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How do I check for equality using Spark Dataframe without SQL Query?

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问题 I want to select a column that equals to a certain value. I am doing this in scala and having a little trouble. Heres my code df.select(df("state")==="TX").show() this returns the state column with boolean values instead of just TX Ive also tried df.select(df("state")=="TX").show() but this doesn't work either. 回答1: I had the same issue, and the following syntax worked for me: df.filter(df("state")==="TX").show() I'm using Spark 1.6. 回答2: There is another simple sql like option. With Spark 1

How do I check for equality using Spark Dataframe without SQL Query?

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How do I check for equality using Spark Dataframe without SQL Query?

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Is there anyway, in Scala, to get the Singleton type of something from the more general type?

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问题 I have a situation where I'm trying to use implicit resolution on a singleton type. This works perfectly fine if I know that singleton type at compile time: object Main { type SS = String with Singleton trait Entry[S <: SS] { type out val value: out } implicit val e1 = new Entry["S"] { type out = Int val value = 3 } implicit val e2 = new Entry["T"] { type out = String val value = "ABC" } def resolve[X <: SS](s: X)(implicit x: Entry[X]): x.value.type = { x.value } def main(args: Array[String])

What does : => A syntax mean in method parameter declaration? [duplicate]

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问题 This question already has answers here : By-name parameter vs anonymous function (5 answers) Closed 11 months ago . So, reading the Scala tour about implicit classes in Scala, I came across this piece of code: object Helpers { implicit class IntWithTimes(x: Int) { def times[A](f: => A): Unit = { def loop(current: Int): Unit = if(current > 0) { f loop(current - 1) } loop(x) } } } What is troubling me here is the def times[A](f: => A): Unit = { line. What is going on here? The part of the

flatMap with Shapeless yield FlatMapper not found

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问题 I'm trying to define some structure like this case class Transformer[From, To]( name: String, get: PaymentEvent => From, to: From => To I want to filter elements with names that are part of a Set class filterName(names: Set[String]) extends lowPriority { implicit def get[From, To] = at[Transformer[From, To]]{ trans => if (names.contains(trans.name)) trans :: HNil else HNil } } This is the concrete value: type HTransformer = Transformer[String, String] :: Transformer[Long, Long] :: HNil When I

Load Dataset from Dynamically generated Case Class

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问题 What is Needed: number of tables in source database are changing rapidly and thus I don't want to edit case classes so I dynamically generate them through SCALA code and put in package. But now not able to read it dynamically. If this works than I would parse "com.example.datasources.fileSystemSource.schema.{}" as object schema members in loop What has already been Done: I have some case classes dynamically generated from schema of database tables as below: object schema{ case class Users

Spark Shell Add Multiple Drivers/Jars to Classpath using spark-defaults.conf

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问题 We are using Spark-Shell REPL Mode to test various use-cases and connecting to multiple sources/sinks We need to add custom drivers/jars in spark-defaults.conf file, I have tried to add multiple jars separated by comma like spark.driver.extraClassPath = /home/sandeep/mysql-connector-java-5.1.36.jar spark.executor.extraClassPath = /home/sandeep/mysql-connector-java-5.1.36.jar But its not working, Can anyone please provide details for correct syntax 回答1: As an example in addition to Prateek's

case class、class 、object 、case object

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/* class、object、case class、case object区别 class 类似Java中的class； object Scala不能定义静态成员，用定义单例对象代之； case class被称为样例类，是一种特殊的类，常被用于模式匹配。一、class 和 object 关系： 1.单例对象不能带参数，类可以 2.对象可以和类名一样时，object被称为伴生对象，class被称为伴生类； 3.类和伴生对象可以相互访问其私有属性，但是它们必须在一个源文件当中； 4.类只会被编译，不会被执行。要执行，必须在Object中。二、case class 与 class 区别： 1.初始化的时候可以不用new，也可以加上，但是普通类必须加new; 2.默认实现了equals、hashCode方法； 3.默认是可以序列化的，实现了Serializable； 4.自动从scala.Product中继承一些函数; 5.case class 构造函数参数是public的，我们可以直接访问； 6.case class默认情况下不能修改属性值； 7.case class最重要的功能，支持模式匹配，这也是定义case class的重要原因。三、case class 和 case object 区别： 1.类中有参和无参，当类有参数的时候，用case class

Spark（十四）SparkStreaming的官方文档

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一、SparkCore、SparkSQL和SparkStreaming的类似之处二、SparkStreaming的运行流程 2.1　图解说明 2.2　文字解说 1、我们在集群中的其中一台机器上提交我们的Application Jar，然后就会产生一个Application，开启一个Driver，然后初始化SparkStreaming的程序入口StreamingContext； 2、Master会为这个Application的运行分配资源，在集群中的一台或者多台Worker上面开启Excuter，executer会向Driver注册； 3、Driver服务器会发送多个receiver给开启的excuter，（receiver是一个接收器，是用来接收消息的，在excuter里面运行的时候，其实就相当于一个task任务） 4、receiver接收到数据后，每隔200ms就生成一个block块，就是一个rdd的分区，然后这些block块就存储在executer里面，block块的存储级别是Memory_And_Disk_2； 5、receiver产生了这些block块后会把这些block块的信息发送给StreamingContext； 6、StreamingContext接收到这些数据后，会根据一定的规则将这些产生的block块定义成一个rdd；三