![]() Tip 3: Use the debugging tools in Databricks notebooks. If you want to fix your input data or to drop it if you cannot, then using a flatMap() operation is a great way to accomplish that. I recommend being proactive about deciding for your use case, whether you can drop any bad input, or you want to try fixing and recovering, or otherwise investigating why your input data is bad.Ī filter command is a great way to get only your good input points or your bad input data (If you want to look into that more and debug). When working with large datasets, you will have bad input that is malformed or not as you would expect it. It will allow you to measure the running time of each individual stage and optimize them. This is a useful tip not just for errors, but even for optimizing the performance of your Spark jobs. When debugging, you should call count() on your RDDs / Dataframes to see what stage your error occurred. ![]() Therefore, you’ll want to factor your code such that you can store intermediary RDDs / Dataframes as a variable. ![]() While it’s great that Spark follows a lazy computation model so it doesn’t compute anything until necessary, the downside is that when you do get an error, it may not be clear exactly where the error in your code appeared. Tip 1: Use count() to call actions on intermediary RDDs/Dataframes. Here are some tips for debugging your Spark programs with Databricks. In a perfect world, we all write perfect Apache Spark code and everything runs perfectly all the time, right? Just kidding - in practice, we know that working with large datasets is hardly ever that easy - there is inevitably some data point that will expose any corner cases with your code. She is an early user of Apache Spark and has been helping Databricks customers building production applications for over two years. She has over a decade of experience building big data applications at Google, Square, and Databricks. Vida Ha is a lead solution architect at Databricks.
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