Setting Spark Configuration in Databricks
Setting Spark configurations in Databricks is crucial for optimizing and customizing your Spark applications. Here’s how you can do it:
Using Python
To set a Spark configuration in Python, you can use the spark.conf.set() method. Here’s an example:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark.conf.set("spark.sql.ansi.enabled", "true")
Using SQL
In Databricks SQL, you can set configurations using the SET command:
SET spark.sql.ansi.enabled = true
Configuring at Compute Level
You can also configure Spark properties at the compute level, which applies to all notebooks and jobs using that compute resource. This is typically done through the Databricks UI.
Frequently Asked Questions
FAQs
- Q: How do I get all Spark configurations in Databricks?
A: You can get all Spark configurations using the following Python code:
from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() all_conf = spark.sparkContext.getConf().getAll() - Q: Can I use Markdown with HTML in Databricks notebooks?
A: No, you cannot mix Markdown with HTML directly in Databricks notebooks. However, you can use the
displayHTML()function to display HTML content. - Q: How do I display images in Databricks using HTML?
A: You can display images using the
displayHTML()function by referencing the image URL or path:displayHTML("
")
- Q: Can I configure Spark properties for serverless notebooks?
A: Yes, but only a limited set of properties can be configured for serverless notebooks, such as
spark.databricks.execution.timeoutandspark.sql.legacy.timeParserPolicy. - Q: How do I check the Databricks runtime version?
A: You can check the Databricks runtime version using the following Python code:
from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() spark.conf.get("spark.databricks.clusterUsageTags.sparkVersion") - Q: Can I set Spark configurations for Delta Live Tables pipelines?
A: Yes, you can configure Spark properties for Delta Live Tables pipelines using the UI or JSON configurations.
- Q: How do I submit feedback for new features in Databricks?
A: You can submit feedback for new features, such as mixing HTML and Markdown, through the Azure Databricks feedback portal.
Bottom Line
Setting Spark configurations in Databricks is essential for optimizing performance and customizing your Spark applications. While Databricks recommends using default configurations for most properties, understanding how to set configurations can help you fine-tune your workloads. Additionally, leveraging features like displayHTML() can enhance the presentation of your results in notebooks.