Debugging Spark Code

Kevin Feasel

2016-10-25

Spark

Vida Ha has an article on troubleshooting when writing code using the Spark APIs:

When working with large datasets, you will have bad input that is malformed or not as you would expect it. 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.

A 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). 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.

This is a good set of tips.

Related Posts

SparkSession Versus SparkContext

Abhishek Baranwal explains the differences between the SparkSession object and the SparkContext object when writing Spark code: Prior to spark 2.0, SparkContext was used as a channel to access all spark functionality. The spark driver program uses sparkContext to connect to the cluster through resource manager. SparkConf is required to create the spark context object, […]

Read More

Hadoop + SQL Server In 2019

Travis Wright shows off a big part of what the SQL Server team has been working on the last couple of years: SQL Server 2019 big data clusters provide a complete AI platform. Data can be easily ingested via Spark Streaming or traditional SQL inserts and stored in HDFS, relational tables, graph, or JSON/XML. Data […]

Read More

Categories

October 2016
MTWTFSS
« Sep Nov »
 12
3456789
10111213141516
17181920212223
24252627282930
31