Error Handling In Scala

Manish Mishra gives a few examples of how to handle errors in Scala:

Try[T] is another construct to capture the success or a failure scenarios. It returns a value in both cases. Put any expression in Try and it will return Success[T] if the expression is successfully evaluated and will return Failure[T] in the other case meaning you are allowed to return the exception as a value. However with one restriction that it in case of failures it will only return Throwable types:

def validateZipCode(zipCode:String): Try[Int] = Try(zipCode.toInt)

But Throwing an exception doesn’t make much sense here since it is not much of a calculation. Although we can take this example to understand the use case. If the given string is not a number, it will be a failure. The value from the Try can be extracted in same as Option. It can be matched

As you write more complicated Spark operations, handling errors becomes critical.

Related Posts

AWS Glue Now Supports Scala

Mehul Shah, et al, announce that AWS Glue officially supports Scala: We are excited to announce AWS Glue support for running ETL (extract, transform, and load) scripts in Scala. Scala lovers can rejoice because they now have one more powerful tool in their arsenal. Scala is the native language for Apache Spark, the underlying engine that AWS […]

Read More

Set Operations In Spark

Fisseha Berhane compares SparkSQL, DataFrames, and classic RDDs when performing certain set-based operations: In this fourth part, we will see set operators in Spark the RDD way, the DataFrame way and the SparkSQL way. Also, check out my other recent blog posts on Spark on Analyzing the Bible and the Quran using Spark and Spark […]

Read More

Categories

December 2017
MTWTFSS
« Nov Jan »
 123
45678910
11121314151617
18192021222324
25262728293031