scala – 如何加入2个spark sql流

2023年7月24日 198次阅读

ENV：

Scala spark版本：2.1.1

这是我的溪流(从卡夫卡读取)：

val conf = new SparkConf()
  .setMaster("local[1]")
  .setAppName("JoinStreams")

val spark = SparkSession.builder().config(conf).getOrCreate()

import spark.implicits._

val schema = StructType(
  List(
    StructField("t", DataTypes.StringType),
    StructField("dst", DataTypes.StringType),
    StructField("dstPort", DataTypes.IntegerType),
    StructField("src", DataTypes.StringType),
    StructField("srcPort", DataTypes.IntegerType),
    StructField("ts", DataTypes.LongType),
    StructField("len", DataTypes.IntegerType),
    StructField("cpu", DataTypes.DoubleType),
    StructField("l", DataTypes.StringType),
    StructField("headers", DataTypes.createArrayType(DataTypes.StringType))
  )
)
val baseDataFrame = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host:port")
  .option("subscribe", 'topic')
  .load()
  .selectExpr("cast (value as string) as json")
  .select(from_json($"json", schema).as("data"))
  .select($"data.*")

val requestsDataFrame = baseDataFrame
  .filter("t = 'REQUEST'")
  .repartition($"dst")
  .withColumn("rowId", monotonically_increasing_id())

val responseDataFrame = baseDataFrame
  .filter("t = 'RESPONSE'")
  .repartition($"src")
  .withColumn("rowId", monotonically_increasing_id())

responseDataFrame.createOrReplaceTempView("responses")
requestsDataFrame.createOrReplaceTempView("requests")


val dataFrame = spark.sql("select * from requests left join responses ON requests.rowId = responses.rowId")

启动应用程序时出现此错误：

org.apache.spark.sql.AnalysisException: Left outer/semi/anti joins with a streaming DataFrame/Dataset on the right is not supported;;

我如何加入这两个流？
我也尝试直接连接并得到相同的错误.
我应该先将它保存到文件然后再读一遍吗？
什么是最佳做法？

最佳答案 看来你需要Spark 2.3：

“在Spark 2.3中,我们增加了对流 – 流连接的支持……”

https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins

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