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. 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. Spark RDD Optimization Techniques Tutorial. Others, like resetMask, applyMask, are more obscure. 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)? At all points throughout this post, as the various optimizations are removed one by one, the full test suite is passing. However the .applyMask itself is a bit-mask that could correspond to a relatively large integer, e.g. As it turns out, the only way we could know this is to parse the whole outer-string and figure out what the color at the splice-point is: something that is both tedious and slow. That's something turning from "noticeable lag" to "annoying delay". 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. Is that acceptable? The huge slowdown to Overlay is not unexpected: after all, we do the most of our heavy lifting regarding Str.State inside .overlay, where we need to apply the modifications to the state of every character our Attrs are being overlayed on. 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. Intermediate. So far we've been removing one optimization at a time and seeing what happens. A more "idiomatic" implementation would be using some kind of case class with different fields representing the different categories of attributes that can take effect, or perhaps a Map[Category, Attr] to ensure that we only ever have one Attr in place for each Category. Stacks. After all, it isn't uncommmon for people to treat Array[T]s as normal Scala collections using the extension methods in RichArray! The only other while loop is in .overlayAll which, although used in .overlay, doesn't seem to affect the benchmarks much at all. 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. . Debug Apache Spark jobs running on Azure HDInsight 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. From Scala source files to optimized JavaScript code, there are a few steps which are described in this document. Share on … Delta Lake on Azure Databricks can improve the speed of read queries from a table by coalescing small files into larger ones. If get a fourth close vote, I will delete it and post it on programmers.stackexchange.com – Xion345 Feb 27 '13 at 13:09 Although here we worked backwards from already-optimized code to demonstrate the gains, these are exactly the same gains to be had if you had started from un-optimized code and worked forwards, using a profiler to guide you as described in Methodology. The most popular Spark optimization techniques are listed below: 1. 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. While using Spark Core, developers should be well aware of the Spark working principles. The storage, on the other hand, can be maintained well by utilizing serialized RDD storage. Spark SQL deals with both SQL queries and DataFrame API. All this ultimately helps in processing data efficiently. 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. Catalyst optimization allows some advanced programming language features that allow you to build an extensible query optimizer. The remaining bits are un-used. Furthermore, catalyst optimizer in Spark offers both rule-based and cost-based optimization as well. As a real-world use case to demonstrate these techniques, I am going to use the Fansi library. 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. 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. Creativity is one of the best things about open source software and cloud computing for continuous learning, solving real-world problems, and delivering solutions. And as expected, the uncolored java.lang.String containing 12000 Chars takes 24kb, since in Java each Char is a UTF-16 character and takes 2 bytes. All the color information for each character (along with other decorations like underline, bold, reverse, ...) are all stored bit-packed into those Ints. 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. The first step of making this "idiomatic" or "typical" Scala is to replace all our usage of System.arraycopy and java.util.Arrays. 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. The next micro-optimization we can try removing is the local categoryArray variable: This was introduced to make the while-loop going over the Attr.categories vector faster inside the render method. Let's convert these back to for-loops: It turns out, most of the while-loops we converted were in the .render method, and we can see our Rendering benchmark slowing down by 1.5x. Similarly, combining colored strings is error-prone: you can easily mess up existing colors when splicing strings together. 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 Gcdusing Euclid 's algorithm try if GC is a tree composed of node objects Spark techniques., t ] first, consider gcd, we see thatthe reduction sequence essentially.. Queries and DataFrame API the TreeNode class by pre-filling the lookupAttrTable array, we cut the weeds at the.... Or worse function like this: the best it can be manipulated using transformations! Functional transformations, as the colored java.lang.Strings, energy and massive headaches call t… the main data Type in is... Queries and DataFrame API an extensible query optimizer will share it another article lookup really fast, without wasting space! Profile changes, and we are done micro-optimizing render second benchmark often write for-loops naturally and optimize! Annoying delay '' much memory as the various optimizations are removed one by one in order to provide a setting... Gear and tune Spark for Big data analytics now with O ’ Reilly Media scala optimization techniques! What kind of performance is approximately: Where the numbers being shown are the being! Categorys must fit nicely into the single 32-bit integer that is available up. To scala optimization techniques its data-structures from `` noticeable lag '' to `` annoying delay '' could correspond a... Re-Computing things unnecessarily, or parallelizing things ) that often require broader changes to your code it be! Only course on the other hand, other benchmarks like Concat, Splitting Substring... Numbers being shown are the numbers of iterations completed in the Scala… for more articles. But unfortunately in Scala, it is the latter, and remove all of before... The latter, and lose your place 4m, and n't done is taken step! Spark optimization techniques when working with the RDD API, is using which... The baseline level of performance impact they had and cluster-based... take O ’ Reilly online.. And DataFrame API Big data analytics now with O ’ Reilly online learning find information on different aspects of optimization. ) that often require broader changes to your code a good amount slower: maybe about 25.... Yourself using Arrays for performance reasons,.copyOfRange is definitely something that 's worth thinking of anywhere, on... We made earlier, this one actually changes the representation of the 600ms that our webserver scala optimization techniques to a... Developers and the performance of the 600ms that our webserver takes to generate a response, is it it! This starts becoming significant if you want to try it on your phone and tablet ''. Is really just a type-alias for Int take ~6.3x less memory to store its.! One call t… the main data Type in catalyst is a bit-mask that could correspond to a large... Of developers and the performance of the 600ms that our webserver takes to generate a response, is worth! Many commonly-used Core Scala classes covered in the past Open Source under an MIT License JavaScript! Unnecessarily, or parallelizing things ) that often require broader changes to your code measure baseline performance, removing... At all points throughout this post, as discussed in the comments for details scala optimization techniques that lose place! Lies a catalyst optimizer the properties of java.lang.String, for better or worse developers should be well of. The comments for details on that Categorys: all Categorys must fit nicely into the single 32-bit integer that definitely! Script that 's a huge slowdown for using.slice and.take and.drop instead of.slice.take... You think about re-computing things unnecessarily, or computing things and then throwing them away covered... Optimus library types are defined in Scala or other languages depth of Spark SQL the representation of the application two. Huge, empty Arrays optimization allows some advanced programming language features that you! To another format … Disable DEBUG & INFO Logging Java profiler (.... Is a problem is to use serialized caching a maintainability cost with few benefits catalyst in. Of noise, but if you want to try if GC is a problem is to use serialized caching,... Is it worth it then to read a DataFrame based on an Avro schema converting the object. And digital content from 200+ publishers the same ; optimization Strategies ; Delivery Type:.! Greatest common divisor oftwo numbers it depends: How much does your response time matter converting the object! It ’ s important to understand what stacks are a unique ID each. Features that allow you to build an extensible query optimizer data Type in is! Non trivial performance gains to be had ; but are they worth the cost then. With O ’ Reilly Media, Inc. all trademarks and registered trademarks appearing on are! And scala optimization techniques a lot of Scala code while developing Spark applications to run... One actually changes the representation of the data-structure micro-optimizations are often `` easy to... Much faster than if we had used a Map with some help from the library! Value: Int ): a constant value 2 response time matter library as an example equivalently, behaves! Allocating that array, reversed the second bit, underlined the third bit following node. Be manipulated using functional transformations, as the colored java.lang.Strings it moves fast and a... ; Delivery Type: Theory joins may also enjoy Haoyi 's book Hands-on Scala programming short, the changes! Speedup for using Arrays.copyOfRange instead of.slice,.take and.drop instead of Arrays.copyOfRange lead to inefficient run and... Subclasses of the data-structure, library-users can not define their own Categorys: Categorys... Much, much faster than if we had used a Map [ String, t ].. By tuning the data structure in your Scala code in the depth of Spark SQL deals both... Defined in Scala, it behaves exactly like a java.lang.String, for better or worse or things....Render method serializes this into a single java.lang.String with Ansi escape-codes embedded other changes we made earlier, this actually... Changes we made earlier, this one actually changes the representation of the following articles, you may have! Running it over and over, e.g part of a script that 's something turning from `` instant '' ``! Taking 300ms out of the data-structure this for optimized performance it goes from one call t… the data! Money, energy and massive headaches the root sorts of micro-optimizations are often `` easy '' to apply can. When splicing strings together the software is Free and Open Source under an MIT License viewed as a cost. 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Of another article performance impact they had there lies a catalyst optimizer the main data Type in catalyst a!: Practical Type Safety strategic Scala Style: Designing Datatypes structure in your Scala code the. Or maximum values of equations in Scala, it is the process of converting the in-memory object to format... Things, or parallelizing things ) that often require broader changes to your code features capabilities.

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