here is my code :
val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
val lines = stream.map(_._2)
lines.print()
lines.foreachRDD { rdd =>
rdd.foreach( data =>
if (data != null) {
println(data.toString)
val records = data.toString
CassandraConnector(conf).withSessionDo {
session =>
session.execute("INSERT INTO propatterns_test.meterreadings JSON "+records+";")
}
}
)
}
so where i am going wrong.
my error log is :
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2037)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:874)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:873)
at etc.
I am storing rdd message into cassandra.
Related
Want to put Avro messages from Kafka topics into Elasticsearch using Spark job (and SchemaRegistry with many defined schemas). I was able to read and deserialize records into Strings (json) format succesfully (with those 2 methods):
// Deserialize Avro to String
def avroToJsonString(record: GenericRecord): String = try {
val baos = new ByteArrayOutputStream
try {
val schema = record.getSchema
val jsonEncoder = EncoderFactory.get.jsonEncoder(schema, baos, false)
val avroWriter = new SpecificDatumWriter[GenericRecord](schema)
avroWriter.write(record, jsonEncoder)
jsonEncoder.flush()
baos.flush()
new String(baos.toByteArray)
} catch {
case ex: IOException =>
throw new IllegalStateException(ex)
} finally if (baos != null) baos.close()
}
// Parse JSON String
val parseJsonStream = (inStream: String) => {
try {
val parsed = Json.parse(inStream)
Option(parsed)
} catch {
case e: Exception => System.err.println("Exception while parsing JSON: " + inStream)
e.printStackTrace()
None
}
}
I'm reading record by record and I see deserialized JSON strings in debugger, everything looks fine, but for some reason I couldn't save them into Elasticsearch, because I guess RDD is needed to call saveToEs method. This is how I read avro records from Kafka:
val kafkaStream : InputDStream[ConsumerRecord[String, GenericRecord]] = KafkaUtils.createDirectStream[String, GenericRecord](ssc, PreferBrokers, Subscribe[String, GenericRecord](KAFKA_AVRO_TOPICS, kafkaParams))
val kafkaStreamParsed= kafkaStream.foreachRDD(rdd => {
rdd.foreach( x => {
val jsonString: String = avroToJsonString(x.value())
parseJsonStream(jsonString)
})
})
In case when I was reading json (not Avro) records, I was able to do it with:
EsSparkStreaming.saveToEs(kafkaStreamParsed, ELASTICSEARCH_EVENTS_INDEX + "/" + ELASTICSEARCH_TYPE)
I have an error in saveToEs method saying
Cannot resolve overloaded method 'saveToEs'
Tried to make rdd with sc.makeRDD() but had no luck either. How should I put all these records from batch job into RDD and afterward to Elasticsearch or I'm doing it all wrong?
UPDATE
Tried with solution:
val messages: DStream[Unit] = kafkaStream
.map(record => record.value)
.flatMap(record => {
val record1 = avroToJsonString(record)
JSON.parseFull(record1).map(rawMap => {
val map = rawMap.asInstanceOf[Map[String,String]]
})
})
again with the same Error (cannot resolve overloaded method)
UPDATE2
val kafkaStreamParsed: DStream[Any] = kafkaStream.map(rdd => {
val eventJSON = avroToJsonString(rdd.value())
parseJsonStream(eventJSON)
})
try {
EsSparkStreaming.saveToEs(kafkaStreamParsed, ELASTICSEARCH_EVENTS_INDEX + "/" + ELASTICSEARCH_TYPE)
} catch {
case e: Exception =>
EsSparkStreaming.saveToEs(kafkaStreamParsed, ELASTICSEARCH_FAILED_EVENTS)
e.printStackTrace()
}
Now I get the records in ES.
Using Spark 2.3.0 and Scala 2.11.8
I've managed to do it:
val kafkaStream : InputDStream[ConsumerRecord[String, GenericRecord]] = KafkaUtils.createDirectStream[String, GenericRecord](ssc, PreferBrokers, Subscribe[String, GenericRecord](KAFKA_AVRO_EVENT_TOPICS, kafkaParams))
val kafkaStreamParsed: DStream[Any] = kafkaStream.map(rdd => {
val eventJSON = avroToJsonString(rdd.value())
parseJsonStream(eventJSON)
})
try {
EsSparkStreaming.saveToEs(kafkaStreamParsed, ELASTICSEARCH_EVENTS_INDEX + "/" + ELASTICSEARCH_TYPE)
} catch {
case e: Exception =>
EsSparkStreaming.saveToEs(kafkaStreamParsed, ELASTICSEARCH_FAILED_EVENTS)
e.printStackTrace()
}
I have a Scala/Spark/Kafka process that I run. When I first start the process I create a KuduClient Object using a function I made that I share between classes. For this job I only create the KuduClient once, and let the process run continuously. I've noticed that after several days I frequently get exceptions.
I'm not really sure what to do. I think maybe an option would be to create a new Kudu client every day or so but I'm unsure of how to do that in this case as well.
import org.apache.spark.SparkConf
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.json.JSONObject
import org.apache.kudu.client.KuduClient
import org.apache.log4j.Logger
object Thing extends Serializable {
#transient lazy val client: KuduClient = createKuduClient(config)
#transient lazy val logger: Logger = Logger.getLogger(getClass.getName)
def main(args: Array[String]) {
UtilFunctions.loadConfig(args) //I send back a config object.
UtilFunctions.loadLogger() //factory method to load logger
val props: Map[String, String] = setKafkaProperties()
val topic = Set(config.getString("config.TOPIC_NAME"))
val conf = new SparkConf().setMaster("local[2]").setAppName(config.getString("config.SPARK_APP_NAME"))
val ssc = new StreamingContext(conf, Seconds(10))
ssc.sparkContext.setLogLevel("ERROR")
ssc.checkpoint(config.getString("config.SPARK_CHECKPOINT_NAME"))
// val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, props, topic)
val kafkaStream = KafkaUtils.createDirectStream[String, String](ssc, PreferConsistent, Subscribe[String, String](topic, props))
val distRecordsStream = kafkaStream.map(record => (record.key(), record.value()))
distRecordsStream.window(Seconds(10), Seconds(10))
distRecordsStream.foreachRDD(distRecords => {
logger.info(distRecords + " : " + distRecords.count())
distRecords.foreach(record => {
logger.info(record._2)
MyClass.DoSomethingWithThisData(new JSONObject(record._2), client)
})
})
ssc.start()
ssc.awaitTermination()
}
def createKuduClient(config: Config): KuduClient = {
var client: KuduClient = null
try{
client = new KuduClient.KuduClientBuilder(config.getString("config.KUDU_MASTER"))
.defaultAdminOperationTimeoutMs(config.getInt("config.KUDU_ADMIN_TIMEOUT_S") * 1000)
.defaultOperationTimeoutMs(config.getInt("config.KUDU_OPERATION_TIMEOUT_S") * 1000)
.build()
}
catch {
case e: Throwable =>
logger.error(e.getMessage)
logger.error(e.getStackTrace.toString)
Thread.sleep(10000) //try to create a new kudu client
client = createKuduClient(config)
}
client //return
}
def setKafkaProperties(): Map[String, String] = {
val zookeeper = config.getString("config.ZOOKEEPER")
val offsetReset = config.getString("config.OFFSET_RESET")
val brokers = config.getString("config.BROKERS")
val groupID = config.getString("config.GROUP_ID")
val deserializer = config.getString("config.DESERIALIZER")
val autoCommit = config.getString("config.AUTO_COMMIT")
val maxPollRecords = config.getString("config.MAX_POLL_RECORDS")
val maxPollIntervalms = config.getString("config.MAX_POLL_INTERVAL_MS")
val props = Map(
"bootstrap.servers" -> brokers,
"zookeeper.connect" -> zookeeper,
"group.id" -> groupID,
"key.deserializer" -> deserializer,
"value.deserializer" -> deserializer,
"enable.auto.commit" -> autoCommit,
"auto.offset.reset" -> offsetReset,
"max.poll.records" -> maxPollRecords,
"max.poll.interval.ms" -> maxPollIntervalms)
props
}
}
Exceptions below. I've removed the IP address inplace of using "x"
ERROR client.TabletClient: [Peer
master-ip-xxx-xx-xxx-40.ec2.internal:7051] Unexpected exception from
downstream on [id: 0x42ba3f4d, /xxx.xx.xxx.39:36820 =>
ip-xxx-xxx-xxx-40.ec2.internal/xxx.xx.xxx.40:7051]
java.lang.RuntimeException: Could not deserialize the response,
incompatible RPC? Error is: step
at org.apache.kudu.client.KuduRpc.readProtobuf(KuduRpc.java:383)
at org.apache.kudu.client.Negotiator.parseSaslMsgResponse(Negotiator.java:282)
at org.apache.kudu.client.Negotiator.handleResponse(Negotiator.java:235)
at org.apache.kudu.client.Negotiator.messageReceived(Negotiator.java:229)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeline.java:791)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.timeout.ReadTimeoutHandler.messageReceived(ReadTimeoutHandler.java:184)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeline.java:791)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:296)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.oneone.OneToOneDecoder.handleUpstream(OneToOneDecoder.java:70)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeline.java:791)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:296)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.unfoldAndFireMessageReceived(FrameDecoder.java:462)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.callDecode(FrameDecoder.java:443)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.messageReceived(FrameDecoder.java:310)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeline.java:791)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:296)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.unfoldAndFireMessageReceived(FrameDecoder.java:462)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.callDecode(FrameDecoder.java:443)
at org.apache.kudu.client.shaded.org.jboss.netty.handler.codec.frame.FrameDecoder.messageReceived(FrameDecoder.java:303)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:559)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:268)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:255)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.socket.nio.NioWorker.read(NioWorker.java:88)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.socket.nio.AbstractNioWorker.process(AbstractNioWorker.java:108)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.socket.nio.AbstractNioSelector.run(AbstractNioSelector.java:337)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.socket.nio.AbstractNioWorker.run(AbstractNioWorker.java:89)
at org.apache.kudu.client.shaded.org.jboss.netty.channel.socket.nio.NioWorker.run(NioWorker.java:178)
at org.apache.kudu.client.shaded.org.jboss.netty.util.ThreadRenamingRunnable.run(ThreadRenamingRunnable.java:108)
at org.apache.kudu.client.shaded.org.jboss.netty.util.internal.DeadLockProofWorker$1.run(DeadLockProofWorker.java:42)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
I've also seen exceptions like these after running for a while which others seem to attribute to being the open file handles limit of your user.
java.io.IOException: All datanodes
DatanodeInfoWithStorage[xxx.xx.xxx.36:1004,DS-55c403c3-203a-4dac-b383-72fcdb686185,DISK]
are bad. Aborting...
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1465)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:1236)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputSt
Is this have something too do with having too many open files? A way to "purge" these files once they reach a limit?
I have a question.There is a guide how to implement exactly-one，here is the code:
https://spark.apache.org/docs/latest/streaming-kafka-0-10-integration.html#storing-offsets
val stream = KafkaUtils.createDirectStream[String, String](
streamingContext,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams)
)
stream.map(record => (record.key, record.value))
//=====================================================
//separate line
//=====================================================
stream.foreachRDD { rdd =>
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
// some time later, after outputs have completed
stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
}
But what if I want to using the 'reduceByKeyAndWindow' int the separate line,just like this:
val stream = KafkaUtils.createDirectStream[String, String](
streamingContext,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams)
)
val lines: DStream[String] = stream.map(record => record.value)
lines.map(row => {
(row.split(",")(1), 1)
}).reduceByKeyAndWindow((a: Int, b: Int) => (a + b), Seconds(30), Seconds(5))
.foreachRDD(rdd => {
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
//mycode start
rdd.foreach(println)
//mycaode end
stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
})
I am trying this ,but I got a error:
Exception in thread "main" java.lang.ClassCastException: org.apache.spark.rdd.MapPartitionsRDD cannot be cast to org.apache.spark.streaming.kafka010.HasOffsetRanges
AnyHelp?Thanks in advance！
I want to insert streaming data into hbase;
this is my code :
val tableName = "streamingz"
val conf = HBaseConfiguration.create()
conf.addResource(new Path("file:///opt/cloudera/parcels/CDH-5.4.7-1.cdh5.4.7.p0.3/etc/hbase/conf.dist/hbase-site.xml"))
conf.set(TableInputFormat.INPUT_TABLE, tableName)
val admin = new HBaseAdmin(conf)
if (!admin.isTableAvailable(tableName)) {
print("-----------------------------------------------------------------------------------------------------------")
val tableDesc = new HTableDescriptor(tableName)
tableDesc.addFamily(new HColumnDescriptor("z1".getBytes()))
tableDesc.addFamily(new HColumnDescriptor("z2".getBytes()))
admin.createTable(tableDesc)
} else {
print("Table already exists!!--------------------------------------------------------------------------------------")
}
val ssc = new StreamingContext(sc, Seconds(10))
val topicSet = Set("fluxAstellia")
val kafkaParams = Map[String, String]("metadata.broker.list" - > "10.32.201.90:9092")
val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicSet)
val lines = stream.map(_._2).map(_.split(" ", -1)).foreachRDD(rdd => {
if (!rdd.partitions.isEmpty) {
val myTable = new HTable(conf, tableName)
rdd.map(rec => {
var put = new Put(rec._1.getBytes)
put.add("z1".getBytes(), "name".getBytes(), Bytes.toBytes(rec._2))
myTable.put(put)
}).saveAsNewAPIHadoopDataset(conf)
myTable.flushCommits()
} else {
println("rdd is empty")
}
})
ssc.start()
ssc.awaitTermination()
}
}
I got this error:
:66: error: value _1 is not a member of Array[String]
var put = new Put(rec._1.getBytes)
I'm beginner so how I can't fix this error, and I have a question:
where exactly create the table; outside the streaming process or inside ?
Thank you
You error is basically on line var put = new Put(rec._1.getBytes)
You can call _n only on a Map(_1 for key and _2 for value) or a Tuple.
rec is a String Array you got by splitting the string in the stream by space characters. If you were after first element, you'd write it as var put = new Put(rec(0).getBytes). Likewise in the next line you'd write it as put.add("z1".getBytes(), "name".getBytes(), Bytes.toBytes(rec(1)))
I'm using spark directStream api to read data from Kafka. My code as following please:
val sparkConf = new SparkConf().setAppName("testdirectStreaming")
val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(2))
val kafkaParams = Map[String, String](
"auto.offset.reset" -> "smallest",
"metadata.broker.list"->"10.0.0.11:9092",
"spark.streaming.kafka.maxRatePerPartition"->"100"
)
//I set all of the 3 partitions fromOffset are 0
var fromOffsets:Map[TopicAndPartition, Long] = Map(TopicAndPartition("mytopic",0) -> 0)
fromOffsets+=(TopicAndPartition("mytopic",1) -> 0)
fromOffsets+=(TopicAndPartition("mytopic",2) -> 0)
val kafkaData = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, MessageAndMetadata[String, String]](
ssc, kafkaParams, fromOffsets,(mmd: MessageAndMetadata[String, String]) => mmd)
var offsetRanges = Array[OffsetRange]()
kafkaData.transform { rdd =>
offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
rdd
}.map {
_.message()
}.foreachRDD { rdd =>
for (o <- offsetRanges) {
println(s"---${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
}
rdd.foreachPartition{ partitionOfRecords =>
partitionOfRecords.foreach { line =>
println("===============value:"+line)
}
}
}
I'm sure there are data in the kafka cluster, but my code could not get any of them. Thanks in advance.
I found the reason: The old messages in kafka have already been deleted since the retention period expired. So when I set the fromOffset is 0 it caused OutOfOffSet exception. The exception caused Spark reset the offset with the latest ones. Therefore I could not get any messages. The solution is that I need to set the appropriate fromOffset to avoid the Exception.