scala

问题 I would like to build my project in Scala and then use it in a script in Python for my data hacking (as a module or something like that). I have seen that there are ways to integrate python code into JVM languages with Jython (only Python 2 projects though). What I want to do is the other way around though. I found no information on the net how to do this, but it seems strange that this should not be possible. 回答1: General solution -- use some RPC/IPC (sockets, protobuf, whatever). However,

问题 Given 2 GADT Algebras which know about each other and 2 interpreters that are mutually recursive, I am having issues having to cast from type A to type h <: HList even though in the context of the pattern match, it should be implied that type A is type h. Is there a way to avoid the asInstanceOf[h] call in the interpreter? abstract class KvpHList[H<:HList] object KvpNil extends KvpHList[HNil] case class KvpCons[H <: A :: T,A, T<:HList](head: KvpValue[A], tail: KvpHList[T])(implicit isHCons:

问题 I have the following code snippet, that does not get compiled: trait Environment[F[_]] { def get(v: EnvVariable): F[Option[EnvValue]] } final class LiveBadEnvironment[F[_] : Sync] extends Environment[F] { override def get(v: env.EnvVariable): F[Option[env.EnvValue]] = None.pure[F] } the compiler complains: [error] found : F[None.type] [error] required: F[Option[io.databaker.env.EnvValue]] [error] (which expands to) F[Option[io.databaker.env.EnvValue.Type]] [error] Note: None.type <: Option[io

问题 I'm using Shapeless to accumulate materialized values in Akka as an HList and convert that to a case class. (You don't have to know Akka much for this question, but the default approach accumulates materialized values as recursively nested 2-tuples, which isn't much fun, so Shapeless HLists seemed a more sensible approach -- and works pretty well. But I don't know how to properly re-use that approach. Here, I'll simplify the kinds of values Akka produces.) For example, let's say we've got two

问题 I am running a Spark application that processes multiple sets of data points; some of these sets need to be processed sequentially. When running the application for small sets of data points (ca. 100), everything works fine. But in some cases, the sets will have a size of ca. 10,000 data points, and those cause the worker to crash with the following stack trace: Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 26.0 failed 4 times,

问题 I am using Spark on a Zeppelin notebook, and groupByKey() does not seem to be working. This code: df.groupByKey(row => row.getLong(0)) .mapGroups((key, iterable) => println(key)) Gives me this error (presumably a compilation error, since it shows up in no time while the dataset I am working on is pretty big): error: Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for

问题 I am using Spark on a Zeppelin notebook, and groupByKey() does not seem to be working. This code: df.groupByKey(row => row.getLong(0)) .mapGroups((key, iterable) => println(key)) Gives me this error (presumably a compilation error, since it shows up in no time while the dataset I am working on is pretty big): error: Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for

问题 I have a Spark DataFrame with the following structure: root |-- distribution: vector (nullable = true) +--------------------+ | topicDistribution| +--------------------+ | [0.1, 0.2] | | [0.3, 0.2] | | [0.5, 0.2] | | [0.1, 0.7] | | [0.1, 0.8] | | [0.1, 0.9] | +--------------------+ My question is: How to add a column with the index of the maximum value for each row? It should be something like this: root |-- distribution: vector (nullable = true) |-- max_index: integer (nullable = true) +----

问题 I have a Spark DataFrame with the following structure: root |-- distribution: vector (nullable = true) +--------------------+ | topicDistribution| +--------------------+ | [0.1, 0.2] | | [0.3, 0.2] | | [0.5, 0.2] | | [0.1, 0.7] | | [0.1, 0.8] | | [0.1, 0.9] | +--------------------+ My question is: How to add a column with the index of the maximum value for each row? It should be something like this: root |-- distribution: vector (nullable = true) |-- max_index: integer (nullable = true) +----

问题 I have a Spark DataFrame with the following structure: root |-- distribution: vector (nullable = true) +--------------------+ | topicDistribution| +--------------------+ | [0.1, 0.2] | | [0.3, 0.2] | | [0.5, 0.2] | | [0.1, 0.7] | | [0.1, 0.8] | | [0.1, 0.9] | +--------------------+ My question is: How to add a column with the index of the maximum value for each row? It should be something like this: root |-- distribution: vector (nullable = true) |-- max_index: integer (nullable = true) +----