Скачать или смотреть How to Use if-else Statements in Scala for Databricks Streaming

Learn how to efficiently run `if-else` statements in Scala with Databricks streaming to trigger different actions based on data frame content.
---
This video is based on the question https://stackoverflow.com/q/67129379/ asked by the user 'xyz' ( https://stackoverflow.com/u/6729977/ ) and on the answer https://stackoverflow.com/a/67177554/ provided by the user 'xyz' ( https://stackoverflow.com/u/6729977/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to run an if else statement in Scala in Databricks streaming

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Introduction

Are you new to both Scala and Databricks streaming? Are you trying to handle streaming data and conditionally execute actions based on whether your data frame is empty? If you've encountered errors while using if-else statements in your code—such as the dreaded AnalysisException—you're not alone. This guide will guide you through a simple yet effective way to incorporate conditional logic into your streaming processes in Databricks.

Understanding the Challenge

When working with streaming data in Databricks, you may want to trigger different notebooks or actions based on whether your data frame has content. The initial code you might write could look something like this:

[[See Video to Reveal this Text or Code Snippet]]

However, trying to execute such conditions directly can lead to an AnalysisException like this:

[[See Video to Reveal this Text or Code Snippet]]

This error highlights the fundamental difference in how streaming queries operate compared to batch processing. In streaming contexts, you must leverage the writeStream function to handle data properly.

The Solution

To effectively manage your streaming data with conditional statements in Scala on Databricks, follow these steps:

Step 1: Use writeStream.foreachBatch

Instead of trying to evaluate the data frame directly, utilize the writeStream.foreachBatch method. This allows you to process each micro-batch of data from the stream.

Sample Code

Here’s a basic structure that shows how to implement this:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Define Your Conditional Logic

Now, define the function (myfunc) that contains your if-else logic. This function should filter the data frame and check the conditions you are interested in.

Example Implementation

[[See Video to Reveal this Text or Code Snippet]]

In this example:

Two filters (test1 and test2) are set based on a column's content.

The if statements check if there are any records in each filtered data frame and, depending on the results, trigger a different notebook.

Step 3: Execute Your Streaming Job

Once you have the above components in place, you can start your stream processing. The system will handle each incoming batch of data, executing your logic only when relevant data arrives.

Conclusion

Incorporating if-else statements with streaming data in Scala within Databricks is entirely feasible through careful use of writeStream.foreachBatch. This approach allows you to manage and respond to dynamic data effectively.

Whether you're looking to trigger different notebooks or actions based on your streaming events, this method streamlines the process, helping you become more proficient in Scala and Databricks. Happy coding!