How To Check Spark Version In Databricks

Checking Spark Version in Databricks

To check the Spark version in Databricks, you can use the SQL function version(). This function returns the Apache Spark version as a string, including the release version and a git revision.

Here’s how you can use it:

      SELECT version();
    

This will output something like “1.1.0 a6d6ea3efedbad14d99c24143834cd4e2e52fb40”, where “1.1.0” is the release version and “a6d6ea3efedbad14d99c24143834cd4e2e52fb40” is the git revision.

Frequently Asked Questions

• Q: How do I check the Databricks SQL version?

  A: You can check the Databricks SQL version using the current_version() function. Specifically, use SELECT current_version().dbsql_version; to get the Databricks SQL version.

• Q: Can I display HTML content in Databricks notebooks?

  A: Yes, you can display HTML content in Databricks notebooks using the displayHTML() function.

• Q: How do I check the PySpark version in a Databricks environment?

  A: In a Databricks environment, you can check the PySpark version by using the SparkContext or SparkSession object in Python. For example, you can use spark.sparkContext.version or spark.version after creating a SparkSession.

• Q: Can I mix HTML and Markdown in Databricks notebooks?

  A: No, currently, you cannot mix HTML and Markdown in Databricks notebooks. You can use either Markdown or the displayHTML() function for HTML content.

• Q: How do I check the Spark version from the command line?

  A: You can check the Spark version from the command line by using commands like pyspark --version, spark-submit --version, or spark-shell --version.

• Q: Can I display images using the displayHTML function?

  A: Yes, you can display images using the displayHTML function in Databricks notebooks. However, the images must be accessible via a URL or stored in a connected database.

• Q: Are there any limitations to using the displayHTML function?

  A: Yes, there is a limitation that you can only use one displayHTML function per cell in a Databricks notebook.

Bottom Line

Checking the Spark version in Databricks is straightforward using the version() SQL function. Additionally, Databricks offers versatile tools like displayHTML for enhancing notebook content and various methods for checking PySpark versions.

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