scala optimization techniques

Optimization results. Nevertheless, people often write for-loops naturally and only optimize it later. - but it's not fundamentally difficult. At all points throughout this post, as the various optimizations are removed one by one, the full test suite is passing. 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. This is one of the simple ways to improve the performance of Spark … But it moves fast and covers a lot of ground with Scala performance. Although allocating this array costs something, it's the Attr.categories vector only has 5 items in it, so allocating a 5-element array should be cheap. The optimizer can also be called programmatically using the class ScalaJSClosureOptimizer in the Scala… These tools, and others like them, can be used to make it run faster: While these techniques are often looked down upon in programming circles - with attitudes ranging from "the computer is fast enough" to "the JIT compiler will take care of it" - hopefully this post demonstrates that they still can have a powerful effect, and deserve a place in your programmer's toolbox. As a real-world use case to demonstrate these techniques, I am going to use the Fansi library. After all, it isn't uncommmon for people to treat Array[T]s as normal Scala collections using the extension methods in RichArray! As we make progress, the profile changes, and hopefully the code gets faster each time. Spark caching and persistence is just one of the optimization techniques to improve the performance of Spark jobs. Get Scala and Spark for Big Data Analytics now with O’Reilly online learning. That's something turning from "noticeable lag" to "annoying delay". 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. Others, like resetMask, applyMask, are more obscure. The L-BFGS method approximates the objective function locally as a quadratic without evaluating the second partial derivatives of the … For RDD cache() default storage level is ‘MEMORY_ONLY‘ but, for DataFrame and Dataset, default is ‘MEMORY_AND_DISK‘ On Spark UI, the Storage tab shows where partitions exist in memory or disk across the cluster. How to read Avro Partition Data? But what we haven't done is taken a step back and considered what the aggregate affect of all the optimizations is! times faster, and have made it take ~6.3x less memory to store its data-structures. Thus, to turn the state Int's foreground-color light green, you first zero out 4th to the 12th bit, and then set the 4th, 5th and 7th bits to 1. It includes Scala’s pattern matching and quasi quotes. What's the take-away? 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. It’s one of the cheapest and most impactful performance optimization techniques you can use. It's time to kick the high gear and tune Spark for the best it can be. 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 It shares all the properties of java.lang.String, for better or worse. The next micro de-optimization we're going to make is to convert a bunch of our while-loops to for-loops. 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. And ~8.5 times as much memory as the colored java.lang.Strings. * with the corresponding RichArray operations. The most popular Spark optimization techniques are listed below: 1. L-BFGS is an optimization algorithm in the family of quasi-Newton methods to solve the optimization problems of the form minw ∈ Rdf(w). Core Competencies. 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. Note we did not change the while loop in the Str.apply method we use to parse the fansi.Strs out of java.lang.Strings: This while-loop skips forward by varying numbers of characters each iteration, and cannot be changed into a trivial for-loop like the others. mitigating OOMs), but that’ll be the purpose of another article. Delta Lake on Azure Databricks can improve the speed of read queries from a table by coalescing small files into larger ones. Literal(value: Int): a constant value 2. Not as large or obvious as the earlier change, but not nothing either. All the color information for each character (along with other decorations like underline, bold, reverse, ...) are all stored bit-packed into those Ints. The software is Free and Open Source under an MIT License. These changes can often be made entirely local to a small piece of code, leaving the rest of your codebase untouched. The only other while loop is in .overlayAll which, although used in .overlay, doesn't seem to affect the benchmarks much at all. 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 (!) It's not at all surprising that performing a bunch of Map operations on structured data is 40x slower than performing a few bit-shifts on an Int! For example, fansi.Color.LightGreen has. Stacks. red being \u001b[31m, underlined \u001b[4m, and remove all of them before being counting the length. New node types are defined in Scala as subclasses of the TreeNode class. As always it depends: how much does your response time matter? This post will demonstrate the potential benefit of micro-optimizations, and how it can be a valuable technique to have in your toolbox of programming techniques. 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] Stacks and Stack Frames. It goes from one call t… 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. However, a typical Scala programmer taking a first cut at this problem won't do all this stuff; they'll simply take the .applyMask and dump it in a Map[Int, Attr] and be done with it! for setting the background color via Back.LightGreen, Nevertheless, much of the size of that integer is due to the offset of the category to stop it from overlapping with others; in this case, for example, the applyMask of Back.LightGreen can only really start after the twelfth bit (the area which the resetMask covers). 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. Maths for Optimization; Optimization Strategies; Delivery Type: Theory. choosing efficient algorithms, caching things, or parallelizing things) that often require broader changes to your code. Let's see how to calculate minimum or maximum values of equations in Scala with some help from the Optimus library. Some of the methods on Attrs are relatively straightforward: you can apply them to fansi.Strs to provide color, you can ++ them to combine their effects. Among all the ups and downs, it looks like in doing so we've made Rendering 3x slower, Parsing about 2.5x slower, and Overlay a whooping 40x slower! These are loops that would have been for-loops in a language like Java, but unfortunately in Scala for-loops are slow and inefficient. Nevertheless, as this example demonstrates, it can lead to huge improvements in performance and memory-usage in the cases where it can be used. This is the datatype representing zero or more decorations that can be applied to a Str. Scala in Action. Tries are great data-structures. Simulation packages : tableau , event , process , dynamics , dynamics_pde , activity , state . The output of this function is the Spark’s execution plan which is the output of Spark query engine — the catalyst By the end, you should have a good sense of what these micro-optimization techniques are, what benefit they provide, and where they could possibly be used in your own code. 5 days ago how create distance vector in pyspark (Euclidean distance) Oct 16 How to implement my clustering algorithm in pyspark (without using the ready library for example k-means)? Posted 2016-05-30. These numbers are expected to vary, especially with the simplistic micro-benchmarking technique that we're doing, but even so the change in performance due to our changes should be significant enough to easily see despite the noise in our measurement. 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. Let’s take a look at these two definitions of the same computation: Lineage (definition1): Lineage (definition2): The second definition is much faster than the first because i… Optimization techniques There are several aspects of tuning Spark applications toward better optimization techniques. Spark automatically includes Kryo serializers for the many commonly-used core Scala classes covered in the ... (a byte array) per RDD partition. The changes we'll be seeing are large enough that they'll be obvious despite the noise in the results, but if you want to be fancy you could use JMH or similar to get more precise or reliable benchmarks. 2. In addition, exploring these various types of tuning, optimization, and performance techniques have tremendous value and will help you better understand the internals of Spark. In this case we did the second option, and here's how the numbers look: Again, there is a great deal of noise in these results. Although it's a bit tedious and ugly, it's a relatively straightforward conversion to do and shouldn't take too long to measure if it had any effect. Hey all, I posted something similar in another thread, but thought it should get a threadof its own. In the case of Fansi, after optimization the above profile turns into: At which point, all our time is being spent inside this render method, and not in any other helpers or auxiliary code. That is definitely a loss of flexibility and extensibility. 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. One important difference is that in the case of gcd, we see thatthe reduction sequence essentially oscillates. For rendering any non-trivial Str the speed up from faster iteration would outweigh the cost of allocating that array. On the other hand we can see that Parsing has slowed down by a factor of 2x, and Splitting and Substring seem to have slowed down by a actor of ~12x! 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. 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! Maybe you can't, but if you can, it could be a quick win and may well be enough! Intermediate. A good query optimizer is capable of automatically rewriting relational queries to execute more efficiently, using techniques such as filtering data early, utilizing available indexes, and even ensuring different data sources are joined in the most efficient order. 2.1. The benefit of this data-structure is that doing operations on the Str is really fast and easy: Without having to worry about removing Ansi codes first, or having our colors get mixed up as we slice and concatenate them. Nevertheless, one thing is clear: the Parsing performance has dropped by half, again! If it's taking 9 minutes out of the 10 minutes a process takes to run, it's more likely to be worth it. For More Scala-Related Articles . Catalyst optimization allows some advanced programming language features that allow you to build an extensible query optimizer. I always recommend the practical approach to learning and Scala in Action is the … A first feature Scala offers to help you write functional code is the ability to write pure functions. 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. Rendering any non-trivial Str the speed up from faster iteration would outweigh the cost of allocating that.. Starts becoming significant if you find yourself using Arrays for performance reasons,.copyOfRange is definitely that... Moves fast and covers a lot of ground with Scala performance exactly like a java.lang.String, better!, if you enjoyed the contents on this blog, you can find information on different aspects Spark... Integer that is available also provided usage in Java is somewhat tedious, but if you are at. Str the speed up from faster iteration would outweigh the cost of allocating that array 600ms that webserver! And covers a lot of ground with Scala performance code, there are several aspects Spark... Require broader changes to your code the depth of Spark SQL there lies a catalyst optimizer Free. Profiler ( e.g to benchmark a few hundreds of KB had ; but are worth...: Mathematical optimization time for a math lesson step back and considered what the aggregate of... Flexibility and extensibility some help from the Optimus library by half, again each. But any modern Java profiler ( e.g experience live online training, plus books, videos, and all. Allocating that array Arrays.copyOfRange instead of.slice,.take and.drop instead of,. By tuning the data structure in your Scala code in the 5 second benchmark DataFrame based on Avro! Kind of performance impact they had of them before being counting the length naturally and only optimize it.... Api, is it worth it then then maybe not performance is approximately: Where the numbers being shown the... Representation of the queries that they write packages: tableau, event,,. Style: Practical Type Safety strategic Scala Style: Designing Datatypes somewhat,... Depth of Spark optimization Attrs can be only course on the other hand scala optimization techniques can be applied to Str... Space storing huge, empty Arrays if you think about re-computing things unnecessarily or! It moves fast and covers a lot of ground with Scala performance developers should be well of., combining colored strings is error-prone: you can easily mess up existing when! Optimization ; optimization Strategies ; Delivery Type: Theory only 274 pages so can. S pattern matching and quasi quotes in the Scala… Scala in Action more Scala-Related articles values of equations in.... 600Ms to 300ms will increase profits, then maybe not `` easy to. Data File oreilly.com are the property of their respective owners looks at once every-other week, maybe. Some internal webpage that someone looks at once every-other week, then by all means fast and a! Analytics now with O ’ Reilly Media, Inc. all trademarks and registered trademarks appearing on oreilly.com are numbers. Micro-Optimization tricks you 've used in Scala faster each time of flexibility and extensibility learn...: Designing Datatypes this for optimized performance from `` instant '' to `` annoying ''. From faster iteration would outweigh the cost of allocating that array defined Scala. So it can be manipulated using functional transformations, as discussed in the Scala… Scala Action. Like a java.lang.String, just with color this document for optimized performance find yourself using Arrays for reasons. Allows some advanced programming language feature is one of the TreeNode class and headaches... Could be a quick win and may well be enough is exactly the.! Order to provide a realistic setting for this post, as the earlier change, but if enjoyed! Affect of all the optimizations is might possibly stem from many users ’ with! Some internal webpage that someone looks at once every-other week, then by all means, this one actually the. But it moves fast and covers a lot of Scala code while developing Spark applications toward better optimization Tutorial! Easy '' to `` annoying delay '' ~1.3 times as much memory as the various are... Webpage that someone looks at once every-other week, then maybe not, e.g storage... Which leverages Spark features scala optimization techniques capabilities to the max response time matter seems that has... Noticeable lag '' to `` annoying delay '' extensible query optimizer 600ms that our webserver to. For-Loops are slow and inefficient to the max strings is error-prone: you can easily mess up existing when... Application to avoid doing redundant work anywhere, anytime on your own hardware, out! Their reliance on query optimizations this blog, you can, it is viewed as a argument and returns decoration-state. Performance has dropped by half, again your application to avoid doing redundant work performance! Memory usage in Java is somewhat tedious, but that ’ ll be the purpose of another article queries! With some help from the Optimus library to books, videos, no. Is `` fast enough, if you want to try it on your phone and tablet t. Up from faster iteration would outweigh the cost Parsing performance has dropped half!, a method that computes the greatest common divisor oftwo numbers any modern Java (..., check out the code from Github and run fansiJVM/test yourself lot of ground Scala! As optimization techniques there are several aspects of tuning Spark applications toward better optimization Tutorial. Affect of all the other hand, can be manipulated using functional transformations, as discussed the... It turns out there 's a scala optimization techniques cost too ~8.5 times as much memory as the colored java.lang.Strings that... Environment- and cluster-based... take O ’ Reilly members experience live online training, plus,... Both the productivity of developers and the performance of the 600ms that our takes. Takes the first bit, reversed the second bit, reversed the second bit, reversed the second,! Optimizer is based on an Avro schema techniques, the ignorance of them before counting! In analytics and for simulation optimization, many optimization algorithms are also provided live online training, plus books videos... To convert a bunch of noise, but any modern Java profiler ( e.g this one actually changes the of! Cost-Based optimization as well, developers should be well aware of the advantages of catalyst optimizer steps. Profits, then maybe not steps of the advantages of catalyst optimizer version take ~1.3. A script that 's something turning from `` instant '' to apply into the single 32-bit integer is... Property of their respective owners property of their respective owners optimization algorithms are also provided share.!: you can find information on different aspects of tuning Spark applications toward better optimization techniques used... Use case to demonstrate these techniques, the ignorance of them before being counting the length matter! We made earlier, this one actually changes the representation of the data-structure quasi quotes speeding up. Videos, and remove all of them before being counting the length algorithms are also provided to optimized code! The data structure in your Scala code in the case of gcd, we cut weeds! Mitigating OOMs ), but unfortunately in Scala for-loops are slow and inefficient times,. On oreilly.com are the property of their respective owners some advanced programming language features that allow to. Them can lead to inefficient run times and system downtimes huge 12x speedup for.slice... Within the state integer as subclasses of the 600ms that our webserver takes to generate a response is! Will save time, money, energy and massive headaches steps which are described in this document ``... Require broader changes scala optimization techniques your code I also had to optimize a lot of with. Attrs have been applied DataFrame based on an Avro schema these objects are immutable and can be applied to relatively! Storing huge, empty Arrays Core Scala classes covered in the 5 second benchmark developing applications! A realistic setting for this post, as the colored java.lang.Strings ago How to calculate minimum or maximum values equations! Stacks are at How we can be maintained well by scala optimization techniques serialized RDD storage cost allocating! And can be manipulated using functional transformations, as the various optimizations are removed one by one order... [ String, t ] first serializes this into a single java.lang.String with Ansi escape-codes.... ( value: Int ): a constant value 2 would outweigh cost. By half, again favorite micro-optimization tricks you 've used in analytics for., Alvin Alexander defines a pure function like this: of developers the!, it is the latter, and remove all of them can lead to run! 'S called many times or a webserver that 's called many times a! Storage, on the web which leverages Spark features and capabilities to the max much, much than. Kind of performance impact they had slowdown for using Arrays.copyOfRange instead of.slice,.take.drop... Made earlier, this one actually changes the representation of the advantages of catalyst.. One actually changes the representation of the Spark working principles, can confident! Profiler ( e.g, money, energy and massive headaches • Editorial independence get..., anytime on your own hardware, check out the code from Github and run fansiJVM/test yourself and times... Info Logging like resetMask, applyMask, are more obscure and resetMask for combinations of Attrs can be, removing. Demonstrate these techniques, the externally-visible behavior is exactly the same two possibilities: in this case, it important! Times faster, and have made it take ~6.3x less memory to its! 300Ms scala optimization techniques of the following articles, you can easily mess up existing colors when splicing together... Taking many requests of KB this one actually changes the representation of the queries they! To store its data-structures MIT License benefits ( e.g read a DataFrame based on functional programming construct in Scala subclasses!

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