Spark Dataframe:如何添加索引列:Aka分布式数据索引

Question

我从csv文件中读取数据,但没有索引.

我想在1到行的数字中添加一列.

我该怎么办,谢谢(scala)

Answer 1

使用Scala,您可以使用:

import org.apache.spark.sql.functions._ 

df.withColumn("id",monotonicallyIncreasingId)

您可以参考这个例子和scala 文档.

使用Pyspark,您可以使用:

from pyspark.sql.functions import monotonically_increasing_id 

df_index = df.select("*").withColumn("id", monotonically_increasing_id())

Answer 2

monotonically_increasing_id - 保证生成的ID单调递增且唯一,但不是连续的.

"我想在1到行的数字中添加一列."

假设我们有以下DF

+--------+-------------+-------+
| userId | productCode | count |
+--------+-------------+-------+
|     25 |        6001 |     2 |
|     11 |        5001 |     8 |
|     23 |         123 |     5 |
+--------+-------------+-------+

生成从1开始的ID

val w = Window.orderBy("count")
val result = df.withColumn("index", row_number().over(w))

这将添加通过增加count值排序的索引列.

+--------+-------------+-------+-------+
| userId | productCode | count | index |
+--------+-------------+-------+-------+
|     25 |        6001 |     2 |     1 |
|     23 |         123 |     5 |     2 |
|     11 |        5001 |     8 |     3 |
+--------+-------------+-------+-------+

Answer 3

如何获得一个连续的 id 列id[1, 2, 3, 4...n]：

from pyspark.sql.functions import desc, row_number, monotonically_increasing_id
from pyspark.sql.window import Window

df_with_seq_id = df.withColumn('index_column_name', row_number().over(Window.orderBy(monotonically_increasing_id())) - 1)

请注意，row_number() 从 1 开始，因此如果您想要 0 索引列，则减去 1

Answer 4

注意：以上方法没有给出序列号，但是却给出了递增的id。

做到这一点并确保索引顺序的简单方法如下。 zipWithIndex。

样本数据。

+-------------------+
|               Name|
+-------------------+
|     Ram Ghadiyaram|
|        Ravichandra|
|              ilker|
|               nick|
|             Naveed|
|      Gobinathan SP|
|Sreenivas Venigalla|
|     Jackela Kowski|
|   Arindam Sengupta|
|            Liangpi|
|             Omar14|
|        anshu kumar|
+-------------------+

    package com.example

import org.apache.spark.internal.Logging
import org.apache.spark.sql.SparkSession._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{LongType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row}

/**
  * DistributedDataIndex : Program to index an RDD  with
  */
object DistributedDataIndex extends App with Logging {

  val spark = builder
    .master("local[*]")
    .appName(this.getClass.getName)
    .getOrCreate()

  import spark.implicits._

  val df = spark.sparkContext.parallelize(
    Seq("Ram Ghadiyaram", "Ravichandra", "ilker", "nick"
      , "Naveed", "Gobinathan SP", "Sreenivas Venigalla", "Jackela Kowski", "Arindam Sengupta", "Liangpi", "Omar14", "anshu kumar"
    )).toDF("Name")
  df.show
  logInfo("addColumnIndex here")
  // Add index now...
  val df1WithIndex = addColumnIndex(df)
    .withColumn("monotonically_increasing_id", monotonically_increasing_id)
  df1WithIndex.show(false)

  /**
    * Add Column Index to dataframe to each row
    */
  def addColumnIndex(df: DataFrame) = {
    spark.sqlContext.createDataFrame(
      df.rdd.zipWithIndex.map {
        case (row, index) => Row.fromSeq(row.toSeq :+ index)
      },
      // Create schema for index column
      StructType(df.schema.fields :+ StructField("index", LongType, false)))
  }
}

结果：

+-------------------+-----+---------------------------+
|Name               |index|monotonically_increasing_id|
+-------------------+-----+---------------------------+
|Ram Ghadiyaram     |0    |0                          |
|Ravichandra        |1    |8589934592                 |
|ilker              |2    |8589934593                 |
|nick               |3    |17179869184                |
|Naveed             |4    |25769803776                |
|Gobinathan SP      |5    |25769803777                |
|Sreenivas Venigalla|6    |34359738368                |
|Jackela Kowski     |7    |42949672960                |
|Arindam Sengupta   |8    |42949672961                |
|Liangpi            |9    |51539607552                |
|Omar14             |10   |60129542144                |
|anshu kumar        |11   |60129542145                |
+-------------------+-----+---------------------------+