从 avro 文件将数据集转换为数据框答案

【问题标题】：Convert dataset to dataframe from an avro file从 avro 文件将数据集转换为数据框
【发布时间】：2021-05-01 15:58:47
【问题描述】：

我编写了一个 scala 脚本来加载一个 avro 文件，并使用生成的数据（以检索顶级贡献者）。问题是，在加载文件时，它提供了一个我无法转换为数据框的数据集，因为它包含一些复杂的类型：

    val history_src = "path_to_avro_files\\frwiki*.avro"
    val revisions_dataset = spark.read.format("avro").load(history_src) 
//gives a dataset the we can see the data and make a take(1) without problems 
    
    val first_essay = revisions_dataset.map(row => (row.getString(0), row.getLong(2), row.get(3).asInstanceOf[mutable.WrappedArray[Revision]].array
      .map(x=> (x.r_contributor.r_username, x.r_contributor.r_contributor_id, x.r_contributor.r_contributor_ip)))).take(1) 
//gives GenericRowWithSchema cannot be cast to Revision

    val second_essay = revisions_dataset.map(row => (row.getString(0), row.getLong(2), row.get(3).asInstanceOf[mutable.WrappedArray[GenericRowWithSchema]].toStream
      .map(x=> (x.getLong(0),row.get(3).asInstanceOf[mutable.WrappedArray[GenericRowWithSchema]].map(c => (c.getLong(0))))))).take(1) 
//  gives WrappedArray$ofRef cannot be cast to scala.collection.mutable.ArrayBuffer

我使用下面的案例类尝试使用编码器和编码器，但没有成功

  case class History (title: String, namespace: Long, id: Long, revisions: Array[Revision])
  case class Contributor (r_username: String, r_contributor_id: Long, r_contributor_ip: String)
  case class Revision(r_id: Long, r_parent_id: Long, timestamp : Long, r_contributor: Contributor, sha: String)

我可以从我的 revisions_dataset 生成架构是这样的，它给出了这个：

root
|-- p_title: string (nullable = true)
|-- p_namespace: long (nullable = true)
|-- p_id: long (nullable = true)
|-- p_revisions: array (nullable = true)
|    |-- element: struct (containsNull = true)
|    |    |-- r_id: long (nullable = true)
|    |    |-- r_parent_id: long (nullable = true)
|    |    |-- r_timestamp: long (nullable = true)
|    |    |-- r_contributor: struct (nullable = true)
|    |    |    |-- r_username: string (nullable = true)
|    |    |    |-- r_contributor_id: long (nullable = true)
|    |    |    |-- r_contributor_ip: string (nullable = true)
|    |    |-- r_sha1: string (nullable = true)

我的目标是拥有一个数据框，以便能够检索修订列表中的贡献者列表并将其展平以在页面内包含贡献者列表（与标题具有相同的级别）。

有什么帮助吗？

【问题讨论】：

标签： scala dataframe apache-spark spark-avro

【解决方案1】：

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

val r1 = Revision(1, 1, 1, Contributor("c1", 1, "ip1"), "sha")
val r2 = Revision(1, 1, 1, Contributor("c2", 2, "ip2"), "sha")
val r3 = Revision(1, 1, 1, Contributor("c3", 3, "ip3"), "sha")
val revisions_dataset = Seq(
  ("title1", 0L, 1L, Array(r1, r2)),
  ("title1", 0L, 2L, Array(r1, r3)),
  ("title1", 0L, 3L, Array(r2))
).toDF("p_title", "p_namespace", "p_id", "p_revisions")

val flattened = revisions_dataset.select($"p_title", $"p_id", explode($"p_revisions").alias("p_revision"))
        .withColumn("r_contributor_username", $"p_revision.r_contributor.r_username")
        .withColumn("r_contributor_id", $"p_revision.r_contributor.r_contributor_id")
        .withColumn("r_contributor_ip", $"p_revision.r_contributor.r_contributor_ip")
        .drop("p_revision")

flattened.show(false)

输出：

+-------+----+----------------------+----------------+----------------+
|p_title|p_id|r_contributor_username|r_contributor_id|r_contributor_ip|
+-------+----+----------------------+----------------+----------------+
|title1 |1   |c1                    |1               |ip1             |
|title1 |1   |c2                    |2               |ip2             |
|title1 |2   |c1                    |1               |ip1             |
|title1 |2   |c3                    |3               |ip3             |
|title1 |3   |c2                    |2               |ip2             |
+-------+----+----------------------+----------------+----------------+

【讨论】：