![]() If your data is NOT sortable - or you don’t want to change the current order of your dataĪnother option, is to combine row_number() with monotonically_increasing_id(), which according to the documentation creates: But you might end up with an OOM Exception, as I’ll explain in a bit.No extra work to reformat your dataframe.You will need to have all your data in the dataframe - updates will not add an auto-increment id.You will need to work with a very big window (as big as your data).Row_number() is a windowing function, which means it operates over predefined windows / groups of data. Resuming from the previous example - using row_number over sortable data to provide indexes I hope they are more helpful than they are confusing :). Practicing Sketchnoting again, yes, there are terrible sketches through out the article, trying to visually explain things as I understand them.I’ll be glad to answer any questions I can :). If not, here is a short intro with what it is and I’ve put several helpful resources in the Useful links and notes section. Please, note that this article assumes that you have some working knowledge of Spark, and more specifically of PySpark.Throughout this post, we will explore the obvious and not so obvious options, what they do, and the catch behind using them. What happens though when you have distributed data, split into partitions that might reside in different machines like in Spark? When the data is in one table or dataframe (in one machine), adding ids is pretty straigth-forward. Even though Scala manages to deal with some of the pain from Java’s type system, at the end of the day you are still compiling to the JVM and have to be aware of type erasure.A representation of a Spark Dataframe - what the user sees and what it is like physicallyĭepending on the needs, we might be found in a position where we would benefit from having a (unique) auto-increment-ids’-like behavior in a spark dataframe. We use the -unchecked option to provide us with more detailed information about type erasure warnings. In the rare event that a file gets saved with a different encoding, the compiler can catch this issue and warn us about it. Not all of our developers use the same operating systems, and sometimes these different operating systems have different default character encodings. It is important to note that the -encoding option actually takes an argument (in our case “UTF-8”). We like to set the encoding of all our files with the -encoding option. It’s important to note that while JVMs 1.5 through 1.8 are supported, JVM 1.5 is deprecated, and its use will generate a warning. At Threat Stack, we target JVM 1.8, so we use the -target:jvm-1.8. We always define what our target JVM is for object files. Here is an example of some basic flags that we use: scalacOptions ++= Seq( ![]() Note: Part 2 of this series is now available: Useful Scala Compiler Options, Part 2: Advanced Language Features Generally Useful Flags Note: At Threat Stack we use Scala 2.11.8 and Java 8, so all the examples and options in this post will focus on these versions of Scala and the JVM. Where applicable, I’ll show examples of issues in code that will be caught by the compiler once the correct options have been enabled. I’ll discuss ones that we use at Threat Stack as well as other common options that we have opted not to use. In this series, I’ll cover a number of Scala Compiler options that ease development. ( Rob Norris’ Blog comes to mind.) However, most of the resources don’t really explain why you want to enable these options. And, as it turns out, the Scala Compiler offers a number of features that makes our lives way better!Ī couple of resources make great recommendations about what options you should pass to the Scala Compiler. Since we use Scala, it only makes sense for us to always be looking into ways of getting the most out of the Scala Compiler to enhance our productivity. At Threat Stack, we like to leverage our tools to the fullest.
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