5

我有两个版本的 Spark 代码。第一个使用带有 Kafka 源的结​​构化流：

dfStream.printSchema()
//root
//|-- dt: string (nullable = true)
//|-- ip: string (nullable = true)
//|-- device: string (nullable = true)

val dfWindowed = dfStream                                                
    .groupBy($"ip")
    .agg(concat_ws(",", collect_list($"device")).alias("devices"))
    .writeStream                                             
    .outputMode("complete")                                              
    .format("memory")                                                        
    .start()

第二个从文件中读取。但是数据真的和上面一样：

logDF.printSchema() 
//root
//|-- dt: string (nullable = true)
//|-- ip: string (nullable = true)
//|-- device: string (nullable = true)

logDF.repartition(32)                                                                                                   
    .groupBy("ip")
    .agg(concat_ws(",", collect_list($"device")).alias("devices"))

问题是，虽然第二个效果很好，但第一个一直给我以下错误： 

at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:284)
    at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:177)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 9 in stage 1.0 failed 4 times, most recent failure: Lost task 9.3 in stage 1.0 (TID 28, c3-hadoop-prc-st3417.bj, executor 3): java.lang.RuntimeException: Collect cannot be used in partial aggregations.

长句..但错误似乎如下：

java.lang.RuntimeException: Collect cannot be used in partial aggregations.

我发现了几个相关的 SO 问题，但到目前为止还没有解决方案。非常感谢您对以下方面的任何建议：

  1. “部分聚合”的含义以及静态（非流）数据集没有此类问题的原因，
  2. 一种解决方法...
4

2 回答 2

0

我想您可以将解决方法视为groupByKey -> reduceGroup链，例如：

case class Data(ip: Int, column1: String, column2: String)

import spark.implicits._

val path = "/tmp/spark-streaming/test-data"

  Seq(
    (1, "val1", "field1"),
    (1, "val2", "field2"),
    (1, "val3", "field3"),
    (1, "val4", "field4"),
    (2, "val1", "field1"),
    (3, "val1", "field1"),
    (4, "val1", "field1"),
    (4, "val2", "field2")
  ).toDF("ip", "column1", "column2").write.mode("overwrite").parquet(path)

  spark.read.parquet(path).printSchema()

  spark.read.parquet(path).show(false)

  spark.sql("SET spark.sql.streaming.schemaInference=true")

  val stream = spark.readStream.parquet(path).as[Data]

  val result =
    stream
      .groupByKey(_.ip)
      .reduceGroups { (l, r) =>
        l.copy(column1 = l.column1.concat(",").concat(r.column1), column2 = l.column2.concat(",").concat(r.column2))
      }
      .map(_._2)



   result.printSchema()

   result.writeStream
    .option("checkpointLocation", "/tmp/spark-streaming-checkpoint-test")
    .option("truncate", "false")
    .format("console")
    .outputMode("update")
    .start()
    .awaitTermination(300000)

Seq(
    (1, "val5", "field5"),
    (2, "val2", "field2"),
    (3, "val2", "field2"),
    (4, "val3", "field3")
  ).toDF("ip", "column1", "column2").write.mode("append").parquet(path)

这将导致如下所示：

+---+-------------------+---------------------------+                           
|ip |column1            |column2                    |
+---+-------------------+---------------------------+
|1  |val1,val2,val3,val4|field1,field2,field3,field4|
|3  |val1               |field1                     |
|4  |val1,val2          |field1,field2              |
|2  |val1               |field1                     |
+---+-------------------+---------------------------+

注意：从 2.3.1 开始，完整模式不支持聚合操作

希望能帮助到你！

于 2018-08-14T22:33:19.080 回答
0

我最终写了一个 UDAF，正如这里所建议的那样。

class ConcatString extends UserDefinedAggregateFunction {
    // This is the input fields for your aggregate function.
    override def inputSchema: org.apache.spark.sql.types.StructType =
        StructType(StructField("value", StringType) :: Nil)

    // This is the internal fields you keep for computing your aggregate.
    override def bufferSchema: StructType = StructType(
        StructField("concated", StringType) :: Nil)

    // This is the output type of your aggregatation function.
    override def dataType: DataType = StringType

    override def deterministic: Boolean = true

    // This is the initial value for your buffer schema.
    override def initialize(buffer: MutableAggregationBuffer): Unit = {
        buffer(0) = "-1" 
    }

    // This is how to update your buffer schema given an input.
    override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
         buffer(0) = (buffer.getAs[String](0) + ",,," + input.getAs[String](0))
                    .stripPrefix("-1,,,")
    }

    // This is how to merge two objects with the bufferSchema type.
    override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
        buffer1(0) = (buffer1.getAs[String](0) + ",,," + buffer2.getAs[String](0))
                    .stripPrefix("-1,,,")
                    .stripSuffix(",,,-1")
    }

    // This is where you output the final value, given the final value of your bufferSchema.
    override def evaluate(buffer: Row): Any = {
        buffer.getString(0)
    }
}

注意：分隔符是',,,'。看似奇怪的“-1”初始化和随后的 stripPre/Subfix() 是我无意中连接缓冲区初始值的糟糕解决方案。

用法如下：

val udafConcatCol = new ConcatString
val dfWindowed = dfStream                                                
     .groupBy($"ip")
     .agg(udafConcatCol(col("device")).as("devices")
     ....
于 2018-08-17T09:47:22.537 回答