如何在单个查询中针对不同类型的列计算流数据框架上的统计信息? [英] How to compute statistics on a streaming dataframe for different type of columns in a single query?

问题描述

我有一个流数据帧，该数据帧具有三列时间col1，col2.

I have a streaming dataframe having three columns time, col1,col2.

+-----------------------+-------------------+--------------------+
|time                   |col1               |col2                |
+-----------------------+-------------------+--------------------+
|2018-01-10 15:27:21.289|0.4988615628926717 |0.1926744113882285  |
|2018-01-10 15:27:22.289|0.5430687338123434 |0.17084552928040175 |
|2018-01-10 15:27:23.289|0.20527770821641478|0.2221980020202523  |
|2018-01-10 15:27:24.289|0.130852802747647  |0.5213147910202641  |
+-----------------------+-------------------+--------------------+

col1和col2的数据类型是可变的.它可以是字符串或数字数据类型. 因此，我必须计算每一列的统计信息. 对于字符串列，仅计算有效计数和无效计数. 对于时间戳列，仅计算分钟和分钟；最大限度. 对于数字类型列，计算最小值，最大值，平均值和均值. 我必须在一个查询中计算所有统计信息. 现在，我已经针对每种类型的列分别计算了三个查询.

The datatype of col1 and col2 is variable. It could be a string or numeric datatype. So I have to calculate statistics for each column. For string column, calculate only valid count and invalid count. For timestamp column, calculate only min & max. For numeric type column, calculate min, max, average and mean. I have to compute all statistics in a single query. Right now, I have computed with three queries separately for every type of column.

推荐答案

枚举您想要的案例并选择.例如，如果流定义为:

Enumerate cases you want and select. For example, if stream is defined as:

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

val schema = StructType(Seq(
  StructField("v", TimestampType),
  StructField("x", IntegerType),
  StructField("y", StringType),
  StructField("z", DecimalType(10, 2))
))

val df = spark.readStream.schema(schema).format("csv").load("/tmp/foo")

结果将是

val stats = df.select(df.dtypes.flatMap {
  case (c, "StringType") => 
    Seq(count(c) as s"valid_${c}", count("*") - count(c) as s"invalid_${c}")
  case (c, t) if Seq("TimestampType", "DateType") contains t => 
    Seq(min(c), max(c))
  case (c, t) if (Seq("FloatType", "DoubleType", "IntegerType") contains t) || t.startsWith("DecimalType") => 
    Seq(min(c), max(c), avg(c), stddev(c))
  case _ => Seq.empty[Column]
}: _*)

// root
//  |-- min(v): timestamp (nullable = true)
//  |-- max(v): timestamp (nullable = true)
//  |-- min(x): integer (nullable = true)
//  |-- max(x): integer (nullable = true)
//  |-- avg(x): double (nullable = true)
//  |-- stddev_samp(x): double (nullable = true)
//  |-- valid_y: long (nullable = false)
//  |-- invalid_y: long (nullable = false)
//  |-- min(z): decimal(10,2) (nullable = true)
//  |-- max(z): decimal(10,2) (nullable = true)
//  |-- avg(z): decimal(14,6) (nullable = true)
//  |-- stddev_samp(z): double (nullable = true)

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