytics to include real-time analysis tools and Machine Learning training models.

Upon a complete walkthrough of this article, you will gain a decent understanding of Python Spark MongoDB Connection and Workflow. Read along to learn more about the Python Spark MongoDB Connection!

Prerequisites

• Basic knowledge of Apache Spark and MongoDB Servers.
• Running MongoDB instances is required while creating connections to MongoDB. The version on instance should be greater than version 2.6 and above.
• The spark version should be 2.4.x, and Scala should be 2.12.x.

What is Apache Spark?

Today, Data scientists are eagerly interested in studying more about Natural Language Processing and other Machine Learning technologies.

When Big Data technology was introduced, Apache Spark quickly gained attraction in the modern age of network data processing because it provides a simple-to-use, flexible, and in-memory approach compared to the MapReduce approach.

Apache Spark is a Distributed Processing System that is Open Source and used for Big Data workloads. For fast analytic queries against any size of data, it uses in-memory caching and optimized query execution.

Due to these features, it has overtaken the MapReduce approach in terms of marketplace usability. There are no restrictions on how it will be utilized with de-centralized storage systems such as HDFS, Apache Cassandra, and MongoDB.

What is MongoDB?

MongoDB is a NoSQL database that is becoming widely popular. This database’s exceptional capability is to manage document-oriented content by utilizing the designed distribution, management, and replication features.

It allows scaling horizontally while also maintaining high provision and reliability.

You can change the structure of records (which MongoDB refers to as Documents) by simply adding new fields or deleting existing ones.

This feature of MongoDB allows you to easily represent Hierarchical Relationships, Store Arrays, and other complex Data Structures.

Nowadays, many tech giants, including Facebook, eBay, Adobe, and Google, use MongoDB to store colossal amounts of data.

Python Spark MongoDB Connection & Workflow

Here we are using the pyspark shell while setting up MongoDB Spark Connector Python. PySpark is responsible for connecting Python API to the Spark core and setup the Spark context. Many Python applications can set up spark context through self-contained code.

Python Spark Shell

When we start with the Python Spark shell, We need to set up some constraints and specify them according to our needs. It includes:

• Downloading and installing the MongoDB Spark Connector package using the  – – packages option.
• The MongoDB Spark Connector can be configured using the –conf function option.

Whenever you define the Python Spark MongoDB Connector configuration using SparkConf, you must ensure that all settings are initialized correctly.

Example Scenario

Here we take the example of Python spark-shell to MongoDB. It should be initialized with command-line execution. We are using databases and collections here.

• Use spark.mongodb.input.uri to provide the MongoDB server address (127.0.0.1), the database to connect to (test), the collections (myCollection) from where to read data, and the reading option. The databases to connect to MongoDB are determined by the spark.mongodb.connection.uri.
• When using the spark.mongodb.output.uri parameter, you can specify the MongoDB server IP (127.0.0.1), the databases to connect to (test), and the collections (myCollection) where data write to get an output of the SparkSession. By default, it creates a connection to port 27017.
• The packages provide the Apache location coordinates for the Spark Connection in the configuration groupId:artifactId:version.

Creating SparkSession Object

When you first run pyspproviderk, we create a spark SparkSession object. You must explicitly build your SparkSession. The following example shows the Sparksession my_spark in Python application only.

Then, the standard SparkSession object uses the configuration settings you gave when you launched pyspark, including the spark.mongodb.input.uri and spark.mongodb.output.uri config options. 

When you prefer to configure a custom SparkSession object within pyspark, you can call SparkSession.builder and specify various configuration parameters. Alternatively, you can use SparkSession.builder and specify other customization settings.

from pyspark.sql import SparkSession

my_spark = SparkSession 
   .builder 
   .appName("PyApp") 
   .config("spark.mongodb.input.uri", "mongodb://127.0.0.1/test.coll") 
   .config("spark.mongodb.output.uri", "mongodb://127.0.0.1/test.coll") 
.getOrCreate()

Using the SparkSession object, you may conduct actions such as writing data to MongoDB, reading data from MongoDB, creating DataFrames, and performing SQL queries.

Read Data from MongoDB

When utilizing the spark.mongodb.input.uri parameter in your SparkSession option, you can build a Spark DataFrame to store data from the MongoDB database given in the spark.mongodb.input.uri function in your SparkSession method.

Example:

Consider the Collection of data named Cars that contain the following document:

{ "_id" : 1, "company" : "Tata", "quantity" : 10 }
{ "_id" : 2, "company" : "Mercedeze", "quantity" : 15 }
{ "_id" : 3, "company" : "Jeep", "quantity" : 20 }

Python Spark MongoDB may bind the collections to a DataFrame with spark.read(). This process is to be performed inside the pyspark shell.

spark.read.format("Tata"). load()

Spark performs a sampling operation to deduce the collection configuration for each record in the data collection.

The Python spark-shell output is generated by the preceding operation below.

root
|-- id: double (nullable = true)
|-- quantity: double (nullable = true)
|-- company: string (nullable = true)

The .option function can read data into an array of DataFrames if you want to interpret from a distinct MongoDB collection.

If you want to read data from a collection named a city in a country dataset, you can use the people parameter like country.city in the URI tag for the input function.

spark.read.format("Tata").option("uri",
"mongodb://127.0.0.1/country.city").load()

In this way, we can read data from MongoDB through the Python Spark connection. Next, let us look into writing to the MongoDB function.

Write to MongoDB

A DataFrame is formed by first creating a SparkSession object and then calling the createDataFrame() method on that object. createDataFrame() in the below scenario accepts a collection of tuples that include country and prices.

country = spark.createDataFrame([("India",  50000), ("USA", 30000), ("China", 20000)], ["country", "price"])

The write method can be used to write the country DataFrame to the MongoDB dataset and collection defined in spark.mongodb.output.uri.

country.write.format("mongo").mode("append").save()

When you connect to the pyspark shell, the spark.mongodb.output.uri method specifies the MongoDB dataset and collection to write to it. So by country.show() function data prints in pyspark in the below form.

country	price
India	50000
USA	30000
China	20000

And in pyspark shell, output results are,

root
 |-- _id: struct (nullable = true)
 |    |-- oid: string (nullable = true)
 |-- price: long (nullable = true)
 |-- country: string (nullable = true)

You can use the .option() method in the function with .write() to start writing to a distinct MongoDB collection. We can use the same URI logic of reading from MongoDB here as well.

Benefits of using Python Spark MongoDB 

The key benefits of the Python Spark MongoDB connector are as follows:

• Easily read and write BSON documents directly to/from MongoDB.
• Conversion of MongoDB Collections to Spark RDD.
• Integration between MongoDB aggregation pipelines. 
• Spark has a predicate pushdown that prevents memory from being loaded unnecessarily with the filtered data.
• Spark MongoDB setup fetches data based on cluster location. It saves network traffic when uploading data into Spark units.

Conclusion

This article introduced you to Python Spark MongoDB Connection & Workflow in detail. MongoDB and Apache Spark allow outcomes by transforming data into actionable real-time scenarios.

Developers can create more useful apps through Python in less time with Spark and MongoDB connections. It is built on single database architecture.

As we see, MongoDB connections simplify the interaction between Spark and MongoDB, which develops a robust combination of sophisticated Python applications.

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