scala

Implement SCD Type 2 in Spark

由 自闭症网瘾萝莉.ら 提交于 2021-02-18 08:47:47
问题 Trying to implement SCD Type 2 logic in Spark 2.4.4. I've two Data Frames; one containing 'Existing Data' and the other containing 'New Incoming Data'. Input and expected output are given below. What needs to happen is: All incoming rows should get appended to the existing data. Only following 3 rows which were previously 'active' should become inactive with appropriate 'endDate' populated as follows: pk=1, amount = 20 => Row should become 'inactive' & 'endDate' is the 'startDate' of

你真的懂CAP吗？

由 人盡茶涼 提交于 2021-02-18 07:37:10
想写这个是源于微信群里面的一个讨论。在讨论分布式系统的时候，有群友明确地如下说： CAP是可以兼顾的啊！ 这把我惊起了一身冷汗，赶紧去查了一下是不是分布式系统理论界又有新的论文来推翻了之前的CAP定理了。后来深入讨论以后，才发现是他对CAP的理解有误。 CAP理论是分布式领域的基础，所以大家的讨论和研究很多。学界和工业界也想出来好多办法来折中处理不可兼得时候的情形，例如著名的“BASE"。但是诸如上面的“CAP可以兼顾”的话是绝对不应该出现的。如果能证明这点并且能写出学术文章的话，那是肯定能发 PODC 并且成为学术大牛的。而现阶段的研究没有一个往着打破CAP定理的方向走，这说明CAP定理挺牢固的，只是因为“BASE”的存在而产生好像兼顾了的误解。那么，为了帮助大家更好的理解CAP及其应用呢，借此机会，我来试着写篇文章讨论一下这方面的内容，并且争取能通过实践将其表达的更加清楚。 CAP定理到底是什么 以下定义摘自维基百科： 在理论计算机科学中，CAP定理（CAP theorem），又被称作布鲁尔定理（Brewer's theorem），它指出对于一个分布式计算系统来说，不可能同时满足以下三点： 一致性（Consistency） （等同于所有节点访问同一份最新的数据副本） 可用性（Availability）（每次请求都能获取到非错的响应——但是不保证获取的数据为最新数据） 分区容错性

Intellij IDEA 安装Scala插件

由 时间秒杀一切 提交于 2021-02-18 06:19:39
参考博文 https://blog.csdn.net/m635761952/article/details/83348076 1.打开IntelliJ IDEA的Settings界面 2.查看scala插件版本： 1.选择 Plugins标签 2.输入scala 3.选择Scala 4.点击Install JetBrains plugin 点击后，可以查到需要一个v2018.2.11版本的Scala插件 3.到http://plugins.jetbrains.com/plugin/1347-scala下载一个对应版本的插件。 4.本地安装下载好的插件，然后重启，创建项目便有了Scala 来源： oschina 链接： https://my.oschina.net/u/4383341/blog/3462174

Chisel3-Intellij IDEA安装Scala插件

由 依然范特西╮ 提交于 2021-02-18 05:32:53
https://mp.weixin.qq.com/s/xTk5ucvSNuwsh8C6E362cg 后续开启RISC-V开发相关内容。 RISC-V开发推荐使用Chisel编程语言。Chisel即Constructing Hardware in a Scala Embedded Language： Chisel is an open-source hardware construction language developed at UC Berkeley that supports advanced hardware design using highly parameterized generators and layered domain-specific hardware languages. Hardware construction language (not C to Gates) Embedded in the Scala programming language Algebraic construction and wiring Abstract data types and interfaces Bulk connections Hierarchical + object oriented + functional construction Highly

How to test client-side Akka HTTP

由 别等时光非礼了梦想. 提交于 2021-02-17 21:33:35
问题 I've just started testing out the Akka HTTP Request-Level Client-Side API (Future-Based). One thing I've been struggling to figure out is how to write a unit test for this. Is there a way to mock the response and have the future completed without having to actually do an HTTP request? I was looking at the API and the testkit package, trying to see how I could use that, only to find in the docs that it actually says: akka-http-testkit A test harness and set of utilities for verifying server

How to test client-side Akka HTTP

由 霸气de小男生 提交于 2021-02-17 21:32:13
问题 I've just started testing out the Akka HTTP Request-Level Client-Side API (Future-Based). One thing I've been struggling to figure out is how to write a unit test for this. Is there a way to mock the response and have the future completed without having to actually do an HTTP request? I was looking at the API and the testkit package, trying to see how I could use that, only to find in the docs that it actually says: akka-http-testkit A test harness and set of utilities for verifying server

Scala: Mutable vs. Immutable Object Performance - OutOfMemoryError

由 故事扮演 提交于 2021-02-17 21:23:07
问题 I wanted to compare the performance characteristics of immutable.Map and mutable.Map in Scala for a similar operation (namely, merging many maps into a single one. See this question). I have what appear to be similar implementations for both mutable and immutable maps (see below). As a test, I generated a List containing 1,000,000 single-item Map[Int, Int] and passed this list into the functions I was testing. With sufficient memory, the results were unsurprising: ~1200ms for mutable.Map,

Why is reference assignment atomic in Java?

由 拟墨画扇 提交于 2021-02-17 19:13:38
问题 As far as I know reference assignment is atomic in a 64 bit JVM. Now, I assume the jvm doesn't use atomic pointers internally to model this, since otherwise there would be no need for Atomic References. So my questions are: Is atomic reference assignment in the "specs" of java/Scala and guaranteed to happen or is it just a happy coincidence that it is that way most times ? Is atomic reference assignment implied for any language that compiles to the JVM's bytecode (e.g. clojure, Groovy, JRuby,

Why is reference assignment atomic in Java?

由 Deadly 提交于 2021-02-17 19:11:30
问题 As far as I know reference assignment is atomic in a 64 bit JVM. Now, I assume the jvm doesn't use atomic pointers internally to model this, since otherwise there would be no need for Atomic References. So my questions are: Is atomic reference assignment in the "specs" of java/Scala and guaranteed to happen or is it just a happy coincidence that it is that way most times ? Is atomic reference assignment implied for any language that compiles to the JVM's bytecode (e.g. clojure, Groovy, JRuby,

Should we parallelize a DataFrame like we parallelize a Seq before training

由 不羁的心 提交于 2021-02-17 15:36:40
问题 Consider the code given here, https://spark.apache.org/docs/1.2.0/ml-guide.html import org.apache.spark.ml.classification.LogisticRegression val training = sparkContext.parallelize(Seq( LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)))) val lr = new LogisticRegression() lr.setMaxIter(10).setRegParam(0.01) val model1 = lr.fit(training) Assuming we
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