Spark UserDefinedAggregateFunction：scala.MatchError 0.0（class java.lang.Double）
Spark UserDefinedAggregateFunction: scala.MatchError 0.0 (of class java.lang.Double)
我正在尝试在 Spark 2.0.2 上使用 UserDefinedAggregateFunction 和 Scala，但我遇到了匹配错误。我创建了以下内容作为测试用例，我正在编写的代码与以下内容类似。
我正在尝试通过聚合 window 来累积一个值。这不仅仅是累加和，我需要根据某些条件计算要保留的数量。
作为测试用例，我创建了一个摊销table，我必须在其中计算每个月的期初和期末余额。
数据看起来像：
+------+--------+------------+---------+
|Period| Capital|InterestRate|Repayment|
+------+--------+------------+---------+
|201601| 0.00 | 0.10 | 0.00 |
|201602|1000.00 | 0.00 | 0.00 |
|201603|2000.00 | 0.10 | 0.00 |
|201604| 0.00 | 0.10 | -200.00 |
|201605| 0.00 | 0.10 | -200.00 |
|201606| 0.00 | 0.10 | -200.00 |
|201607| 0.00 | 0.10 | -200.00 |
|201608| 0.00 | 0.00 | -200.00 |
|201609| 0.00 | 0.10 | -200.00 |
|201610| 0.00 | 0.10 | -200.00 |
|201611| 0.00 | 0.10 | -200.00 |
|201612| 0.00 | 0.10 | -200.00 |
+------+--------+------------+---------+
我无法正确设置 CSV 格式，但我已将其添加到此处的要点中：https://gist.github.com/nevi-me/8b2362a5365e73af947fc13bb5836adc。
我正在尝试计算 Opening 和 Closing 余额，然后 return 来自聚合的 Closing 余额。
斯卡拉
package me.nevi
import org.apache.spark.sql._
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction, Window}
import org.apache.spark.sql.types.{StructType, DoubleType, DataType}
object AggregationTest {
object amortisedClosingBalance extends UserDefinedAggregateFunction {
override def inputSchema: StructType = new StructType().add("Capital", DoubleType).add("InterestRate", DoubleType).add("Repayment", DoubleType)
override def bufferSchema: StructType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def dataType: DataType = new StructType().add("Closing", DoubleType)
override def deterministic: Boolean = true
override def initialize(buffer: MutableAggregationBuffer): Unit = {
buffer.update(0, 0.0)
buffer.update(1, 0.0)
}
override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if (!input.isNullAt(0)) {
println(buffer.get(0))
println(buffer.get(1))
buffer.update(0, buffer.getDouble(1))
// (opening + capital) * interestrate - repayment
buffer.update(1, (buffer.getDouble(0) + input.getDouble(0)) * input.getDouble(1) + input.getDouble(2))
} else {
// if first record?
buffer.update(0, input.getDouble(0))
buffer.update(1, input.getDouble(0))
}
}
override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
buffer1.update(0, buffer1.getDouble(0))
buffer1.update(1, buffer1.getDouble(1))
}
override def evaluate(buffer: Row): Any = {
buffer.getDouble(1)
}
}
def main(args: Array[String]): Unit = {
System.setProperty("hadoop.home.dir", "C:/spark")
System.setProperty("spark.sql.warehouse.dir", "file:///tmp/spark-warehouse")
val spark: SparkSession = SparkSession.builder()
.master("local[*]")
.appName("Aggregation Test")
.getOrCreate()
import spark.implicits._
val df = spark.read.option("header", true).csv("file:///d:/interest_calc.csv")
df.show()
val windowSpec = Window.orderBy(df.col("Period"))
val calc = df.withColumn("Closing", amortisedClosingBalance($"Capital", $"InterestRate", $"Repayment").over(windowSpec))
calc.show()
}
}
我得到异常：
scala.MatchError: 0.0 (of class java.lang.Double)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:256)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:251)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter.apply(CatalystTypeConverters.scala:403)
at org.apache.spark.sql.execution.aggregate.ScalaUDAF.eval(udaf.scala:440)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificMutableProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.AggregateProcessor.evaluate(WindowExec.scala:1029)
at org.apache.spark.sql.execution.UnboundedPrecedingWindowFunctionFrame.write(WindowExec.scala:822)
at org.apache.spark.sql.execution.WindowExec$$anonfun$$anon.next(WindowExec.scala:398)
at org.apache.spark.sql.execution.WindowExec$$anonfun$$anon.next(WindowExec.scala:289)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:370)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:246)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:240)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:803)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:803)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
有谁知道我做错了什么？我最初使用的是 Spark 2.0.0，我遇到了其他人也有类似的 UDTF 问题，建议升级到 2.0.1，但是升级后；我的问题仍然存在。
解决方案：对于任何感兴趣的人
根据已接受的答案，问题出在我的架构上。以下是计算正常的片段。
package me.nevi
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction, Window}
import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
object AggregationTest {
object amortisedClosingBalance extends UserDefinedAggregateFunction {
override def inputSchema: StructType = new StructType().add("Capital", DoubleType).add("InterestRate", DoubleType).add("Repayment", DoubleType)
override def bufferSchema: StructType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def dataType: DataType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def deterministic: Boolean = true
override def initialize(buffer: MutableAggregationBuffer): Unit = {
buffer.update(0, 0.0)
buffer.update(1, 0.0)
}
override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if (!input.isNullAt(0)) {
println(buffer.get(0))
println(buffer.get(1))
buffer.update(0, buffer.getDouble(1))
// (opening + capital) * interestrate - repayment
buffer.update(1, input.getDouble(0)
+ buffer.getDouble(0) + input.getDouble(2) + (buffer.getDouble(0) + input.getDouble(0)) * (input.getDouble(1) / 12))
} else {
// if first record?
buffer.update(0, input.getDouble(0))
buffer.update(1, input.getDouble(0))
}
}
override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
buffer1.update(0, buffer1.getDouble(0))
buffer1.update(1, buffer1.getDouble(1))
}
override def evaluate(buffer: Row): Any = {
Row(buffer.getDouble(0), buffer.getDouble(1))
}
}
def main(args: Array[String]): Unit = {
System.setProperty("hadoop.home.dir", "C:/spark")
System.setProperty("spark.sql.warehouse.dir", "file:///tmp/spark-warehouse")
val spark: SparkSession = SparkSession.builder()
.master("local[*]")
.appName("Aggregation Test")
.getOrCreate()
import spark.implicits._
val df = spark.read.option("header", true).csv("file:///d:/interest_calc.csv")
df.show()
val windowSpec = Window.orderBy(df.col("Period").asc)
var calc = df.withColumn("Calcs", amortisedClosingBalance($"Capital", $"InterestRate", $"Repayment").over(windowSpec))
calc = calc.withColumn("Opening", round($"Calcs".getField("Opening"), 2)).withColumn("Closing", round($"Calcs".getField("Closing"),2))
.drop("Calcs")
calc.show()
}
}
结果如下：
+------+--------+------------+---------+-------+-------+
|Period| Capital|InterestRate|Repayment|Opening|Closing|
+------+--------+------------+---------+-------+-------+
|201601| 0.00 | 0.10 | 0.00 | 0.0| 0.0|
|201602|1000.00 | 0.00 | 0.00 | 0.0| 1000.0|
|201603|2000.00 | 0.10 | 0.00 | 1000.0| 3025.0|
|201604| 0.00 | 0.10 | -200.00 | 3025.0|2850.21|
|201605| 0.00 | 0.10 | -200.00 |2850.21|2673.96|
|201606| 0.00 | 0.10 | -200.00 |2673.96|2496.24|
|201607| 0.00 | 0.10 | -200.00 |2496.24|2317.05|
|201608| 0.00 | 0.00 | -200.00 |2317.05|2117.05|
|201609| 0.00 | 0.10 | -200.00 |2117.05|1934.69|
|201610| 0.00 | 0.10 | -200.00 |1934.69|1750.81|
|201611| 0.00 | 0.10 | -200.00 |1750.81| 1565.4|
|201612| 0.00 | 0.10 | -200.00 | 1565.4|1378.44|
+------+--------+------------+---------+-------+-------+
由于 dataType 定义不正确，您得到一个异常。您将其声明为：
StructType(StructField(Closing,DoubleType,true))
实际上你 return 是一个标量。应该定义为：
override def dataType: DataType = DoubleType
或者您应该重新定义 evalute，例如：
override def evaluate(buffer: Row): Any = {
Row(buffer.getDouble(1))
}
后一个将return嵌套列：
|-- Closing: struct (nullable = true)
| |-- Closing: double (nullable = true)
所以这可能不是您要找的。
我正在尝试在 Spark 2.0.2 上使用 UserDefinedAggregateFunction 和 Scala，但我遇到了匹配错误。我创建了以下内容作为测试用例，我正在编写的代码与以下内容类似。
我正在尝试通过聚合 window 来累积一个值。这不仅仅是累加和，我需要根据某些条件计算要保留的数量。
作为测试用例，我创建了一个摊销table，我必须在其中计算每个月的期初和期末余额。
数据看起来像：
+------+--------+------------+---------+
|Period| Capital|InterestRate|Repayment|
+------+--------+------------+---------+
|201601| 0.00 | 0.10 | 0.00 |
|201602|1000.00 | 0.00 | 0.00 |
|201603|2000.00 | 0.10 | 0.00 |
|201604| 0.00 | 0.10 | -200.00 |
|201605| 0.00 | 0.10 | -200.00 |
|201606| 0.00 | 0.10 | -200.00 |
|201607| 0.00 | 0.10 | -200.00 |
|201608| 0.00 | 0.00 | -200.00 |
|201609| 0.00 | 0.10 | -200.00 |
|201610| 0.00 | 0.10 | -200.00 |
|201611| 0.00 | 0.10 | -200.00 |
|201612| 0.00 | 0.10 | -200.00 |
+------+--------+------------+---------+
我无法正确设置 CSV 格式，但我已将其添加到此处的要点中：https://gist.github.com/nevi-me/8b2362a5365e73af947fc13bb5836adc。
我正在尝试计算 Opening 和 Closing 余额，然后 return 来自聚合的 Closing 余额。
斯卡拉
package me.nevi
import org.apache.spark.sql._
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction, Window}
import org.apache.spark.sql.types.{StructType, DoubleType, DataType}
object AggregationTest {
object amortisedClosingBalance extends UserDefinedAggregateFunction {
override def inputSchema: StructType = new StructType().add("Capital", DoubleType).add("InterestRate", DoubleType).add("Repayment", DoubleType)
override def bufferSchema: StructType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def dataType: DataType = new StructType().add("Closing", DoubleType)
override def deterministic: Boolean = true
override def initialize(buffer: MutableAggregationBuffer): Unit = {
buffer.update(0, 0.0)
buffer.update(1, 0.0)
}
override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if (!input.isNullAt(0)) {
println(buffer.get(0))
println(buffer.get(1))
buffer.update(0, buffer.getDouble(1))
// (opening + capital) * interestrate - repayment
buffer.update(1, (buffer.getDouble(0) + input.getDouble(0)) * input.getDouble(1) + input.getDouble(2))
} else {
// if first record?
buffer.update(0, input.getDouble(0))
buffer.update(1, input.getDouble(0))
}
}
override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
buffer1.update(0, buffer1.getDouble(0))
buffer1.update(1, buffer1.getDouble(1))
}
override def evaluate(buffer: Row): Any = {
buffer.getDouble(1)
}
}
def main(args: Array[String]): Unit = {
System.setProperty("hadoop.home.dir", "C:/spark")
System.setProperty("spark.sql.warehouse.dir", "file:///tmp/spark-warehouse")
val spark: SparkSession = SparkSession.builder()
.master("local[*]")
.appName("Aggregation Test")
.getOrCreate()
import spark.implicits._
val df = spark.read.option("header", true).csv("file:///d:/interest_calc.csv")
df.show()
val windowSpec = Window.orderBy(df.col("Period"))
val calc = df.withColumn("Closing", amortisedClosingBalance($"Capital", $"InterestRate", $"Repayment").over(windowSpec))
calc.show()
}
}
我得到异常：
scala.MatchError: 0.0 (of class java.lang.Double)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:256)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:251)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter.apply(CatalystTypeConverters.scala:403)
at org.apache.spark.sql.execution.aggregate.ScalaUDAF.eval(udaf.scala:440)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificMutableProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.AggregateProcessor.evaluate(WindowExec.scala:1029)
at org.apache.spark.sql.execution.UnboundedPrecedingWindowFunctionFrame.write(WindowExec.scala:822)
at org.apache.spark.sql.execution.WindowExec$$anonfun$$anon.next(WindowExec.scala:398)
at org.apache.spark.sql.execution.WindowExec$$anonfun$$anon.next(WindowExec.scala:289)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:370)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:246)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:240)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:803)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:803)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
有谁知道我做错了什么？我最初使用的是 Spark 2.0.0，我遇到了其他人也有类似的 UDTF 问题，建议升级到 2.0.1，但是升级后；我的问题仍然存在。
解决方案：对于任何感兴趣的人
根据已接受的答案，问题出在我的架构上。以下是计算正常的片段。
package me.nevi
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction, Window}
import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
object AggregationTest {
object amortisedClosingBalance extends UserDefinedAggregateFunction {
override def inputSchema: StructType = new StructType().add("Capital", DoubleType).add("InterestRate", DoubleType).add("Repayment", DoubleType)
override def bufferSchema: StructType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def dataType: DataType = new StructType().add("Opening", DoubleType).add("Closing", DoubleType)
override def deterministic: Boolean = true
override def initialize(buffer: MutableAggregationBuffer): Unit = {
buffer.update(0, 0.0)
buffer.update(1, 0.0)
}
override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if (!input.isNullAt(0)) {
println(buffer.get(0))
println(buffer.get(1))
buffer.update(0, buffer.getDouble(1))
// (opening + capital) * interestrate - repayment
buffer.update(1, input.getDouble(0)
+ buffer.getDouble(0) + input.getDouble(2) + (buffer.getDouble(0) + input.getDouble(0)) * (input.getDouble(1) / 12))
} else {
// if first record?
buffer.update(0, input.getDouble(0))
buffer.update(1, input.getDouble(0))
}
}
override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
buffer1.update(0, buffer1.getDouble(0))
buffer1.update(1, buffer1.getDouble(1))
}
override def evaluate(buffer: Row): Any = {
Row(buffer.getDouble(0), buffer.getDouble(1))
}
}
def main(args: Array[String]): Unit = {
System.setProperty("hadoop.home.dir", "C:/spark")
System.setProperty("spark.sql.warehouse.dir", "file:///tmp/spark-warehouse")
val spark: SparkSession = SparkSession.builder()
.master("local[*]")
.appName("Aggregation Test")
.getOrCreate()
import spark.implicits._
val df = spark.read.option("header", true).csv("file:///d:/interest_calc.csv")
df.show()
val windowSpec = Window.orderBy(df.col("Period").asc)
var calc = df.withColumn("Calcs", amortisedClosingBalance($"Capital", $"InterestRate", $"Repayment").over(windowSpec))
calc = calc.withColumn("Opening", round($"Calcs".getField("Opening"), 2)).withColumn("Closing", round($"Calcs".getField("Closing"),2))
.drop("Calcs")
calc.show()
}
}
结果如下：
+------+--------+------------+---------+-------+-------+
|Period| Capital|InterestRate|Repayment|Opening|Closing|
+------+--------+------------+---------+-------+-------+
|201601| 0.00 | 0.10 | 0.00 | 0.0| 0.0|
|201602|1000.00 | 0.00 | 0.00 | 0.0| 1000.0|
|201603|2000.00 | 0.10 | 0.00 | 1000.0| 3025.0|
|201604| 0.00 | 0.10 | -200.00 | 3025.0|2850.21|
|201605| 0.00 | 0.10 | -200.00 |2850.21|2673.96|
|201606| 0.00 | 0.10 | -200.00 |2673.96|2496.24|
|201607| 0.00 | 0.10 | -200.00 |2496.24|2317.05|
|201608| 0.00 | 0.00 | -200.00 |2317.05|2117.05|
|201609| 0.00 | 0.10 | -200.00 |2117.05|1934.69|
|201610| 0.00 | 0.10 | -200.00 |1934.69|1750.81|
|201611| 0.00 | 0.10 | -200.00 |1750.81| 1565.4|
|201612| 0.00 | 0.10 | -200.00 | 1565.4|1378.44|
+------+--------+------------+---------+-------+-------+
由于 dataType 定义不正确，您得到一个异常。您将其声明为：
StructType(StructField(Closing,DoubleType,true))
实际上你 return 是一个标量。应该定义为：
override def dataType: DataType = DoubleType
或者您应该重新定义 evalute，例如：
override def evaluate(buffer: Row): Any = {
Row(buffer.getDouble(1))
}
后一个将return嵌套列：
|-- Closing: struct (nullable = true)
| |-- Closing: double (nullable = true)
所以这可能不是您要找的。