Question

我正在尝试将Kafka消息值分解为case类实例。（我将消息放在另一侧。）

此代码：


    import ss.implicits._
    import org.apache.spark.sql.functions._

    val enc: Encoder[TextRecord] = Encoders.product[TextRecord]
    ss.udf.register("deserialize", (bytes: Array[Byte]) => {
      DefSer.deserialize(bytes).asInstanceOf[TextRecord] }
    )

    val inputStream = ss.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", conf.getString("bootstrap.servers"))
      .option("subscribe", topic)
      .option("startingOffsets", "earliest")
      .load()

    inputStream.printSchema

    val records = inputStream
        .selectExpr(s"deserialize(value) AS record")

    records.printSchema

    val rec2 = records.as(enc)

    rec2.printSchema

产生以下输出：



root
 |-- key: binary (nullable = true)
 |-- value: binary (nullable = true)
 |-- topic: string (nullable = true)
 |-- partition: integer (nullable = true)
 |-- offset: long (nullable = true)
 |-- timestamp: timestamp (nullable = true)
 |-- timestampType: integer (nullable = true)

root
 |-- record: struct (nullable = true)
 |    |-- eventTime: timestamp (nullable = true)
 |    |-- lineLength: integer (nullable = false)
 |    |-- windDirection: float (nullable = false)
 |    |-- windSpeed: float (nullable = false)
 |    |-- gustSpeed: float (nullable = false)
 |    |-- waveHeight: float (nullable = false)
 |    |-- dominantWavePeriod: float (nullable = false)
 |    |-- averageWavePeriod: float (nullable = false)
 |    |-- mWaveDirection: float (nullable = false)
 |    |-- seaLevelPressure: float (nullable = false)
 |    |-- airTemp: float (nullable = false)
 |    |-- waterSurfaceTemp: float (nullable = false)
 |    |-- dewPointTemp: float (nullable = false)
 |    |-- visibility: float (nullable = false)
 |    |-- pressureTendency: float (nullable = false)
 |    |-- tide: float (nullable = false)

当我到达水槽



    val debugOut = rec2.writeStream
      .format("console")
      .option("truncate", "false")
      .start()

    debugOut.awaitTermination()

催化剂抱怨：



Caused by: org.apache.spark.sql.AnalysisException: cannot resolve '`eventTime`' given input columns: [record];
    at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)

我通过调用rec2.map(r=>r.getAs[TextRecord](0))，explode("record")等尝试了许多操作来“拉起TextRecord”，但碰到了ClassCastExceptions。

Answer 1

最简单的方法是使用import ss.implicits._ val inputStream = ss.readStream .format("kafka") .option("kafka.bootstrap.servers", conf.getString("bootstrap.servers")) .option("subscribe", topic) .option("startingOffsets", "earliest") .load() val records = inputStream.map(row => DefSer.deserialize(row.getAs[Array[Byte]]("value")).asInstanceOf[TextRecord] )函数将inputStream Row实例直接映射到TextRecord（假设它是案例类）

records

Dataset[TextRecord]将直接为{{1}}。

只要您导入SparkSession隐式对象，就不需要为case类提供编码器类，Scala会为您隐式地进行处理。

DataFrame到数据集的转换（标量）

1 个答案: