Are you looking to find out how to create an RDD of PySpark in Azure Databricks cloud or maybe you are looking for a solution, to find a method to create an RDD in PySpark using different methods? If you are looking for any of these problem solutions, you have landed on the correct page. I will also show you how to create an Empty RDD in PySpark Azure Databricks. I will explain it with a practical example. So please don’t waste time let’s start with a step-by-step guide to understand how to create an RDD in PySpark Azure Databricks.
In this blog, I will teach you the following with practical examples:
- Creating RDD using parallelize()
- Creating RDD by reading files
- Creating empty RDD
Contents
- 1 How to create an RDD in PySpark Azure Databricks using the parallelize() function?
- 2 How to create an RDD in PySpark Azure Databricks by reading files?
- 3 How to create an empty RDD in PySpark Azure Databricks?
- 4 When should you create an RDD in PySpark using Azure Databricks?
- 5 Real World Use Case Scenarios for creating an RDD in PySpark Azure Databricks?
- 6 What are the alternatives for creating RDD in PySpark using Azure Databricks?
- 7 Final Thoughts
How to create an RDD in PySpark Azure Databricks using the parallelize() function?
In this section, let’s see how to create an RDD in PySpark Azure Databricks using the parallelize() function. Let me provide an example.
Example:
rdd1 = sc.parallelize([1,2,3,4,5])
print(type(rdd1))
rdd1.collect()
"""
Output:
<class 'pyspark.rdd.RDD'>
[1, 2, 3, 4, 5]
"""
How to create an RDD in PySpark Azure Databricks by reading files?
In this section, let’s see how to create an RDD in PySpark Azure Databricks by reading files. Let me provide an example.
Example:
# replace the file_path with the source file location which you have downloaded.
rdd2 = sc.textFile(file_path)
print(type(rdd2))
rdd2.collect()
How to create an empty RDD in PySpark Azure Databricks?
In this section, let’s see how to create an empty RDD in PySpark Azure Databricks. Let me provide an example.
Example:
# Method 1:
rdd3 = sc.emptyRDD()
rdd3.collect()
# Method 2:
rdd4 = sc.parallelize([])
rdd4.collect()
# The above codes generate the same output
"""
Output:
[]
"""
I have attached the complete code used in this blog in notebook format to this GitHub link. You can download and import this notebook in databricks, jupyter notebook, etc.
When should you create an RDD in PySpark using Azure Databricks?
These could be the possible reasons:
- Schema is unimportant
- Data is not organized
Real World Use Case Scenarios for creating an RDD in PySpark Azure Databricks?
- Assume you have a Dataset with no schema, You can use RDD because RDDs are schemaless.
- Whenever you have data other than structured and semi-structured data, you can use RDD.
What are the alternatives for creating RDD in PySpark using Azure Databricks?
There are multiple alternatives for creating an RDD in a PySpark DataFrame, which are as follows:
- parallelize(): used for creating an RDD from collections
- sc.textFile(): used for creating an RDD by reading a file
- We can also create RDD from an existing RDD
Final Thoughts
In this article, we have learned about how to create an RDD using different methods in PySpark Azure Databricks along with the examples explained clearly. I have also covered different scenarios with practical examples that could be possible. I hope the information that was provided helped in gaining knowledge.
Please share your comments and suggestions in the comment section below and I will try to answer all your queries as time permits.
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- Azure Databricks Lesson 1
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As a big data engineer, I design and build scalable data processing systems and integrate them with various data sources and databases. I have a strong background in Python and am proficient in big data technologies such as Hadoop, Hive, Spark, Databricks, and Azure. My interest lies in working with large datasets and deriving actionable insights to support informed business decisions.
