apache-spark-dataset | 易学教程

Encode an ADT / sealed trait hierarchy into Spark DataSet column

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问题 If I want to store an Algebraic Data Type (ADT) (ie a Scala sealed trait hierarchy) within a Spark DataSet column, what is the best encoding strategy? For example, if I have an ADT where the leaf types store different kinds of data: sealed trait Occupation case object SoftwareEngineer extends Occupation case class Wizard(level: Int) extends Occupation case class Other(description: String) extends Occupation Whats the best way to construct a: org.apache.spark.sql.DataSet[Occupation] 回答1: TL;DR

Why is “Unable to find encoder for type stored in a Dataset” when creating a dataset of custom case class?

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Spark 2.0 (final) with Scala 2.11.8. The following super simple code yields the compilation error Error:(17, 45) 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 serializing other types will be added in future releases. import org.apache.spark.sql.SparkSession case class SimpleTuple(id: Int, desc: String) object DatasetTest { val dataList = List( SimpleTuple(5, "abc"), SimpleTuple(6, "bcd") ) def main(args: Array[String]): Unit = { val sparkSession = SparkSession

Perform a typed join in Scala with Spark Datasets

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问题 I like Spark Datasets as they give me analysis errors and syntax errors at compile time and also allow me to work with getters instead of hard-coded names/numbers. Most computations can be accomplished with Dataset’s high-level APIs. For example, it’s much simpler to perform agg, select, sum, avg, map, filter, or groupBy operations by accessing a Dataset typed object’s than using RDD rows’ data fields. However the join operation is missing from this, I read that I can do a join like this ds1

Difference between DataFrame, Dataset, and RDD in Spark

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问题 I\'m just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for Dataset[Row] ) in Apache Spark? Can you convert one to the other? 回答1: A DataFrame is defined well with a google search for "DataFrame definition": A data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case. So, a DataFrame has additional metadata due to its tabular format,

Difference between DataFrame, Dataset, and RDD in Spark

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DataFrame / Dataset groupBy behaviour/optimization

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问题 Suppose we have DataFrame df consisting of the following columns: Name, Surname, Size, Width, Length, Weigh Now we want to perform a couple of operations, for example we want to create a couple of DataFrames containing data about Size and Width. val df1 = df.groupBy(\"surname\").agg( sum(\"size\") ) val df2 = df.groupBy(\"surname\").agg( sum(\"width\") ) as you can notice, other columns, like Length are not used anywhere. Is Spark smart enough to drop the redundant columns before the

How to store custom objects in Dataset?

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问题 According to Introducing Spark Datasets: As we look forward to Spark 2.0, we plan some exciting improvements to Datasets, specifically: ... Custom encoders – while we currently autogenerate encoders for a wide variety of types, we’d like to open up an API for custom objects. and attempts to store custom type in a Dataset lead to following error like: Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by

Encoder error while trying to map dataframe row to updated row

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When I m trying to do the same thing in my code as mentioned below dataframe.map(row => { val row1 = row.getAs[String](1) val make = if (row1.toLowerCase == "tesla") "S" else row1 Row(row(0),make,row(2)) }) I have taken the above reference from here: Scala: How can I replace value in Dataframs using scala But I am getting encoder error as Unable to find encoder for type stored in a Dataset. Primitive types (Int, S tring, etc) and Product types (case classes) are supported by importing spark.im plicits._ Support for serializing other types will be added in future releases. Note: I am using