each

I'm learning the details of how each works in ruby, and I tried out the following line of code: p [1,2,3,4,5].each { |element| el } And the result is an array of [1,2,3,4,5] But I don't think I fully understand why. Why is the return value of each the same array? Doesn't each just provide a method for iterating? Or is it just common practice for the each method to return the original value? Array#each returns the [array] object it was invoked upon: the result of the block is discarded . Thus if there are no icky side-effects to the original array then nothing will have changed. Perhaps you

Is there a way to use .each so it does not throw an error if the object is nil or empty (without adding an additional nil/blank test? It seems that if I say phonelist.each do |phone| that if phonelist is empty, then the block should not be executed. But in my view (haml) I have - @myvar.phonelist.each do |phone| and if phonelist is empty, it throws a NoMethodError. I run into this a lot, and always workaround by adding an explicit check/branch for .blank? but it seems there should be an easier way to tell .each that empty means do nothing. Ed S. You're attempting to smack a band-aid on a

Within a rails .each code, how can I eliminate duplicates from the result? PMRelationships is a relationship table that links people and movies MGRelationships is a relationship table that links genres and movies The process Im looking for is to find All a Person's genres through first PmRelationship then MgRelationship <% @pm_relationships = PmRelationship.where(:people_id => @person.id) %> <ul class="basic-info-genres"> <% @pm_relationships.each do |pm_relationship| %> <% @movie=Movie.find(pm_relationship.movie_id) %> <% @mg_relationships = MgRelationship.where(:movie_id => @movie.id) %> <%

前言 spark streaming在2.2.1版本之后出现一个类似的实时计算框架Structured Streaming。 引用一句 spark streaming structured streaming区别 博客的原话，建议扩展读下：Structured Streaming 通过提供一套 high-level 的 declarative api 使得流式计算的编写相比 Spark Streaming 简单容易不少，同时通过提供 end-to-end 的 exactly-once 语义。 核心优势有以下几点：用流式计算代替batch计算，declarative api可以减少代码编写难度，可以保证exactly-once。 一：StreamingContext详解 两种创建方式： 一：sparkConf方式 val conf = new SparkConf().setAppName(appName).setMaster(master); val ssc = new StreamingContext(conf, Seconds(1)); 二：sparkContext方式 val sc = new SparkContext(conf) val ssc = new StreamingContext(sc, Seconds(1)); 一个StreamingContext定义之后

I am currently using JvectorMap and trying to highlight multiple countries when hovering over text, I have gotten it to a point where if i hover over the word Africa, it will highlight the entire map, how would i filter it to highlight only Africa when i am hovering over the content name of Africa. currently i am creating a list of continents using a jQuery.each and i am returning continentCodes , which contains all of the country codes (ZA, US) with a color assigned to them... I have tried doing the following: jQuery('.continentLink').hover(function() { jQuery.each(mapObject.mapData.paths,

CF471B MUH and Important Things 洛谷评测传送门 题目描述 It's time polar bears Menshykov and Uslada from the zoo of St. Petersburg and elephant Horace from the zoo of Kiev got down to business. In total, there are n n tasks for the day and each animal should do each of these tasks. For each task, they have evaluated its difficulty. Also animals decided to do the tasks in order of their difficulty. Unfortunately, some tasks can have the same difficulty, so the order in which one can perform the tasks may vary. Menshykov, Uslada and Horace ask you to deal with this nuisance and come up with individual plans

CS5487 – Assignment 3 – Course Project Department of Computer Science City University of Hong Kong Proposal due date: Fri, Week 10 Presentation date: TBA, Week 14 Report due date: Fri, Week 14 1 Course Project The final assignment is a student-defined course project. The goal of the project is to get some hands-on experience using the course material on your own research problems. If you can’t think of a project, then you can do the “default” project, which is digit classification (see Section 2). 1.1 Project topic The goal of the project is to get some hands-on experience using the course

数据库分片思想 垂直切分 按照业务拆分的方式称为垂直分片，又称为纵向拆分，它的核心理念是专库专用。 水平切分 水平分片又称为横向拆分。 相对于垂直分片，它不再将数据根据业务逻辑分类，而是通过某个字段（或某几个字段），根据某种规则将数据分散至多个库或表中，每个分片仅包含数据的一部分。 例如：根据主键分片，偶数主键的记录放入0库（或表），奇数主键的记录放入1库（或表） Sharding-JDBC简介 定位为轻量级Java框架，在Java的JDBC层提供的额外服务。 它使用客户端直连数据库，以jar包形式提供服务，无需额外部署和依赖，可理解为增强版的JDBC驱动，完全兼容JDBC和各种ORM框架。 适用于任何基于Java的ORM框架，如：JPA, Hibernate, Mybatis, Spring JDBC Template或直接使用JDBC。 基于任何第三方的数据库连接池，如：DBCP, C3P0, BoneCP, Druid, HikariCP等。 支持任意实现JDBC规范的数据库。目前支持MySQL，Oracle，SQLServer和PostgreSQL。 Sharding-JDBC采用无中心化架构，适用于Java开发的高性能的轻量级OLTP应用； 功能列表 分库 & 分表 读写分离 分布式主键 引入依赖 <dependency> <groupId>io.shardingjdbc