scala optimization techniques

That's a huge slowdown for using .slice and .take and .drop instead of Arrays.copyOfRange. 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. gcd(14, 21)is evaluated as follows: Now, consider factorial: factorial(4)is evaluated as follows: What are the differences between the two sequences? Furthermore, catalyst optimizer in Spark offers both rule-based and cost-based optimization as well. DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descentT. 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. The applyMask and resetMask for combinations of Attrs can be computed from those of each individual Attrs object. Let everyone know in the comments below! That's something turning from "noticeable lag" to "annoying delay". Share on … 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 .render method serializes this into a single java.lang.String with Ansi escape-codes embedded. 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. Posted 2016-05-30. It turns out that this is far less code: But you end up paying a performance cost for it: Among the noise, it seems Overlay has gotten about 10% slower as a result of this change. If it's taking 300ms out of the 600ms that our webserver takes to generate a response, is it worth it then? The storage, on the other hand, can be maintained well by utilizing serialized RDD storage. Get Scala and Spark for Big Data Analytics now with O’Reilly online learning. Optimization techniques There are several aspects of tuning Spark applications toward better optimization techniques. 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. This is the datatype representing zero or more decorations that can be applied to a Str. If you want to try it on your own hardware, check out the code from Github and run fansiJVM/test yourself. The software is Free and Open Source under an MIT License. To pass we have to aggressively throw out-of-bounds exceptions ourselves: It turns out that this works, and all the test cases pass, but at a cost of some performance: There's some noise in this measure, as you'd expect: Rendering and Concat has seemed to have gotten faster. Literal(value: Int): a constant value 2. JProfiler) should do it just fine. 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. 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. 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. Data Serialization in Spark. Data Serialization. It is important to realize that the RDD API doesn’t apply any such optimizations. Simulation packages : tableau , event , process , dynamics , dynamics_pde , activity , state . transform takes the decoration-state as a argument and returns the decoration-state after these Attrs have been applied. And Array lookup is much, much faster than if we had used a Map. Typically, we would reach for a Map[String, T] first. Often, it is viewed as a maintainability cost with few benefits. This includes. Equivalently, it's a huge 12x speedup for using Arrays.copyOfRange instead of .slice, .take and .drop! Is that acceptable? On the other hand, if you are writing a business application and the business rules are changing constantly, then this loss of flexibility is more painful and may not be worth it. 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. Each node has a node type and zero or more children. PROJECT 2. Optimization Techniques; Learning Objectives: Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp. The baseline level of performance is approximately: Where the numbers being shown are the numbers of iterations completed in the 5 second benchmark. In order to provide a realistic setting for this post, I'm going to use the Fansi library as an example. In short, the different "kinds" of decoration each take up a separate bit-range within the State integer. It goes from one call t… Intermediate. Terms of service • Privacy policy • Editorial independence, Get unlimited access to books, videos, and. Thus we have a choice of either. To understand functional loops in Scala, it’s important to understand what stacks are. 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! Measuring memory usage in Java is somewhat tedious, but any modern Java profiler (e.g. Do you think about re-computing things unnecessarily, or computing things and then throwing them away? Optimization Techniques in Spark (i)Data Serialization - Java Serialization, Kyro serialization (ii)Memory Tuning - Data Structure tuning, Garbage collection tuning (iii)Memory Management - Cache() and Persist() Stacks. For an example of the benefits of optimization, see the following notebooks: Delta Lake on Databricks optimizations Python notebook Open notebook in new tab Copy link for import Delta Lake on Azure Databricks can improve the speed of read queries from a table by coalescing small files into larger ones. Thus, this post will take the opposite tack: We will start off with a tour of the already-optimized Fansi library, Discuss the internals and highlight the micro-optimizations that are meant to make Fansi fast. 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. L-BFGS is an optimization algorithm in the family of quasi-Newton methods to solve the optimization problems of the form minw ∈ Rdf(w). Debug Apache Spark jobs running on Azure HDInsight 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. The book is only 274 pages so it can feel pretty small. 2. New node types are defined in Scala as subclasses of the TreeNode class. Here's an implementation of gcdusing Euclid's algorithm. A new extensible optimizer called Catalyst emerged to implement Spark SQL. Catalyst Optimizer supports both rule-based and cost-based optimization. And ~8.5 times as much memory as the colored java.lang.Strings. "Micro-optimization" is normally used to describe low-level optimizations that do not change the overall structure of the program; this is as opposed to "high level" optimizations (e.g. . Bit-packing is a technique that is often ignored in "high level" languages like Scala, despite having a rich history of usage in C++, C, or Assembly programs. Exercise your consumer rights by contacting us at donotsell@oreilly.com. After all, it isn't uncommmon for people to treat Array[T]s as normal Scala collections using the extension methods in RichArray! Attribute(name: String):an attribute from a… These changes can often be made entirely local to a small piece of code, leaving the rest of your codebase untouched. 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. 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. Home Assignment Yes First, consider gcd, a method that computes the greatest common divisor oftwo numbers. Welcome to the fourteenth lesson ‘Spark RDD Optimization Techniques’ of Big Data Hadoop Tutorial which is a part of ‘Big Data Hadoop and Spark Developer Certification course’ offered by Simplilearn. Others, like resetMask, applyMask, are more obscure. 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. - but it's not fundamentally difficult. Hands-on workshop No hands-on. 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. Then roll back the optimizations one by one in order to see what kind of performance impact they had. 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… For example, if all our time is spent inside foreach, we may replace that .foreach with a while-loop that runs much faster in Scala. OPTIMIZE makes no data related … Creativity is one of the best things about open source software and cloud computing for continuous learning, solving real-world problems, and delivering solutions.