My requirement is to enrich data stream data with profile information from a HBase table. I was looking to use a broadcast variable. Enclosed the whole code here.
The output of HBase data is as follows
In the Driver node HBaseReaderBuilder
(org.apache.spark.SparkContext#3c58b102,hbase_customer_profile,Some(data),WrappedArray(gender, age),None,None,List()))
In the Worker node
HBaseReaderBuilder(null,hbase_customer_profile,Some(data),WrappedArray(gender, age),None,None,List()))
As you can see it has lost the spark context. When i issue the statement val
myRdd = bcdocRdd.map(r => Profile(r._1, r._2, r._3)) i get a NullPointerException
java.lang.NullPointerException
at it.nerdammer.spark.hbase.HBaseReaderBuilderConversions$class.toSimpleHBaseRDD(HBaseReaderBuilder.scala:83)
at it.nerdammer.spark.hbase.package$.toSimpleHBaseRDD(package.scala:5)
at it.nerdammer.spark.hbase.HBaseReaderBuilderConversions$class.toHBaseRDD(HBaseReaderBuilder.scala:67)
at it.nerdammer.spark.hbase.package$.toHBaseRDD(package.scala:5)
at testPartition$$anonfun$main$1$$anonfun$apply$1$$anonfun$apply$2.apply(testPartition.scala:34)
at testPartition$$anonfun$main$1$$anonfun$apply$1$$anonfun$apply$2.apply(testPartition.scala:33)
object testPartition {
def main(args: Array[String]) : Unit = {
val sparkMaster = "spark://x.x.x.x:7077"
val ipaddress = "x.x.x.x:2181" // Zookeeper
val hadoopHome = "/home/hadoop/software/hadoop-2.6.0"
val topicname = "new_events_test_topic"
val mainConf = new SparkConf().setMaster(sparkMaster).setAppName("testingPartition")
val mainSparkContext = new SparkContext(mainConf)
val ssc = new StreamingContext(mainSparkContext, Seconds(30))
val eventsStream = KafkaUtils.createStream(ssc,"x.x.x.x:2181","receive_rest_events",Map(topicname.toString -> 2))
val docRdd = mainSparkContext.hbaseTable[(String, Option[String], Option[String])]("hbase_customer_profile").select("gender","age").inColumnFamily("data")
println ("docRDD from Driver ",docRdd)
val broadcastedprof = mainSparkContext.broadcast(docRdd)
eventsStream.foreachRDD(dstream => {
dstream.foreachPartition(records => {
println("Broadcasted docRDD - in Worker ", broadcastedprof.value)
val bcdocRdd = broadcastedprof.value
records.foreach(record => {
//val myRdd = bcdocRdd.map(r => Profile(r._1, r._2, r._3))
//myRdd.foreach(println)
val Rows = record._2.split("\r\n")
})
})
})
ssc.start()
ssc.awaitTermination()
}
}
Related
I have following code :-
case class event(imei: String, date: String, gpsdt: String,dt: String,id: String)
case class historyevent(imei: String, date: String, gpsdt: String)
object kafkatesting {
def main(args: Array[String]) {
val clients = new RedisClientPool("192.168.0.40", 6379)
val conf = new SparkConf()
.setAppName("KafkaReceiver")
.set("spark.cassandra.connection.host", "192.168.0.40")
.set("spark.cassandra.connection.keep_alive_ms", "20000")
.set("spark.executor.memory", "3g")
.set("spark.driver.memory", "4g")
.set("spark.submit.deployMode", "cluster")
.set("spark.executor.instances", "4")
.set("spark.executor.cores", "3")
.set("spark.streaming.backpressure.enabled", "true")
.set("spark.streaming.backpressure.initialRate", "100")
.set("spark.streaming.kafka.maxRatePerPartition", "7")
val sc = SparkContext.getOrCreate(conf)
val ssc = new StreamingContext(sc, Seconds(10))
val sqlContext = new SQLContext(sc)
val kafkaParams = Map[String, String](
"bootstrap.servers" -> "192.168.0.113:9092",
"group.id" -> "test-group-aditya",
"auto.offset.reset" -> "largest")
val topics = Set("random")
val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
kafkaStream.foreachRDD { rdd =>
val updatedRDD = rdd.map(a =>
{
implicit val formats = DefaultFormats
val jValue = parse(a._2)
val fleetrecord = jValue.extract[historyevent]
val hash = fleetrecord.imei + fleetrecord.date + fleetrecord.gpsdt
val md5Hash = DigestUtils.md5Hex(hash).toUpperCase()
val now = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(Calendar.getInstance().getTime())
event(fleetrecord.imei, fleetrecord.date, fleetrecord.gpsdt, now, md5Hash)
})
.collect()
updatedRDD.foreach(f =>
{
clients.withClient {
client =>
{
val value = f.imei + " , " + f.gpsdt
val zscore = Calendar.getInstance().getTimeInMillis
val key = new SimpleDateFormat("yyyy-MM-dd").format(Calendar.getInstance().getTime())
val dt = new SimpleDateFormat("HH:mm:ss").format(Calendar.getInstance().getTime())
val q1 = "00:00:00"
val q2 = "06:00:00"
val q3 = "12:00:00"
val q4 = "18:00:00"
val quater = if (dt > q1 && dt < q2) {
System.out.println(dt + " lies in quarter 1");
" -> 1"
} else if (dt > q2 && dt < q3) {
System.out.println(dt + " lies in quarter 2");
" -> 2"
} else if (dt > q3 && dt < q4) {
System.out.println(dt + " lies in quarter 3");
" -> 3"
} else {
System.out.println(dt + " lies in quarter 4");
" -> 4"
}
client.zadd(key + quater, zscore, value)
println(f.toString())
}
}
})
val collection = sc.parallelize(updatedRDD)
collection.saveToCassandra("db", "table", SomeColumns("imei", "date", "gpsdt","dt","id"))
}
ssc.start()
ssc.awaitTermination()
}
}
I'm using this code to insert data from Kafka into Cassandra and Redis, but facing following issues:-
1) application creates a long queue of active batches while the previous batch is currently being processed. So, I want to have next batch only once the previous batch is finished executing.
2) I have four-node cluster which is processing each batch but it takes around 30-40 sec for executing 700 records.
Is my code is optimized or I need to work on my code for better performance?
Yes you can do all your stuff inside mapPartition. There are APIs from datastax that allow you to save the Dstream directly. Here is how you can do it for C*.
val partitionedDstream = kafkaStream.repartition(5) //change this value as per your data and spark cluster
//Now instead of iterating each RDD work on each partition.
val eventsStream: DStream[event] = partitionedDstream.mapPartitions(x => {
val lst = scala.collection.mutable.ListBuffer[event]()
while (x.hasNext) {
val a = x.next()
implicit val formats = DefaultFormats
val jValue = parse(a._2)
val fleetrecord = jValue.extract[historyevent]
val hash = fleetrecord.imei + fleetrecord.date + fleetrecord.gpsdt
val md5Hash = DigestUtils.md5Hex(hash).toUpperCase()
val now = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(Calendar.getInstance().getTime())
lst += event(fleetrecord.imei, fleetrecord.date, fleetrecord.gpsdt, now, md5Hash)
}
lst.toList.iterator
})
eventsStream.cache() //because you are using same Dstream for C* and Redis
//instead of collecting each RDD save whole Dstream at once
import com.datastax.spark.connector.streaming._
eventsStream.saveToCassandra("db", "table", SomeColumns("imei", "date", "gpsdt", "dt", "id"))
Also cassandra accepts timestamp as Long value, so you can also change some part of your code as below
val now = System.currentTimeMillis()
//also change your case class to take `Long` instead of `String`
case class event(imei: String, date: String, gpsdt: String, dt: Long, id: String)
Similarly you can change for Redis as well.
My architecture right now is AWS ELB writes log to S3 and it sends a message to SQS for further processing by Spark Streaming. It's working right now but my problem right now is it's taking a bit of time. I'm new to Spark and Scala so just want to make sure that I'm not doing something stupid.
val conf = new SparkConf()
.setAppName("SparrowOrc")
.set("spark.hadoop.fs.s3a.impl","org.apache.hadoop.fs.s3a.S3AFileSystem")
.set("spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version","2")
.set("spark.speculation","false")
val sc = new SparkContext(conf)
val streamContext = new StreamingContext(sc, Seconds(1))
val sqs = streamContext.receiverStream(new SQSReceiver("queue")
.at(Regions.US_EAST_1)
.withTimeout(5))
// Got 10 messages at a time
val s3Keys = sqs.map(messages => {
val sqsMsg: JsValue = Json.parse(messages)
val s3Key = "s3://" +
Json.stringify(sqsMsg("Records")(0)("s3")("bucket")("name")).replace("\"", "") + "/" +
Json.stringify(sqsMsg("Records")(0)("s3")("object")("key")).replace("\"", "")
s3Key
})
val rawLogs: DStream[String] = s3Keys.transform(keys => {
val fileKeys = keys.collect()
val files = fileKeys.map(f => {
sc.textFile(f)
})
sc.union(files)
})
val jsonRows = rawLogs.mapPartitions(partition => {
// Parsing raw log to json
val txfm = new LogLine2Json
val log = Logger.getLogger("parseLog")
partition.map(line => {
try{
txfm.parseLine(line)
}
catch {
case e: Throwable => {log.info(line); "";}
}
}).filter(line => line != "{}")
})
val sqlSession = SparkSession
.builder()
.getOrCreate()
// Write to S3
jsonRows.foreachRDD(r => {
val parsedFormat = new SimpleDateFormat("yyyy-MM-dd/")
val parsedDate = parsedFormat.format(new java.util.Date())
val outputPath = "bucket" + parsedDate
val jsonDf = sqlSession.read.schema(sparrowSchema.schema).json(r)
jsonDf.write.mode("append").format("orc").option("compression","zlib").save(outputPath)
})
streamContext.start()
streamContext.awaitTermination()
}
Here's the DAG and it seems like everything is merged in union transformation.
I write a code in which twitter streaming take a rdd of tweet class and store each rdd in database but it got error task not serialize I paste the code.
sparkstreaming.scala
case class Tweet(id: Long, source: String, content: String, retweet: Boolean, authName: String, username: String, url: String, authId: Long, language: String)
trait SparkStreaming extends Connector {
def startStream(appName: String, master: String): StreamingContext = {
val db = connector("localhost", "rmongo", "rmongo", "pass")
val dbcrud = new DBCrud(db, "table1")
val sparkConf: SparkConf = new SparkConf().setAppName(appName).setMaster(master).set(" spark.driver.allowMultipleContexts", "true").set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
// .set("spark.kryo.registrator", "HelloKryoRegistrator")
// sparkConf.registerKryoClasses(Array(classOf[DBCrud]))
val sc: SparkContext = new SparkContext(sparkConf)
val ssc: StreamingContext = new StreamingContext(sc, Seconds(10))
ssc
}
}
object SparkStreaming extends SparkStreaming
I use this streaming context in plat controller to store tweets in database but it throws exception. I'm using mongodb to store it.
def streamstart = Action {
val stream = SparkStreaming
val a = stream.startStream("ss", "local[2]")
val db = connector("localhost", "rmongo", "rmongo", "pass")
val dbcrud = DBCrud
val twitterauth = new TwitterClient().tweetCredantials()
val tweetDstream = TwitterUtils.createStream(a, Option(twitterauth.getAuthorization))
val tweets = tweetDstream.filter { x => x.getUser.getLang == "en" }.map { x => Tweet(x.getId, x.getSource, x.getText, x.isRetweet(), x.getUser.getName, x.getUser.getScreenName, x.getUser.getURL, x.getUser.getId, x.getUser.getLang) }
// tweets.foreachRDD { x => x.foreach { x => dbcrud.insert(x) } }
tweets.saveAsTextFiles("/home/knoldus/sentiment project/spark services/tweets/tweets")
// val s=new BirdTweet()
// s.hastag(a.sparkContext)
a.start()
Ok("start streaming")
}
When make a single of streaming which take tweets and use forEachRDD to store each tweet then it works but if I use it from outside it doesn't work.
Please help me.
Try to create connection with MongoDB inside foreachRDD block, as mentioned in Spark Documentation
tweets.foreachRDD { x =>
x.foreach { x =>
val db = connector("localhost", "rmongo", "rmongo", "pass")
val dbcrud = new DBCrud(db, "table1")
dbcrud.insert(x)
}
}
I am able to read the messages from Kafka using the below code:
val ssc = new StreamingContext(sc, Seconds(50))
val topicmap = Map("test" -> 1)
val lines = KafkaUtils.createStream(ssc,"127.0.0.1:2181", "test-consumer-group",topicmap)
But, I am trying to read each message from Kafka and putting into HBase. This is my code to write into HBase but no success.
lines.foreachRDD(rdd => {
rdd.foreach(record => {
val i = +1
val hConf = new HBaseConfiguration()
val hTable = new HTable(hConf, "test")
val thePut = new Put(Bytes.toBytes(i))
thePut.add(Bytes.toBytes("cf"), Bytes.toBytes("a"), Bytes.toBytes(record))
})
})
Well, you are not actually executing the Put, you are mereley creating a Put request and adding data to it. What you are missing is an
hTable.put(thePut);
Adding other answer!!
You can use foreachPartition to establish connection at executor level to be more efficient instead of each row which is costly operation.
lines.foreachRDD(rdd => {
rdd.foreachPartition(iter => {
val hConf = new HBaseConfiguration()
val hTable = new HTable(hConf, "test")
iter.foreach(record => {
val i = +1
val thePut = new Put(Bytes.toBytes(i))
thePut.add(Bytes.toBytes("cf"), Bytes.toBytes("a"), Bytes.toBytes(record))
//missing part in your code
hTable.put(thePut);
})
})
})
I am trying to read records from Kafka message and put into Hbase. Though the scala script is running with out any issue, the inserts are not happening. Please help me.
Input:
rowkey1,1
rowkey2,2
Here is the code which I am using:
object Blaher {
def blah(row: Array[String]) {
val hConf = new HBaseConfiguration()
val hTable = new HTable(hConf, "test")
val thePut = new Put(Bytes.toBytes(row(0)))
thePut.add(Bytes.toBytes("cf"), Bytes.toBytes("a"), Bytes.toBytes(row(1)))
hTable.put(thePut)
}
}
object TheMain extends Serializable{
def run() {
val ssc = new StreamingContext(sc, Seconds(1))
val topicmap = Map("test" -> 1)
val lines = KafkaUtils.createStream(ssc,"127.0.0.1:2181", "test-consumer-group",topicmap).map(_._2)
val words = lines.map(line => line.split(",")).map(line => (line(0),line(1)))
val store = words.foreachRDD(rdd => rdd.foreach(Blaher.blah))
ssc.start()
}
}
TheMain.run()
From the API doc for HTable's flushCommits() method: "Executes all the buffered Put operations". You should call this at the end of your blah() method -- it looks like they're currently being buffered but never executed or executed at some random time.