got it.Thank you
--------------------------------
Thanks&Best regards!
罗辉 San.Luo
----- 原始邮件 -----
发件人：Saisai Shao <sai.sai.s...@gmail.com>
收件人：罗辉 <luohui20...@sina.com>
抄送人：user <user@spark.apache.org>
主题：Re: 回复：Re: Re: How SparkStreaming output messages to Kafka?
日期：2015年03月30日 17点05分
This warning is not related to "--from-beginning". It means there's no new data
for current partition in current batch duration, it is acceptable. If you
pushing the data into Kafka again, this warning log will be disappeared.
Thanks
Saisai
2015-03-30 16:58 GMT+08:00 <luohui20...@sina.com>:
BTW, what's the matter about below warning？ Not quite clear about KafkaRDD
WARN KafkaRDD: Beginning offset ${part.fromOffset} is the same as ending offset
skipping topic1 0.
does this warning occurs relative with my starting consumer without
"--from-beginning" param?
--------------------------------
Thanks&Best regards!
罗辉 San.Luo
----- 原始邮件 -----
发件人：<luohui20...@sina.com>
收件人："Saisai Shao" <sai.sai.s...@gmail.com>
抄送人："user" <user@spark.apache.org>
主题：回复：Re: Re: How SparkStreaming output messages to Kafka?
日期：2015年03月30日 16点46分
Hi Saisai,
following your advice, i modified my code like below, using Singleton:
import kafka.serializer.StringDecoder
import java.util.Properties
import kafka.producer._
object MyKafkaProducerSingleton {
val Array(brokers2, topic2) = Array("localhost:9092", "topic2")
val props = new Properties()
props.put("metadata.broker.list", brokers2)
props.put("serializer.class", "kafka.serializer.StringEncoder")
val config = new ProducerConfig(props)
val myproducer = new Producer[String, String](config)
def send(messages: KeyedMessage[String,String]){
myproducer.send(messages)
}
}
object SparkStreamingSampleDirectApproach2 {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val Array(brokers, topics) = Array("localhost:9092", "topic1")
val conf = new
SparkConf().setMaster("local[2]").setAppName("SparkStreamingSampleDirectApproach").set("log4j.rootCategory",
"WARN, console")
val ssc = new StreamingContext(conf, Seconds(1))
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder,
StringDecoder](ssc, kafkaParams, topicsSet)
// messages.saveAsTextFiles("hdfs://localhost:8020/spark/data", "test")
// val lines = messages.map(_._2)
// val words = lines.flatMap(_.split(" "))
// val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
// wordCounts.print()
// val Array(brokers2, topic2) = Array("localhost:9092", "topic2")
// val props = new Properties()
// props.put("metadata.broker.list", brokers2)
// props.put("serializer.class", "kafka.serializer.StringEncoder")
/* val messages2 = messages.foreachRDD { rdd =>
rdd.foreachPartition { record =>
val config = new ProducerConfig(props)
val producer = new Producer[String, String](config)
record.foreach { piece =>
val msg = new KeyedMessage[String, String](topic2, piece._2)
producer.send(msg)
}
}
}
*/
val messages2 = messages.foreachRDD { rdd =>
rdd.foreachPartition { record =>
record.foreach { piece =>
val msg = new KeyedMessage[String, String]("topic2", piece._2)
MyKafkaProducerSingleton.send(msg)
}
}
}
ssc.start()
ssc.awaitTermination()
}
}
--------------------------------
Thanks&Best regards!
罗辉 San.Luo
----- 原始邮件 -----
发件人：Saisai Shao <sai.sai.s...@gmail.com>
收件人：罗辉 <luohui20...@sina.com>
抄送人：user <user@spark.apache.org>
主题：Re: Re: How SparkStreaming output messages to Kafka?
日期：2015年03月30日 15点17分
Also you could use Producer singletion to improve the performance, since now
you have to create a Producer for each partition in each batch duration, you
could create a singleton object and reuse it (Producer is tread safe as I
know).
-Jerry
2015-03-30 15:13 GMT+08:00 Saisai Shao <sai.sai.s...@gmail.com>:
Yeah, after review again about your code, the reason why you cannot receive any
data is that your previous code lacks ACTION function of DStream, so the code
actually doesn't execute, after you changing to the style as I mentioned,
`foreachRDD` will trigger and run the jobs as you wrote.
Yes, your understanding is correct.
Thanks
Jerry
2015-03-30 14:58 GMT+08:00 <luohui20...@sina.com>:
To Saisai:
it works after I correct some of them with your advices like below:
Furthermore, I am not quite clear about which code running on driver
and which code running on executor, so i wrote my understanding in comment.
would you help check? Thank you.
To akhil:
yes, kafka has enough messages.I tested it with kafka producer sending
scala Random Int ，it also works. thanks.
object SparkStreamingSampleDirectApproach2 {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val Array(brokers, topics) = Array("localhost:9092", "topic1")
val conf = new
SparkConf().setMaster("local[2]").setAppName("SparkStreamingSampleDirectApproach").set("log4j.rootCategory",
"WARN, console")
val ssc = new StreamingContext(conf, Seconds(1))
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder,
StringDecoder](ssc, kafkaParams, topicsSet)
// messages.saveAsTextFiles("hdfs://localhost:8020/spark/data", "test")
// val lines = messages.map(_._2)
// val words = lines.flatMap(_.split(" "))
// val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
// wordCounts.print()
val Array(brokers2, topic2) = Array("localhost:9092", "topic2")
val props = new Properties()
props.put("metadata.broker.list", brokers2)
props.put("serializer.class", "kafka.serializer.StringEncoder")
val messages2 = messages.foreachRDD { rdd =>
//above code running on driver
rdd.foreachPartition { record =>
//code from here running on executor
val config = new ProducerConfig(props)
val producer = new Producer[String, String](config)
record.foreach { piece =>
val msg = new KeyedMessage[String, String](topic2, piece._2)
producer.send(msg)
}
}
}
// val messages2 = new KeyedMessage[String,
String](topic2,messages.toString())
// println(messages2)
ssc.start()
//again, running on driver
ssc.awaitTermination()
}
}
--------------------------------
Thanks&Best regards!
罗辉 San.Luo
----- 原始邮件 -----
发件人：Saisai Shao <sai.sai.s...@gmail.com>
收件人：luohui20...@sina.com
抄送人：user <user@spark.apache.org>
主题：Re: How SparkStreaming output messages to Kafka?
日期：2015年03月30日 14点03分
Hi Hui,
Did you try the direct Kafka stream example under Spark Streaming's examples?
Does it still fail to receive the message? Also would you please check all the
setups including Kafka, test with Kafka console consumer to see if Kafka is OK.
Besides seeing from your code, there's some problems in your code, here:
val messages2 = new KeyedMessage[String,
String](topic2,messages.toString()) println(messages2)
producer.send(messages2)
This code snippets are not lazily evaluated, this will be executed ONLY ONCE
when running to here, so actually you may not write the data into the Kafka,
you need to write like this:
messages.foreachRDD { r =>
r.foreachPartition{ iter =>
// create Producer
// change this partition of data (iter) into keyedMessage and write
into Kafka.
}
}
This is the basic style, sorry for any missing parts and typos, also pay a
attention to serialization issue when you need to create executors on remote
side. Please take a try again.
Thanks
Jerry
2015-03-30 13:34 GMT+08:00 <luohui20...@sina.com>:
Hi guys,
I am using SparkStreaming to receive message from kafka,process it
and then send back to kafka. however ,kafka consumer can not receive any
messages. Any one share ideas?
here is my code:
object SparkStreamingSampleDirectApproach {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val Array(brokers, topics) = Array("localhost:9092", "topic1")
val conf = new
SparkConf().setMaster("local[2]").setAppName("SparkStreamingSampleDirectApproach").set("log4j.rootCategory",
"WARN, console")
val ssc = new StreamingContext(conf, Seconds(1))
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder,
StringDecoder](ssc, kafkaParams, topicsSet)
// messages.saveAsTextFiles("hdfs://localhost:8020/spark/data", "test")
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
wordCounts.print()
val Array(brokers2, topic2) = Array("localhost:9092", "topic2")
val props = new Properties()
props.put("metadata.broker.list", brokers)
props.put("serializer.class", "kafka.serializer.StringEncoder")
val config = new ProducerConfig(props)
val producer = new Producer[String, String](config)
// val messages2 = messages.map{line =>
// new KeyedMessage[String, String](topic2,wordCounts.toString())
// }.toArray
val messages2 = new KeyedMessage[String, String](topic2,messages.toString())
println(messages2)
producer.send(messages2)
ssc.start()
ssc.awaitTermination()
}
}
--------------------------------
Thanks&Best regards!
罗辉 San.Luo
