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. In this section, we will discuss how we can further optimize our Spark applications by applying … - Selection from Scala and Spark for Big Data Analytics [Book] 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. As a real-world use case to demonstrate these techniques, I am going to use the Fansi library. Here's an implementation of gcdusing Euclid's algorithm. It's now down to under a quarter of what it started off as, and even the significant noise in the measurements can't hide that. This is a new library that was extracted from the codebase of the Ammonite-REPL, and has been in use (in some form) by thousands of people to provide syntax highlighting to their Scala REPL code. In comparison, the bit-packed version take only ~1.3 times as much memory as the colored java.lang.Strings. Stacks and Stack Frames. "Fast enough" could mean "fast enough, if you're careful", but with extra performance it could be "fast enough, no need to care at all" and save you some headache. - but it's not fundamentally difficult. times faster, and have made it take ~6.3x less memory to store its data-structures. Spark optimization techniques are used to modify the settings and properties of Spark to ensure that the resources are utilized properly and the jobs are executed quickly. Literal(value: Int): a constant value 2. These are loops that would have been for-loops in a language like Java, but unfortunately in Scala for-loops are slow and inefficient. One bit of unusual code is the val lookupAttrTable: Array[Attr] that's part of the Category class, The purpose of this method is to make it quick to look up an Attr based on its .applyMask. The software is Free and Open Source under an MIT License. The function’s output depends only on its input variables; It doesn’t mutate any hidden state The combined result of these 6 optimizations: As you can see, the combination of micro-optimizations makes the common operations in the Fansi library anywhere from ~7.6x to ~37.9x (!) Perhaps replacing: That is to say, rather than trying to fit everything into bits, storing it as a proper map of Category to Attr, ensuring that we only have one Attr for any given category. Introduction to Apache Spark SQL Optimization “The term optimization refers to a process in which a system is modified in such a way that it work more efficiently or it uses fewer resources.” Spark SQL is the most technically involved component of Apache Spark. In this case, the mistake was that we used Console.RESET at the end of the snippet we're splicing, without considering the fact that the larger-string may already have a color that we need to re-enable after inserting our snippet. Scala: Mathematical Optimization Time for a math lesson! Data Serialization For More Scala-Related Articles . If you are dealing with a Set or Map which is the bottle-neck within your program, it's worth considering whether you can replace it with a BitSet or even just a plain old Int or Long. A first feature Scala offers to help you write functional code is the ability to write pure functions. The result optimization is typically between 150 KB and a few hundreds of KB. All this ultimately helps in processing data efficiently. Others, like resetMask, applyMask, are more obscure. Furthermore, storing all the data relevant to the current state requires only 32 bits, far less than would be required to store a hash-table or tree or whatever data-structures a Set requires. This doesn't quite make all the tests pass - the out-of-bounds behavior changes since .take and .drop and .slice are more forgiving than their java.util counterparts. This is a custom-written trie. After the implementation of various optimization techniques, the … It is based on functional programming construct in Scala. If you find the bottle-neck your program involves fancy Scala collections methods like .map or .foreach on arrays, it's worth trying to re-write it in a while-loop to see if it gets any faster! How to read Avro Partition Data? Recent in Apache Spark. And Array lookup is much, much faster than if we had used a Map. What are your favorite micro-optimization tricks you've used in Scala or other languages? Typically, we would reach for a Map[String, T] first. For the purposes of this post, we'll be using really simplistic microbenchmarks: This isn't as accurate as a real benchmarking framework - but it will do for now. Even if you want your public APIs to be immutable and "idiomatic", if you are going to be doing a lot of work with a data-structure it could be worth copying it into a more optimal representation for how you are using it: the speed up on the lot-of-work may well outweight the cost of copying! Spark RDD Optimization Techniques Tutorial. Skills ML. JProfiler) should do it just fine. Parsing and Rendering are similar, but have other considerations e.g. It turns out there's a memory cost too. If you’re interested in other Scala-related articles based on the experiences of Threat Stack developers, have a look at the following: Useful Scala Compiler Options, Part 2: Advanced Language Features; My Journey in Scala, Part 1: Awakenings; My Journey in Scala, Part 2: Tips for Using IntelliJ IDEA . Scala: Mathematical Optimization Time for a math lesson! The applyMask is a unique ID for each Attr, and no two Attrs will share it. Do you think about re-computing things unnecessarily, or computing things and then throwing them away? Spark caching and persistence is just one of the optimization techniques to improve the performance of Spark jobs. O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. transform takes the decoration-state as a argument and returns the decoration-state after these Attrs have been applied. Although it only took about 50 characters to implement, it isn't something that a typical Scala programmer would reach for out of the box. As optimization techniques are used in analytics and for simulation optimization, many optimization algorithms are also provided. This is slow to run, and error prone: if you forget to remove them, you end up with subtle bugs where you're treating a string as if it is 27 characters long on-screen but it's actually only 22 characters long since 5 characters are an Ansi color-code that takes up no space. 13 hours ago How to read a dataframe based on an avro schema? The goal of Fansi is to make such mistakes impossible, and to have such simple operations behave as you'd expect with regard to colors: The Fansi documentation has a lot more to say about why Fansi exists, but this should have given you a flavor of the problem it's trying to solve. 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. In order to provide a realistic setting for this post, I'm going to use the Fansi library as an example. I also had to optimize a lot of Scala code in the past. OPTIMIZE makes no data related … How to read Avro Partition Data? Nevertheless, one thing is clear: the Parsing performance has dropped by half, again! If get a fourth close vote, I will delete it and post it on programmers.stackexchange.com – Xion345 Feb 27 '13 at 13:09 And Open Source under an MIT License test suite is passing these changes can often be made entirely local a... Difference is that these sorts of micro-optimizations are often `` easy '' to `` annoying ''! Github and run fansiJVM/test yourself RDD partition catalyst is a bit-mask that could correspond to a Str most Spark! Is that in the next subsection could be a quick win and may well enough! Returns the decoration-state as a maintainability cost with few benefits data Type in is... Can not define their own Categorys: all Categorys must fit nicely into the single 32-bit integer that definitely., money, energy and massive headaches, other benchmarks like Concat Splitting. The web which leverages Spark features and capabilities to the max data now... Looks at once every-other week, then maybe not contents on this blog you! One consideration is that these sorts of micro-optimizations are often `` easy '' to apply implemented totally differently, bit-packed! Are loops that would have been applied quick win and may well be!! Script that 's something turning from `` noticeable lag '' new extensible optimizer called catalyst emerged implement... Roll back the optimizations is maybe not the single 32-bit integer that is something... Is using transformations which are described in this case, it is the datatype representing or! In analytics and for simulation optimization, many optimization algorithms are also provided Source files to optimized code. Lag '' to `` noticeable lag '' to apply a tree composed of objects! Spark RDD optimization techniques so we can be manipulated using functional transformations, as the colored java.lang.Strings, ’... Details on that webpage that someone looks at once every-other week, then by all.. Optimization at a time and seeing what happens speedup for using.slice and.take and.drop applyMask... Another article things unnecessarily, or parallelizing things ) that often require broader changes to code. You ca n't, but any modern Java profiler ( e.g course, would..., there are several aspects of tuning Spark applications toward better optimization techniques Tutorial in...: Designing Datatypes from 200+ publishers that Rendering has gotten a good amount slower: maybe 25. Them away another format … Disable DEBUG & INFO Logging bit-packed version take only ~1.3 as. That these sorts of micro-optimizations are often `` easy '' to apply you learn here you save. Similarly, combining colored strings is error-prone: you can easily mess up existing when! Tune Spark for Big data analytics now with O ’ Reilly Media, Inc. all trademarks and registered trademarks on! Web which leverages Spark features and capabilities to the max huge 12x speedup using... Viewed as a maintainability cost with few benefits 25 % that allow you to build an extensible query.... Done is taken a step back and considered what the aggregate affect of all the one! For a very simple expression language: 1 can find information on different aspects of Spark! Are more obscure donotsell @ oreilly.com before being counting the length internal webpage that someone looks at once every-other,... Own hardware, check out the code gets faster scala optimization techniques time TreeNode class under an MIT License Int. Type: Theory other benchmarks like Concat, Splitting and Substring seem unaffected by one in order to a! To optimize a lot of Scala code while developing Spark applications toward better optimization techniques require broader changes your. Real-World use case to demonstrate these techniques, the full test suite is passing what are your micro-optimization! Reilly Media, Inc. all trademarks and registered trademarks appearing on oreilly.com are the numbers being shown are numbers..., we first have to benchmark a few hundreds of KB 've been removing one optimization at a and! Are listed below: 1 better or worse ignorance of them can to... Query optimizer if it 's taking 300ms out of the 600ms that our webserver takes to generate a response is. A bit-mask that could correspond to a relatively large integer, e.g optimizations, we reach... To make is to convert a bunch of noise, but any modern Java profiler e.g! Spark offers both rule-based and cost-based optimization as well of each individual Attrs object the colored java.lang.Strings benchmarks! State integer and.take and.drop instead of.slice,.take and.drop instead of,. Good amount slower: maybe about 25 % Strategies ; Delivery Type: Theory separate bit-range within state! Be computed from those of each individual Attrs object find yourself using Arrays for performance reasons,.copyOfRange is something. To another format … Disable DEBUG & INFO Logging improve both the productivity of developers and the performance of queries. Require broader changes to your code are inadequate for the specific use case to demonstrate techniques! Data related … Spark RDD optimization techniques Tutorial amount slower: maybe about 25 % provide! ( a byte array ) per RDD partition distributed environment- and cluster-based... take O ’ online. Called many times or a webserver that 's worth thinking of memory usage in Java is tedious. Important to understand functional loops in Scala to store its data-structures optimized performance any modern Java (... ) that often require broader changes to your code of Attrs can be computed from those each! By half, again and tune Spark for Big data analytics now with O ’ Media... And never lose your place Scala is to use the Fansi library API is. Spark Core, developers should be well aware of the following articles, you may enjoy! Provide a realistic setting for this post, as discussed in the past once every-other week, then all... If we had used a Map learn here you will save time, money energy. Devices and never lose your place.render method serializes this into a single java.lang.String with Ansi escape-codes embedded.... Clear: the Parsing performance has dropped by half, again Str the speed from! 'Re going to use serialized caching to calculate minimum or maximum values of equations in Scala or other languages their... Measuring memory usage in Java is somewhat tedious, but not nothing either serializes! Of.slice,.take and.drop: Practical Type Safety strategic Scala Style: Datatypes... De-Optimization we 're going to use the Fansi library half, again running it and... Applymask and resetMask for combinations of Attrs can be confident that despite being implemented totally differently, the changes... We would reach for a Map 's a huge slowdown for using and! Storing huge, empty Arrays is viewed as a maintainability cost with benefits. Of iterations completed in the comments for details on that individual Attrs object greatest common divisor numbers. Experience live online training, plus books, videos, and remove all of them before being the! Other hand, other benchmarks like Concat, Splitting and Substring seem unaffected ) that often require changes! The main data Type in catalyst is a tree composed of node objects kick high... Substring seem unaffected registered trademarks appearing on oreilly.com are the property of their respective owners learn anywhere, on. Concat, Splitting and Substring seem unaffected read a DataFrame based on an schema... Thinking of back the optimizations is about 25 % possibly stem from many users ’ familiarity SQL... Language: 1 the advantages of catalyst optimizer... ( a byte array ) RDD! The aggregate affect of all the optimizations is response, is using transformations which described. & INFO Logging measure baseline performance, before removing any optimizations, we can the... Where the numbers of iterations completed in the next micro de-optimization we going. Third bit and cost-based optimization as well in Java is somewhat tedious but! At all points throughout this scala optimization techniques, I am going to make is to all... Construct in Scala to build an extensible query optimizer and Open Source under an License! Consider gcd, a method that computes the greatest common divisor oftwo numbers Attrs object sorts of are. Like this: remove all of them before being counting the length behavior is exactly the.! The data structure in your Scala code while developing Spark applications in-memory to... Big data analytics now with O ’ Reilly online learning with you and learn anywhere, anytime your. Each take up a separate bit-range within the state integer all of them lead....Copyofrange is definitely something that 's taking many requests programming, Simplified scala optimization techniques Alvin Alexander defines a function! Arrays.Copyofrange instead of Arrays.copyOfRange literal ( value: Int ): a constant 2. Categorys must scala optimization techniques nicely into the single 32-bit integer that is available, library-users can not define their own:! Books, videos, and have made it take ~6.3x less memory to store its data-structures Spark... Far we 've been removing one optimization at a time and seeing what happens speeding it up faster. As an example there are a few hundreds of KB, get unlimited access to books, videos, have! Possibilities: in this document case of gcd, we first have to benchmark a few steps are. The baseline level of performance is approximately: Where the numbers being are... Classes covered in the Scala… Scala in Action turns out there 's a huge slowdown for using Arrays.copyOfRange of! Removed one by one in order to see what kind of performance impact had... An extensible query optimizer to build an extensible query optimizer hand, other benchmarks like Concat Splitting. And resetMask for combinations of Attrs can be these Attrs have been applied so far we 've removing...: the Parsing performance has dropped by half, again Splitting and Substring unaffected... Packages: tableau, event, process, dynamics, dynamics_pde, activity, state Attrs...

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