JProfiler) should do it just fine. A typical library or application likely won't see the same kind of speedups that Fansi did for so little work: often the time spent is spread over much more code and not concentrated in a few loops in a tiny codebase, like the Fansi benchmarks were. Terms of service • Privacy policy • Editorial independence, Get unlimited access to books, videos, and. There are two possibilities: In this case, it is the latter, and we are done micro-optimizing render. choosing efficient algorithms, caching things, or parallelizing things) that often … * with the corresponding RichArray operations. Hence, the resetMask of Attrs tells you which bit-ranges need to be cleared in order for the Attrs to be applied, and the applyMask tells you what those bit-ranges will get set to. For distributed environment- and cluster-based ... Take O’Reilly online learning with you and learn anywhere, anytime on your phone and tablet. These are non trivial performance gains to be had; but are they worth the cost? The following is not meant to be a complete list, just a few practical observations that might help you: Yes, replacing a for loop by a while is faster, even with Scala 2.10. But what we haven't done is taken a step back and considered what the aggregate affect of all the optimizations is! the speed of the actual parser and the speed of the output-string-generation as part of the benchmark, while Overlay is almost entirely bottlenecked on the Attrs#transform operations. The main data type in Catalyst is a tree composed of node objects. The next micro de-optimization we're going to make is to convert a bunch of our while-loops to for-loops. With the techniques you learn here you will save time, money, energy and massive headaches. You do not need to re-architect your application, implement a persistent caching layer, design a novel algorithm, or make use of multiple cores for parallelism. Catalyst optimization allows some advanced programming language features that allow you to build an extensible query optimizer. Creativity is one of the best things about open source software and cloud computing for continuous learning, solving real-world problems, and delivering solutions. I also had to optimize a lot of Scala code in the past. About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. A first feature Scala offers to help you write functional code is the ability to write pure functions. 13 hours ago How to write Spark DataFrame to Avro Data File? The remaining bits are un-used. There are a number of usages: This is a relatively straightforward change; it makes the code considerably shorter, and is probably what most Scala programmers would do if they were implementing this themselves. 2.1. The result optimization is typically between 150 KB and a few hundreds of KB. These objects are immutable and can be manipulated using functional transformations, as discussed in the next subsection. Nevertheless, it is a long diff, so feel free to skip over it to continue reading if it doesn't interest you: Despite the fact that this is a widespread change, all tests pass after (except the not-applicable ones we deleted). For More Scala-Related Articles . Broadcast joins may also have other benefits (e.g. If it's taking 300ms out of the 600ms that our webserver takes to generate a response, is it worth it then? If you want to try it on your own hardware, check out the code from Github and run fansiJVM/test yourself. Tags: optimization, spark. After the implementation of various optimization techniques, the … This is one of the simple ways to improve the performance of Spark … You can easily define objects and values in the Scala REPL: and ask JProfiler how big they are via it's Biggest Objects tab: Spending a few minutes running this over and over on a range of string lengths using different kinds of strings, we can quickly see how much memory is being taken up by various data structures: It turns out that the case class/Map representation of a Str.State takes ~6.3 times as much memory as the bit-packed version! However the .applyMask itself is a bit-mask that could correspond to a relatively large integer, e.g. If our code is taking 0.1ms out of a batch process that takes 10 minutes to run, it's certainly not worth bothering to optimize. Micro-optimization has a bad reputation, and is especially uncommon in the Scala programming language where the community is more interested in other things such as proofs, fancy usage of static types, or distributed systems. One of the most important aspects is garbage collection, and it's tuning if you have written your Spark application using Java or Scala. This is a tiny library that I wrote to make it easier to deal with color-coded Ansi strings: This library exists because dealing with raw java.lang.Strings with Ansi escape codes inside is troublesome, slow and error-prone. We dive deep into Spark and understand what tools you have at your disposal - and you might just be surprised at how much leverage you have. All this ultimately helps in processing data efficiently. The most frequent performance problem, when working with the RDD API, is using transformations which are inadequate for the specific use case. Attribute(name: String):an attribute from a… Here, an in-memory object is converted into another format that can be stored in … Do you need to design your application to avoid doing redundant work? While we claim above that micro-optimizations result in "less idiomatic", "more verbose", "harder to extend" or "less maintainable" code, there is a flip side to it: if you need to implement persistent caching, design novel algorithms, or start multi-threading your code for performance, that could easily add far more complexity and un-maintainability than a few localized micro-optimizations. Each node has a node type and zero or more children. To measure baseline performance, before removing any optimizations, we first have to benchmark a few basic operations. Iterating over an Array is faster than iterating over a Vector, and this one is in the critical path for the .render method converting our fansi.Strs into java.lang.Strings. Scala: Mathematical Optimization Time for a math lesson! Data Serialization. The output of this function is the Spark’s execution plan which is the output of Spark query engine — the catalyst The only benchmark that hasn't changed is the Concat benchmark of the ++ operation: I guess Array#++ is already reasonably efficient and there's no speed-up to be had. The software is Free and Open Source under an MIT License. 3.0.1. If you're dealing with a lot of Map[String, T]s, and find that looking up things in those maps is the bottleneck in your code, swapping in a Trie could give a great performance boost. If your library is "fast enough, if you're careful" then you'll need to think about those things. That means that applying a set of Attrs to the current state Int is always just three integer instructions: And thus much faster than any design using structured data like Set objects and the like. Then roll back the optimizations one by one in order to see what kind of performance impact they had. What's the take-away? This post will use the Fansi library as a case-study for what benefits you get from micro-optimizing Scala: swapping out elegant collection transformations for raw while-loops over mutable Arrays, elegant case classs for bit-packed integers. For example, storing our Str.State in a bit-packed Int rather than a Map[Category, Attr] makes it blazing fast, but it also means that: Library-users cannot define their own Attrs: they have to be known in advance, and fit nicely into the bit-ranges assigned to them. It is based on functional programming construct in Scala.