How To Check Pyspark Version In Databricks

Checking PySpark Version in Databricks

To check the PySpark version in Databricks, you can use several methods:

• Using the Command Line: Before starting Databricks, you can check the PySpark version using the command line with commands like pyspark --version, spark-submit --version, or spark-shell --version if you have access to the underlying environment.
• Within a Databricks Notebook: You can create a SparkSession and print the version directly from the notebook using Python. Here’s how you can do it:

      import pyspark
      from pyspark.sql import SparkSession

      spark = SparkSession.builder.master("local").appName('SparkApp').getOrCreate()
      print('PySpark Version:', spark.version)
    

This will display the version of PySpark being used in your Databricks environment.

Frequently Asked Questions

1. Q: How do I check the Databricks Runtime version?
   
   A: You can check the Databricks Runtime version by using the current_version().dbr_version function in Databricks SQL.
2. Q: Can I use PySpark with older versions of Python?
   
   A: PySpark supports Python 3.7 and later versions. Using older versions may lead to compatibility issues.
3. Q: How do I update PySpark in Databricks?
   
   A: Updating PySpark in Databricks typically involves updating the Databricks Runtime version, as PySpark is included in the runtime.
4. Q: Can I install a specific version of PySpark in Databricks?
   
   A: Databricks manages PySpark versions through its runtime environments. You can choose a runtime that includes the desired PySpark version.
5. Q: How do I display HTML content in a Databricks notebook?
   
   A: You can use the displayHTML function to display HTML content in a Databricks notebook.
6. Q: Is PySpark compatible with all Databricks features?
   
   A: PySpark is fully compatible with most Databricks features, but some advanced features might require specific configurations or versions.
7. Q: Can I use PySpark outside of Databricks?
   
   A: Yes, PySpark can be used independently of Databricks. It is a Python API for Apache Spark and can be installed and used on any compatible environment.

Bottom Line: Checking the PySpark version in Databricks is straightforward and can be done by creating a SparkSession in a notebook or by using command-line tools if available. Understanding the version helps ensure compatibility with your applications and workflows.

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