[英]Not able to execute multiple queries in Spark structured streaming
我創建了一個示例代碼來執行多個查詢。 但是我只得到第一個查詢的輸出。 在日志中,我能夠看到所有查詢都在運行。不確定我在做什么錯。
public class A extends D implements Serializable {
public Dataset<Row> getDataSet(SparkSession session) {
Dataset<Row> dfs = session.readStream().format("socket").option("host", hostname).option("port", port).load();
publish(dfs.toDF(), "reader");
return dfs;
}
}
public class B extends D implements Serializable {
public Dataset<Row> execute(Dataset<Row> ds) {
Dataset<Row> d = ds.select(functions.explode(functions.split(ds.col("value"), "\\s+")));
publish(d.toDF(), "component");
return d;
}
}
public class C extends D implements Serializable {
public Dataset<Row> execute(Dataset<Row> ds) {
publish(inputDataSet.toDF(), "console");
ds.writeStream().format("csv").option("path", "hdfs://hostname:9000/user/abc/data1/")
.option("checkpointLocation", "hdfs://hostname:9000/user/abc/cp").outputMode("append").start();
return ds;
}
}
public class D {
public void publish(Dataset<Row> dataset, String directory) {
dataset.writeStream().format("csv").option("path", "hdfs://hostname:9000/user/abc/" + directory)
.option("checkpointLocation", "hdfs://hostname:9000/user/abc/checkpoint/" + directory).outputMode("append")
.start();
}
}
public static void main(String[] args) {
SparkSession session = createSession();
try {
A a = new A();
Dataset<Row> records = a.getDataSet(session);
B b = new B();
Dataset<Row> ds = b.execute(records);
C c = new C();
c.execute(ds);
session.streams().awaitAnyTermination();
} catch (StreamingQueryException e) {
e.printStackTrace();
}
}
問題是由於您正在從中讀取輸入套接字。Spark套接字源打開了兩個與nc的連接(即,因為您有兩個啟動)。 nc的局限性是它只能將數據饋送到一個連接。對於其他輸入源,您的查詢應該可以正常運行。 查看相關問題: 在Spark結構化流中執行單獨的流查詢
嘗試了如下所示的簡單測試,並打印了兩個輸出:
val df1 = spark.readStream.format("socket").option("host","localhost").option("port",5430).load()
val df9 = spark.readStream.format("socket").option("host","localhost").option("port",5431).load()
val df2 = df1.as[String].flatMap(x=>x.split(","))
val df3 = df9.as[String].flatMap(x=>x.split(",")).select($"value".as("name"))
val sq1 = df3.writeStream.format("console").queryName("sq1")
.option("truncate","false").trigger(Trigger.ProcessingTime(10 second)).start()
val sq = df2.writeStream.format("console").queryName("sq")
.option("truncate","false").trigger(Trigger.ProcessingTime(20 second)).start()
spark.streams.awaitAnyTermination()
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