I'm pushing a JSON file into a Kafka topic, connecting the topic in presto and structuring the JSON data into a queryable table.
The problem I am facing is that , presto is not to fetch data its shows error Cannot invoke "com.fasterxml.jackson.databind.JsonNode.has(String)" because "currentNode" is null.
Code for pushing data into kafka topic:
object Producer extends App{
val props = new Properties()
props.put("bootstrap.servers", "localhost:9092")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.connect.json.JsonSerializer")
val producer = new KafkaProducer[String,JsonNode](props)
println("inside prducer")
val mapper = (new ObjectMapper() with ScalaObjectMapper).
registerModule(DefaultScalaModule).
configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false).
findAndRegisterModules(). // register joda and java-time modules automatically
asInstanceOf[ObjectMapper with ScalaObjectMapper]
val filename = "/Users/rishunigam/Documents/devicd.json"
val jsonNode: JsonNode= mapper.readTree(new File(filename))
val s = jsonNode.size()
for(i <- 0 to jsonNode.size()) {
val js = jsonNode.get(i)
println(js)
val record = new ProducerRecord[String, JsonNode]( "tpch.devicelog", js)
println(record)
producer.send( record )
}
println("producer complete")
producer.close()
}
I want the timestamp at which the message was inserted in kafka topic by producer.
And at the kafka consumer side, i want to extract that timestamp.
class Producer {
def main(args: Array[String]): Unit = {
writeToKafka("quick-start")
}
def writeToKafka(topic: String): Unit = {
val props = new Properties()
props.put("bootstrap.servers", "localhost:9094")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
val producer = new KafkaProducer[String, String](props)
val record = new ProducerRecord[String, String](topic, "key", "value")
producer.send(record)
producer.close()
}
}
class Consumer {
def main(args: Array[String]): Unit = {
consumeFromKafka("quick-start")
}
def consumeFromKafka(topic: String) = {
val props = new Properties()
props.put("bootstrap.servers", "localhost:9094")
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
props.put("auto.offset.reset", "latest")
props.put("group.id", "consumer-group")
val consumer: KafkaConsumer[String, String] = new KafkaConsumer[String, String](props)
consumer.subscribe(util.Arrays.asList(topic))
while (true) {
val record = consumer.poll(1000).asScala
for (data <- record.iterator)
println(data.value())
}
}
}
Does kafka provides a way to do it? Else i will have to send an extra field from producer to topic.
Kafka provides a way since v0.10
From that version, all your messages have a timestamp information available in data.timestamp, and the kind of information inside is ruled by the config "message.timestamp.type" on your brokers. The value should be either CreateTime or LogAppendTime.
Before this version, you'll have to implement it by hand, usually through modifying your data structure.
I do not know why the data sent by producer do not reach the consumer.
I am working on cloudera virtual machine.
I am trying to write simple producer consumer where the producer uses Kafka and consumer uses spark streaming.
The Producer Code in scala:
import java.util.Properties
import org.apache.kafka.clients.producer._
object kafkaProducer {
def main(args: Array[String]) {
val props = new Properties()
props.put("bootstrap.servers", "localhost:9092")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
val producer = new KafkaProducer[String, String](props)
val TOPIC = "test"
for (i <- 1 to 50) {
Thread.sleep(1000) //every 1 second
val record = new ProducerRecord(TOPIC, generator.getID().toString(),generator.getRandomValue().toString())
producer.send(record)
}
producer.close()
}
}
The Consumer Code in scala :
import java.util
import org.apache.kafka.clients.consumer.KafkaConsumer
import scala.collection.JavaConverters._
import java.util.Properties
import kafka.producer._
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkConf
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._
object kafkaConsumer {
def main(args: Array[String]) {
var totalCount = 0L
val sparkConf = new SparkConf().setMaster("local[1]").setAppName("AnyName").set("spark.driver.host", "localhost")
val ssc = new StreamingContext(sparkConf, Seconds(2))
ssc.checkpoint("checkpoint")
val stream = KafkaUtils.createStream(ssc, "localhost:9092", "spark-streaming-consumer-group", Map("test" -> 1))
stream.foreachRDD((rdd: RDD[_], time: Time) => {
val count = rdd.count()
println("\n-------------------")
println("Time: " + time)
println("-------------------")
println("Received " + count + " events\n")
totalCount += count
})
ssc.start()
Thread.sleep(20 * 1000)
ssc.stop()
if (totalCount > 0) {
println("PASSED")
} else {
println("FAILED")
}
}
}
The problem is resolved by changing in the consumer code the line :
val stream = KafkaUtils.createStream(ssc, "localhost:9092", "spark-streaming-consumer-group", Map("test" -> 1))
the second parameter should be the zookeeper port which 2181 not 9092 and the zookeeper will manage to connect to the Kafka port 9092 automatically.
Note: Kafka should be started from terminal before running both the producer and consumer.
I am trying my hands on Kafka in Intellij using Spark & Scala. While creating producer Object I am unable to rectify the error. The code in Scala object is given below:
import java.util.Properties
import org.apache.kafka.clients.producer._
import kafka.producer.KeyedMessage
import org.apache.spark._
object kafkaProducer {
def main(args: Array[String]){
val topic = "jovis"
val props = new Properties()
props.put("metadata.broker.list", "localhost:9092")
props.put("serializer.class", "kafka.serializer.StringEncoder")
val config = new ProducerConfig(props)
//Error in Line below
val producer = new Producer[String, String](config)
val conf = new SparkConf().setAppName("Kafka").setMaster("local")
//val ssc = new StreamingContext(conf, Seconds(10))
val sc = new SparkContext(conf)
val data = sc.textFile("/home/hdadmin/empname.txt")
var i = 0
while(i <= data.count){
data.collect().foreach(x => {
println(x)
producer.send(new KeyedMessage[String, String](topic, x))
Thread.sleep(1000)
})
}
Error Log:
constructor ProducerConfig in class ProducerConfig cannot be accessed in object kafkaProducer
val config = new ProducerConfig(props)
Trait Producer is abstract;Cannot be instantiated.
val producer = new Producer[String, String](config)
I have imported the dependency jars below:
http://central.maven.org/maven2/org/apache/kafka/kafka-clients/0.8.2.0/kafka-clients-0.8.2.0.jar
http://central.maven.org/maven2/org/apache/kafka/kafka_2.11/0.10.2.1/kafka_2.11-0.10.2.1.jar
Apart from that I have started zookeeper server as well.
Where am I going wrong?
May be this will help you
what is the difference between kafka ProducerRecord and KeyedMessage
Please, try the new API "org.apache.kafka" %% "kafka" % "0.8.2.0"
import org.apache.kafka.clients.producer.ProducerRecord
import org.apache.kafka.clients.producer.KafkaProducer
val producer = new KafkaProducer[String, String](props)
producer.send(new ProducerRecord[String, String](topic, key, value)
I am using Spark Streaming to process data between two Kafka queues but I can not seem to find a good way to write on Kafka from Spark. I have tried this:
input.foreachRDD(rdd =>
rdd.foreachPartition(partition =>
partition.foreach {
case x: String => {
val props = new HashMap[String, Object]()
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers)
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
"org.apache.kafka.common.serialization.StringSerializer")
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
"org.apache.kafka.common.serialization.StringSerializer")
println(x)
val producer = new KafkaProducer[String, String](props)
val message = new ProducerRecord[String, String]("output", null, x)
producer.send(message)
}
}
)
)
and it works as intended but instancing a new KafkaProducer for every message is clearly unfeasible in a real context and I'm trying to work around it.
I would like to keep a reference to a single instance for every process and access it when I need to send a message. How can I write to Kafka from Spark Streaming?
Yes, unfortunately Spark (1.x, 2.x) doesn't make it straight-forward how to write to Kafka in an efficient manner.
I'd suggest the following approach:
Use (and re-use) one KafkaProducer instance per executor process/JVM.
Here's the high-level setup for this approach:
First, you must "wrap" Kafka's KafkaProducer because, as you mentioned, it is not serializable. Wrapping it allows you to "ship" it to the executors. The key idea here is to use a lazy val so that you delay instantiating the producer until its first use, which is effectively a workaround so that you don't need to worry about KafkaProducer not being serializable.
You "ship" the wrapped producer to each executor by using a broadcast variable.
Within your actual processing logic, you access the wrapped producer through the broadcast variable, and use it to write processing results back to Kafka.
The code snippets below work with Spark Streaming as of Spark 2.0.
Step 1: Wrapping KafkaProducer
import java.util.concurrent.Future
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord, RecordMetadata}
class MySparkKafkaProducer[K, V](createProducer: () => KafkaProducer[K, V]) extends Serializable {
/* This is the key idea that allows us to work around running into
NotSerializableExceptions. */
lazy val producer = createProducer()
def send(topic: String, key: K, value: V): Future[RecordMetadata] =
producer.send(new ProducerRecord[K, V](topic, key, value))
def send(topic: String, value: V): Future[RecordMetadata] =
producer.send(new ProducerRecord[K, V](topic, value))
}
object MySparkKafkaProducer {
import scala.collection.JavaConversions._
def apply[K, V](config: Map[String, Object]): MySparkKafkaProducer[K, V] = {
val createProducerFunc = () => {
val producer = new KafkaProducer[K, V](config)
sys.addShutdownHook {
// Ensure that, on executor JVM shutdown, the Kafka producer sends
// any buffered messages to Kafka before shutting down.
producer.close()
}
producer
}
new MySparkKafkaProducer(createProducerFunc)
}
def apply[K, V](config: java.util.Properties): MySparkKafkaProducer[K, V] = apply(config.toMap)
}
Step 2: Use a broadcast variable to give each executor its own wrapped KafkaProducer instance
import org.apache.kafka.clients.producer.ProducerConfig
val ssc: StreamingContext = {
val sparkConf = new SparkConf().setAppName("spark-streaming-kafka-example").setMaster("local[2]")
new StreamingContext(sparkConf, Seconds(1))
}
ssc.checkpoint("checkpoint-directory")
val kafkaProducer: Broadcast[MySparkKafkaProducer[Array[Byte], String]] = {
val kafkaProducerConfig = {
val p = new Properties()
p.setProperty("bootstrap.servers", "broker1:9092")
p.setProperty("key.serializer", classOf[ByteArraySerializer].getName)
p.setProperty("value.serializer", classOf[StringSerializer].getName)
p
}
ssc.sparkContext.broadcast(MySparkKafkaProducer[Array[Byte], String](kafkaProducerConfig))
}
Step 3: Write from Spark Streaming to Kafka, re-using the same wrapped KafkaProducer instance (for each executor)
import java.util.concurrent.Future
import org.apache.kafka.clients.producer.RecordMetadata
val stream: DStream[String] = ???
stream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
val metadata: Stream[Future[RecordMetadata]] = partitionOfRecords.map { record =>
kafkaProducer.value.send("my-output-topic", record)
}.toStream
metadata.foreach { metadata => metadata.get() }
}
}
Hope this helps.
My first advice would be to try to create a new instance in foreachPartition and measure if that is fast enough for your needs (instantiating heavy objects in foreachPartition is what the official documentation suggests).
Another option is to use an object pool as illustrated in this example:
https://github.com/miguno/kafka-storm-starter/blob/develop/src/main/scala/com/miguno/kafkastorm/kafka/PooledKafkaProducerAppFactory.scala
I however found it hard to implement when using checkpointing.
Another version that is working well for me is a factory as described in the following blog post, you just have to check if it provides enough parallelism for your needs (check the comments section):
http://allegro.tech/2015/08/spark-kafka-integration.html
With Spark >= 2.2
Both read and write operations are possible on Kafka using Structured Streaming API
Build stream from Kafka topic
// Subscribe to a topic and read messages from the earliest to latest offsets
val ds= spark
.readStream // use `read` for batch, like DataFrame
.format("kafka")
.option("kafka.bootstrap.servers", "brokerhost1:port1,brokerhost2:port2")
.option("subscribe", "source-topic1")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load()
Read the key and value and apply the schema for both, for simplicity we are making converting both of them to String type.
val dsStruc = ds.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
Since dsStruc have the schema, it accepts all SQL kind operations like filter, agg, select ..etc on it.
Write stream to Kafka topic
dsStruc
.writeStream // use `write` for batch, like DataFrame
.format("kafka")
.option("kafka.bootstrap.servers", "brokerhost1:port1,brokerhost2:port2")
.option("topic", "target-topic1")
.start()
More configuration for Kafka integration to read or write
Key artifacts to add in the application
"org.apache.spark" % "spark-core_2.11" % 2.2.0,
"org.apache.spark" % "spark-streaming_2.11" % 2.2.0,
"org.apache.spark" % "spark-sql-kafka-0-10_2.11" % 2.2.0,
There is a Streaming Kafka Writer maintained by Cloudera (actually spun off from a Spark JIRA [1]). It basically creates a producer per partition, which amortizes the time spent to create 'heavy' objects over a (hopefully large) collection of elements.
The Writer can be found here: https://github.com/cloudera/spark-kafka-writer
I was having the same issue and found this post.
The author solves the problem by creating 1 producer per executor. Instead of sending the producer itself, he sends only a “recipe” how to create a producer in an executor by broadcasting it.
val kafkaSink = sparkContext.broadcast(KafkaSink(conf))
He uses a wrapper that lazily creates the producer:
class KafkaSink(createProducer: () => KafkaProducer[String, String]) extends Serializable {
lazy val producer = createProducer()
def send(topic: String, value: String): Unit = producer.send(new ProducerRecord(topic, value))
}
object KafkaSink {
def apply(config: Map[String, Object]): KafkaSink = {
val f = () => {
val producer = new KafkaProducer[String, String](config)
sys.addShutdownHook {
producer.close()
}
producer
}
new KafkaSink(f)
}
}
The wrapper is serializable because the Kafka producer is initialized just before first use on an executor. The driver keeps the reference to the wrapper and the wrapper sends the messages using each executor's producer:
dstream.foreachRDD { rdd =>
rdd.foreach { message =>
kafkaSink.value.send("topicName", message)
}
}
Why is it infeasible? Fundamentally each partition of each RDD is going to run independently (and may well run on a different cluster node), so you have to redo the connection (and any synchronization) at the start of each partition's task. If the overhead of that is too high then you should increase the batch size in your StreamingContext until it becomes acceptable (obv. there's a latency cost to doing this).
(If you're not handling thousands of messages in each partition, are you sure you need spark-streaming at all? Would you do better with a standalone application?)
This might be what you want to do. You basically create one producer for each partition of records.
input.foreachRDD(rdd =>
rdd.foreachPartition(
partitionOfRecords =>
{
val props = new HashMap[String, Object]()
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers)
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
"org.apache.kafka.common.serialization.StringSerializer")
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
"org.apache.kafka.common.serialization.StringSerializer")
val producer = new KafkaProducer[String,String](props)
partitionOfRecords.foreach
{
case x:String=>{
println(x)
val message=new ProducerRecord[String, String]("output",null,x)
producer.send(message)
}
}
})
)
Hope that helps
With Spark < 2.2
Since there is no direct way of writing the messages to Kafka from Spark Streaming
Create a KafkaSinkWritter
import java.util.Properties
import org.apache.kafka.clients.producer._
import org.apache.spark.sql.ForeachWriter
class KafkaSink(topic:String, servers:String) extends ForeachWriter[(String, String)] {
val kafkaProperties = new Properties()
kafkaProperties.put("bootstrap.servers", servers)
kafkaProperties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
kafkaProperties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
val results = new scala.collection.mutable.HashMap[String, String]
var producer: KafkaProducer[String, String] = _
def open(partitionId: Long,version: Long): Boolean = {
producer = new KafkaProducer(kafkaProperties)
true
}
def process(value: (String, String)): Unit = {
producer.send(new ProducerRecord(topic, value._1 + ":" + value._2))
}
def close(errorOrNull: Throwable): Unit = {
producer.close()
}
}
Write messages using SinkWriter
val topic = "<topic2>"
val brokers = "<server:ip>"
val writer = new KafkaSink(topic, brokers)
val query =
streamingSelectDF
.writeStream
.foreach(writer)
.outputMode("update")
.trigger(ProcessingTime("25 seconds"))
.start()
Reference link