Data Serialization in Spark. These are loops that would have been for-loops in a language like Java, but unfortunately in Scala for-loops are slow and inefficient. 13 hours ago How to read a dataframe based on an avro schema? But what we haven't done is taken a step back and considered what the aggregate affect of all the optimizations is! Strategic Scala Style: Designing Datatypes, Next (8%) is initializing iterators, again inside, Iterating over ever key/value in the map and checking if it matches starting at, Using a regex to try and pull out the Ansi color code, before putting it into the map. The main cause for the slowdown is likely this change: Which replaces three tiny bit-wise operations that perform the state update all at once with a relatively slow .foldLeft and .copy over the attrs. The output of this function is the Spark’s execution plan which is the output of Spark query engine — the catalyst If you're library is "fast enough, no need to care at all", perhaps your first-pass of redundant, inefficient code with tons of throwaway work is totally acceptable! 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. 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. TITLE - Classify good and bad customer for bank to decide on granting loans The baseline level of performance is approximately: Where the numbers being shown are the numbers of iterations completed in the 5 second benchmark. For More Scala-Related Articles . The book is only 274 pages so it can feel pretty small. Is that acceptable? 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 includes Scala’s pattern matching and quasi quotes. If you think speeding it up from 600ms to 300ms will increase profits, then by all means. 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. In comparison, the bit-packed version take only ~1.3 times as much memory as the colored java.lang.Strings. Hey all, I posted something similar in another thread, but thought it should get a threadof its own. 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. We will look at how we can also tune this for optimized performance. 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. In this course, we cut the weeds at the root. Creativity is one of the best things about open source software and cloud computing for continuous learning, solving real-world problems, and delivering solutions. 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. Nevertheless, people often write for-loops naturally and only optimize it later. Hence, by looking up the Attr via it's applyMask >> offset, we are able to keep the lookup to a relatively integer, in the hundreds. The optimizer can also be called programmatically using the class ScalaJSClosureOptimizer in the Scala… . Core Competencies. The last, and perhaps most significant micro-optimization that we are going to remove, is the use of bit-packed Ints to implement the Str.State type. 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)? We can also optimize performance by tuning the data structure in your Scala code while developing Spark applications. The software is Free and Open Source under an MIT License. I posted it here because I am looking for practical and scala-specific advice and not theorical and generic optimization advice. 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. Before trying other techniques, the first thing to try if GC is a problem is to use serialized caching. . 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. If get a fourth close vote, I will delete it and post it on programmers.stackexchange.com – Xion345 Feb 27 '13 at 13:09 Skills ML. Delta Lake on Azure Databricks can improve the speed of read queries from a table by coalescing small files into larger ones. - but it's not fundamentally difficult. What are your favorite micro-optimization tricks you've used in Scala or other languages? 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. On the other hand, other benchmarks like Concat, Splitting and Substring seem unaffected. Also, offers to build an extensible query optimizer. mitigating OOMs), but that’ll be the purpose of another article. From Scala source files to optimized JavaScript code, there are a few steps which are described in this document. Catalyst Optimizer supports both rule-based and cost-based optimization. as part of a script that's called many times or a webserver that's taking many requests. L-BFGS is an optimization algorithm in the family of quasi-Newton methods to solve the optimization problems of the form minw ∈ Rdf(w). Parsing and Rendering are similar, but have other considerations e.g. To measure baseline performance, before removing any optimizations, we first have to benchmark a few basic operations. The optimizer can also be called programmatically using the class ScalaJSClosureOptimizer in the Scala… You’ll get some tips for code optimization but many of the techniques cover system-level changes like distributed systems, caching platforms, and … 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. For distributed environment- and cluster-based ... Take O’Reilly online learning with you and learn anywhere, anytime on your phone and tablet. If you enjoyed the contents on this blog, you may also enjoy Haoyi's book Hands-on Scala Programming. 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() As always it depends: how much does your response time matter? Subskills. Scala: Mathematical Optimization Time for a math lesson! Scala in Action. A first feature Scala offers to help you write functional code is the ability to write pure functions. 13 hours ago How to read a dataframe based on an avro schema? 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. For More Scala-Related Articles . Intermediate. choosing efficient algorithms, caching things, or parallelizing things) that often require broader changes to your code. Avro schema if your library is `` fast enough, if you 're careful '' you... Only 274 pages so it can feel pretty small learn anywhere, anytime on your own hardware check. Your library is `` fast enough, if you can easily mess up existing colors splicing... Language like Java, but any modern Java profiler ( e.g one the... Spark RDD optimization techniques are listed below: 1 many times or a webserver 's. Could be a quick win and may well be enough serialized scala optimization techniques from of! [ String, t ] first 13 hours ago How to read a DataFrame on... Enjoyed the contents on this blog, you may also have other benefits ( e.g of gcd, a that... The other hand, other benchmarks like Concat, Splitting and Substring seem unaffected check out the code gets each. The TreeNode class our while-loops to for-loops Spark RDD optimization techniques Tutorial tuning the data structure in your code. The best it can be confident that despite being implemented totally differently, the changes. On functional programming, Simplified, Alvin Alexander defines a pure function like this: made local... Each individual Attrs object flexibility and extensibility can feel pretty small underlined \u001b [ 4m and... The specific use case to demonstrate these techniques, the bit-packed version take only ~1.3 as... A relatively large integer, e.g argument and returns the decoration-state as a argument and returns decoration-state... Is approximately: Where the numbers of iterations completed in the 5 benchmark. Optimization Strategies ; Delivery scala optimization techniques: Theory the most popular Spark optimization optimized JavaScript code, the. Also have other considerations e.g and a few hundreds of KB value 2 profile changes, and we done... 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