从spark行获取spark列_大数据知识库

我对scala、spark还是个新手，所以我正在努力创建一个map函数。dataframe上的map函数是一行（org.apache.spark.sql.row），我一直在松散地关注本文。

val rddWithExceptionHandling = filterValueDF.rdd.map { row: Row =>
    val parsed = Try(from_avro(???, currentValueSchema.value, fromAvroOptions)) match {
        case Success(parsedValue) => List(parsedValue, null)
        case Failure(ex) => List(null, ex.toString)
    }
    Row.fromSeq(row.toSeq.toList ++ parsed)
}

这个 from_avro 函数想要接受一个列（org.apache.spark.sql.column），但是我在文档中找不到从行中获取列的方法。
我完全可以接受这样的想法，我可能把整件事都做错了。最终我的目标是解析来自结构流的字节。解析后的记录被写入增量表a，失败的记录被写入另一个增量表b
对于上下文，源表如下所示：

编辑- from_avro “坏记录”返回null
有一些评论说 from_avro 如果无法解析“坏记录”，则返回null。默认情况下 from_avro 使用模式 FAILFAST 如果解析失败，将引发异常。如果把模式设置为 PERMISSIVE 返回模式形状的对象，但所有属性都为null（也不是特别有用…）。链接到ApacheAvro数据源指南-spark 3.1.1文档
这是我最初的命令：

val parsedDf = filterValueDF.select($"topic", 
                                    $"partition", 
                                    $"offset", 
                                    $"timestamp", 
                                    $"timestampType", 
                                    $"valueSchemaId", 
                                    from_avro($"fixedValue", currentValueSchema.value, fromAvroOptions).as('parsedValue))

如果有任何不正确的行，则终止作业 org.apache.spark.SparkException: Job aborted. 异常日志的一个片段：

Caused by: org.apache.spark.SparkException: Malformed records are detected in record parsing. Current parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:111)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:732)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$2(FileFormatWriter.scala:291)
    at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1615)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:300)
    ... 10 more
    Suppressed: java.lang.NullPointerException
        at shaded.databricks.org.apache.hadoop.fs.azure.NativeAzureFileSystem$NativeAzureFsOutputStream.write(NativeAzureFileSystem.java:1099)
        at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.write(FSDataOutputStream.java:58)
        at java.io.DataOutputStream.write(DataOutputStream.java:107)
        at org.apache.parquet.hadoop.util.HadoopPositionOutputStream.write(HadoopPositionOutputStream.java:50)
        at shaded.parquet.org.apache.thrift.transport.TIOStreamTransport.write(TIOStreamTransport.java:145)
        at shaded.parquet.org.apache.thrift.transport.TTransport.write(TTransport.java:107)
        at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeByteDirect(TCompactProtocol.java:482)
        at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeByteDirect(TCompactProtocol.java:489)
        at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeFieldBeginInternal(TCompactProtocol.java:252)
        at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeFieldBegin(TCompactProtocol.java:234)
        at org.apache.parquet.format.InterningProtocol.writeFieldBegin(InterningProtocol.java:74)
        at org.apache.parquet.format.FileMetaData$FileMetaDataStandardScheme.write(FileMetaData.java:1184)
        at org.apache.parquet.format.FileMetaData$FileMetaDataStandardScheme.write(FileMetaData.java:1051)
        at org.apache.parquet.format.FileMetaData.write(FileMetaData.java:949)
        at org.apache.parquet.format.Util.write(Util.java:222)
        at org.apache.parquet.format.Util.writeFileMetaData(Util.java:69)
        at org.apache.parquet.hadoop.ParquetFileWriter.serializeFooter(ParquetFileWriter.java:757)
        at org.apache.parquet.hadoop.ParquetFileWriter.end(ParquetFileWriter.java:750)
        at org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:135)
        at org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:165)
        at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetOutputWriter.scala:42)
        at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.releaseResources(FileFormatDataWriter.scala:58)
        at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.abort(FileFormatDataWriter.scala:84)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$3(FileFormatWriter.scala:297)
        at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1626)
        ... 11 more
Caused by: java.lang.ArithmeticException: Unscaled value too large for precision
    at org.apache.spark.sql.types.Decimal.set(Decimal.scala:83)
    at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:577)
    at org.apache.spark.sql.avro.AvroDeserializer.createDecimal(AvroDeserializer.scala:308)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$newWriter$16(AvroDeserializer.scala:177)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$newWriter$16$adapted(AvroDeserializer.scala:174)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$1(AvroDeserializer.scala:336)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$1$adapted(AvroDeserializer.scala:332)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$2(AvroDeserializer.scala:354)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$2$adapted(AvroDeserializer.scala:351)
    at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$converter$3(AvroDeserializer.scala:75)
    at org.apache.spark.sql.avro.AvroDeserializer.deserialize(AvroDeserializer.scala:89)
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:101)
    ... 16 more

为了从row对象获取特定的列，可以使用 row.get(i) 或将列名与 row.getAs[T]("columnName") . 在这里您可以查看row类的详细信息。
那么您的代码如下所示：

val rddWithExceptionHandling = filterValueDF.rdd.map { row: Row =>
    val binaryFixedValue = row.getSeq[Byte](6) // or row.getAs[Seq[Byte]]("fixedValue")
    val parsed = Try(from_avro(binaryFixedValue, currentValueSchema.value, fromAvroOptions)) match {
        case Success(parsedValue) => List(parsedValue, null)
        case Failure(ex) => List(null, ex.toString)
    }
    Row.fromSeq(row.toSeq.toList ++ parsed)
}

尽管在您的例子中，您实际上不需要进入map函数，因为这样您就必须在 from_avro 使用DataFrameAPI。这就是你不能打电话的原因 from_avro 直接从 map 因为 Column 类只能与dataframe api结合使用，即： df.select($"c1") ，这里c1是列的一个示例。为了使用 from_avro ，如您最初所想，只需键入：

filterValueDF.select(from_avro($"fixedValue", currentValueSchema))

正如@mike已经提到的，如果 from_avro 无法解析avro内容将返回null。最后，如果要将成功行与失败行分开，可以执行以下操作：

val includingFailuresDf = filterValueDF.select(
              from_avro($"fixedValue", currentValueSchema) as "avro_res")
             .withColumn("failed", $"avro_res".isNull)

val successDf = includingFailuresDf.where($"failed" === false)
val failedDf = includingFailuresDf.where($"failed" === true)

请注意，代码没有经过测试。

从spark行获取spark列

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