使用行字段过滤spark数据帧,行字段是字符串数组

Question

使用Spark 1.5和Scala 2.10.6

我正在尝试通过字段"tags"过滤数据帧,这是一个字符串数组.查找标记为"private"的所有行.

val report = df.select("*")
  .where(df("tags").contains("private"))

得到:

线程"main"中的异常org.apache.spark.sql.AnalysisException:由于数据类型不匹配,无法解析'Contains(tags,private)':参数1需要字符串类型,但是'tags'是数组类型.

过滤方法更适合吗？

更新:

数据来自cassandra适配器,但是一个显示我正在尝试做的最小例子,并且还得到上述错误:

  def testData (sc: SparkContext): DataFrame = {
    val stringRDD = sc.parallelize(Seq("""
      { "name": "ed",
        "tags": ["red", "private"]
      }""",
      """{ "name": "fred",
        "tags": ["public", "blue"]
      }""")
    )
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._
    sqlContext.read.json(stringRDD)
  }
  def run(sc: SparkContext) {
    val df1 = testData(sc)
    df1.show()
    val report = df1.select("*")
      .where(df1("tags").contains("private"))
    report.show()
  }

更新:标签数组可以是任意长度,'私人'标签可以在任何位置

更新:一个有效的解决方案:UDF

val filterPriv = udf {(tags: mutable.WrappedArray[String]) => tags.contains("private")}
val report = df1.filter(filterPriv(df1("tags")))

Answer 1

我想如果你使用where(array_contains(...))它会起作用.这是我的结果:

scala> import org.apache.spark.SparkContext
import org.apache.spark.SparkContext

scala> import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.DataFrame

scala> def testData (sc: SparkContext): DataFrame = {
     |     val stringRDD = sc.parallelize(Seq
     |      ("""{ "name": "ned", "tags": ["blue", "big", "private"] }""",
     |       """{ "name": "albert", "tags": ["private", "lumpy"] }""",
     |       """{ "name": "zed", "tags": ["big", "private", "square"] }""",
     |       """{ "name": "jed", "tags": ["green", "small", "round"] }""",
     |       """{ "name": "ed", "tags": ["red", "private"] }""",
     |       """{ "name": "fred", "tags": ["public", "blue"] }"""))
     |     val sqlContext = new org.apache.spark.sql.SQLContext(sc)
     |     import sqlContext.implicits._
     |     sqlContext.read.json(stringRDD)
     |   }
testData: (sc: org.apache.spark.SparkContext)org.apache.spark.sql.DataFrame

scala>   
     | val df = testData (sc)
df: org.apache.spark.sql.DataFrame = [name: string, tags: array<string>]

scala> val report = df.select ("*").where (array_contains (df("tags"), "private"))
report: org.apache.spark.sql.DataFrame = [name: string, tags: array<string>]

scala> report.show
+------+--------------------+
|  name|                tags|
+------+--------------------+
|   ned|[blue, big, private]|
|albert|    [private, lumpy]|
|   zed|[big, private, sq...|
|    ed|      [red, private]|
+------+--------------------+

请注意,如果你写where(array_contains(df("tags"), "private")),它可以工作,但如果你写where(df("tags").array_contains("private"))(更直接类似于你最初写的)它失败了array_contains is not a member of org.apache.spark.sql.Column.看一下源代码Column,我看到有一些东西要处理contains(构建一个Contains实例)但不是array_contains.也许这是一个疏忽.