在我创建的一个主题中,我有如下数据,名为 "sampleTopic"
sid,Believer
其中第一个参数是 username
而第二个论点是 song name
用户经常收听的。现在,我已经开始 zookeeper
, Kafka server
和 producer
与上面提到的主题名称。我已经为该主题输入了上述数据,使用的是 CMD
. 现在,我想读取spark中的主题执行一些聚合,并将其写回流。下面是我的代码。
package com.sparkKafka
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
object SparkKafkaTopic {
def main(args: Array[String]) {
val spark = SparkSession.builder().appName("SparkKafka").master("local[*]").getOrCreate()
println("hey")
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "sampleTopic1")
.load()
val query = df.writeStream
.outputMode("append")
.format("console")
.start().awaitTermination()
}
}
但是,当我执行上面的代码时,它给出了..:
+----+--------------------+------------+---------+------+--------------------+-------------+
| key| value| topic|partition|offset| timestamp|timestampType|
+----+--------------------+------------+---------+------+--------------------+-------------+
|null|[73 69 64 64 68 6...|sampleTopic1| 0| 4|2020-05-31 12:12:...| 0|
+----+--------------------+------------+---------+------+--------------------+-------------+
与无限的下面的循环消息太
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
20/05/31 11:56:12 INFO Fetcher: [Consumer clientId=consumer-1, groupId=spark-kafka-source-0d6807b9-fcc9-4847-abeb-f0b81ab25187--264582860-driver-0] Resetting offset for partition sampleTopic1-0 to offset 4.
不知道这里到底出了什么问题 请指导我完成它。
尝试添加 spark-sql-kafka
库到你的构建文件。请检查以下内容。
build.sbt
libraryDependencies += "org.apache.spark" %% "spark-sql-kafka-0-10" % "2.3.0"
// Change to Your spark version
pom.xml
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_2.11</artifactId>
<version>2.3.0</version> // Change to Your spark version
</dependency>
更改您的代码,如下所示
package com.sparkKafka
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
case class KafkaMessage(key: String, value: String, topic: String, partition: Int, offset: Long, timestamp: String)
object SparkKafkaTopic {
def main(args: Array[String]) {
//val spark = SparkSession.builder().appName("SparkKafka").master("local[*]").getOrCreate()
println("hey")
val spark = SparkSession.builder().appName("SparkKafka").master("local[*]").getOrCreate()
import spark.implicits._
val mySchema = StructType(Array(
StructField("userName", StringType),
StructField("songName", StringType)))
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "sampleTopic1")
.load()
val query = df
.as[KafkaMessage]
.select(split($"value", ",")(0).as("userName"),split($"value", ",")(1).as("songName"))
.writeStream
.outputMode("append")
.format("console")
.start()
.awaitTermination()
}
}
/*
+------+--------+
|userid|songname|
+------+--------+
| sid|Believer|
+------+--------+
*/
}
}
spark-sql-kafka jar缺失,它有'kafka'数据源的实现。
你可以使用config选项添加这个jar,或者建立包含spark-sql-kafka jar的fat jar。请使用相关版本的jar
val spark = SparkSession.builder()
.appName("SparkKafka").master("local[*]")
.config("spark.jars","/path/to/spark-sql-kafka-xxxxxx.jar")
.getOrCreate()