apache-spark - 使用聚合函数时发生 Spark 数据帧错误

Question

我一直在研究Spark数据帧代码，并且正在使用Spark版本2.2.0。当我尝试在列上运行聚合函数时遇到错误

+----------------------------------+
|amount                            |
+----------------------------------+
|[1197, 8797, 6146, 253, 4521, 955]|
|[1197, 8797, 6146, 253, 4521, 955]|
+----------------------------------+

我想对每一行中存在的数组值求和并将结果添加到现有数据框中

object JsonSpark {
  def main(args: Array[String]): Unit = {
    val spark:SparkSession=SparkSession.builder()
      .master("local[1]")
      .appName(("Spark"))
      .getOrCreate()

    val df=spark.read.option("multiline",true).json("C:\\Users\\Admin\\Desktop\\jsonfile.json")
    val df2=df.select("transactions")
    val df2extract=df2.select("transactions.amount")

    df2extract.selectExpr(
      "aggregate(amount, 0, (x, y) -> x + y) as details_sum"
    ).show()

  }
}

我得到的错误是

Exception in thread "main" org.apache.spark.sql.catalyst.parser.ParseException: 
extraneous input '>' expecting {'(', 'SELECT', 'FROM', 'ADD', 'AS', 'ALL', 'DISTINCT', 'WHERE', 'GROUP', 'BY', 'GROUPING', 'SETS', 'CUBE', 'ROLLUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'IN', NOT, 'NO', 'EXISTS', 'BETWEEN', 'LIKE', RLIKE, 'IS', 'NULL', 'TRUE', 'FALSE', 'NULLS', 'ASC', 'DESC', 'FOR', 'INTERVAL', 'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'JOIN', 'CROSS', 'OUTER', 'INNER', 'LEFT', 'SEMI', 'RIGHT', 'FULL', 'NATURAL', 'ON', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'AFTER', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'VIEW', 'REPLACE', 'INSERT', 'DELETE', 'INTO', 'DESCRIBE', 'EXPLAIN', 'FORMAT', 'LOGICAL', 'CODEGEN', 'COST', 'CAST', 'SHOW', 'TABLES', 'COLUMNS', 'COLUMN', 'USE', 'PARTITIONS', 'FUNCTIONS', 'DROP', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'TO', 'TABLESAMPLE', 'STRATIFY', 'ALTER', 'RENAME', 'ARRAY', 'MAP', 'STRUCT', 'COMMENT', 'SET', 'RESET', 'DATA', 'START', 'TRANSACTION', 'COMMIT', 'ROLLBACK', 'MACRO', 'IGNORE', 'IF', '+', '-', '*', 'DIV', '~', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'USING', 'SERDE', 'SERDEPROPERTIES', 'RECORDREADER', 'RECORDWRITER', 'DELIMITED', 'FIELDS', 'TERMINATED', 'COLLECTION', 'ITEMS', 'KEYS', 'ESCAPED', 'LINES', 'SEPARATED', 'FUNCTION', 'EXTENDED', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'LAZY', 'FORMATTED', 'GLOBAL', TEMPORARY, 'OPTIONS', 'UNSET', 'TBLPROPERTIES', 'DBPROPERTIES', 'BUCKETS', 'SKEWED', 'STORED', 'DIRECTORIES', 'LOCATION', 'EXCHANGE', 'ARCHIVE', 'UNARCHIVE', 'FILEFORMAT', 'TOUCH', 'COMPACT', 'CONCATENATE', 'CHANGE', 'CASCADE', 'RESTRICT', 'CLUSTERED', 'SORTED', 'PURGE', 'INPUTFORMAT', 'OUTPUTFORMAT', DATABASE, DATABASES, 'DFS', 'TRUNCATE', 'ANALYZE', 'COMPUTE', 'LIST', 'STATISTICS', 'PARTITIONED', 'EXTERNAL', 'DEFINED', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'REPAIR', 'RECOVER', 'EXPORT', 'IMPORT', 'LOAD', 'ROLE', 'ROLES', 'COMPACTIONS', 'PRINCIPALS', 'TRANSACTIONS', 'INDEX', 'INDEXES', 'LOCKS', 'OPTION', 'ANTI', 'LOCAL', 'INPATH', 'CURRENT_DATE', 'CURRENT_TIMESTAMP', STRING, BIGINT_LITERAL, SMALLINT_LITERAL, TINYINT_LITERAL, INTEGER_VALUE, DECIMAL_VALUE, DOUBLE_LITERAL, BIGDECIMAL_LITERAL, IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 29)

== SQL ==
aggregate(amount, 0, (x, y) -> x + y) as details_sum
-----------------------------^^^

请帮助我解决此错误。非常感谢!

Answer 1

假设您尝试对数组求和-
spark>=2.4.0

  val df = spark.sql(
      """
        |select amount
        |from values
        |(array(1197, 8797, 6146, 253, 4521, 955)),
        |(array(1197, 8797, 6146, 253, 4521, 955))
        |T(amount)
      """.stripMargin)
    df.show(false)
    df.printSchema()

    /**
      * +----------------------------------+
      * |amount                            |
      * +----------------------------------+
      * |[1197, 8797, 6146, 253, 4521, 955]|
      * |[1197, 8797, 6146, 253, 4521, 955]|
      * +----------------------------------+
      *
      * root
      * |-- amount: array (nullable = false)
      * |    |-- element: integer (containsNull = false)
      */

    df.selectExpr(
      "aggregate(amount, 0, (x, y) -> x + y) as details_sum"
    ).show()

    /**
      * +-----------+
      * |details_sum|
      * +-----------+
      * |      21869|
      * |      21869|
      * +-----------+
      */

spark< 2.4
要么-

    //spark < 2.4
    val intArraySum = udf((arrayInt: mutable.WrappedArray[Int]) => arrayInt.sum)
    df.select(
      intArraySum($"amount").as("details_sum")
    ).show()

    /**
      * +-----------+
      * |details_sum|
      * +-----------+
      * |      21869|
      * |      21869|
      * +-----------+
      */

要么

 val intSeqSum = udf((arrayInt: Seq[Int]) => arrayInt.sum)
    df.select(
      intSeqSum($"amount").as("details_sum")
    ).show()

    /**
      * +-----------+
      * |details_sum|
      * +-----------+
      * |      21869|
      * |      21869|
      * +-----------+
      */

来自评论-

Add result column as new column in the existing dataframe

df.withColumn("details_sum", intSeqSum(function.col("amount")))
.show()