I have a spark application, that output result to redis.
It works fine on local mode, but cannot connect the redisHost with the args(0) that I assign like 10.242.10.100 on yarn-cluster mode.
The redisHost is unchanged 127.0.0.1.
object TestSparkClosure {
val logger: Logger = LoggerFactory.getLogger(TestSparkClosure.getClass)
var redisHost = "127.0.0.1"
var redisPort = 6379
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("TestSparkClosure")
if (args.length > 0) {
redisHost = args(0)
} else {
conf.setMaster("local")
}
val sparkContext = new SparkContext(conf)
var rdd = getRdd(sparkContext)
rdd.foreachPartition(partitionOfRecords => {
logger.info("host:port:" + redisHost + ":" + redisPort.toString)
val jedis = new Jedis(redisHost, redisPort)
partitionOfRecords.foreach(pair => {
val keystr = pair._1
val valuestr = pair._2
jedis.set(keystr, valuestr)
})
})
}
def getRdd(spark: SparkContext): RDD[(String, String)] = {
val rdd = spark.parallelize(List("2017\t1", "2018\t2", "2017\t3", "2018\t4", "2017\t5", "2018\t6")).map(line => {
val cols = line.split("\t")
(cols(0), cols(1))
})
rdd.reduceByKey((x, y) => {
((x.toInt + y.toInt).toString)
}, 3)
}
}
When I replace redisHost with local variable like this, It works fine again.
var localRedisHost = redisHost
rdd.foreachPartition(partitionOfRecords => {
logger.info("host:port:" + localRedisHost + ":" + redisPort.toString)
val jedis = new Jedis(localRedisHost , redisPort)
partitionOfRecords.foreach(pair => {
val keystr = pair._1
val valuestr = pair._2
jedis.set(keystr, valuestr)
})
})
Can anyone explain how the spark closure work here?
Thanks so much.
Its because you are using a variable which isnt able to use serialization. when you define a local element it can so you are able to use it inside of the RDD.
Related
i am getting error at this line
val Array(outputDirectory, Utils.IntParam(numTweetsToCollect), Utils.IntParam(intervalSecs), Utils.IntParam(partitionsEachInterval)) =
Utils.parseCommandLineWithTwitterCredentials(args)
recursive value x$7 needs
type
recursive value x$1 needs
type
what does this error meaning, please guide me how to resolve this error.
object Collect {
private var numTweetsCollected = 0L
private var partNum = 0
private var gson = new Gson()
def main(args: Array[String]) {
// Process program arguments and set properties
if (args.length < 3) {
System.err.println("Usage: " + this.getClass.getSimpleName +
"<outputDirectory> <numTweetsToCollect> <intervalInSeconds> <partitionsEachInterval>")
System.exit(1)
}
val Array(outputDirectory, Utils.IntParam(numTweetsToCollect), Utils.IntParam(intervalSecs), Utils.IntParam(partitionsEachInterval)) =
Utils.parseCommandLineWithTwitterCredentials(args)
val outputDir = new File(outputDirectory.toString)
if (outputDir.exists()) {
System.err.println("ERROR - %s already exists: delete or specify another directory".format(
outputDirectory))
System.exit(1)
}
outputDir.mkdirs()
println("Initializing Streaming Spark Context...")
val conf = new SparkConf().setAppName(this.getClass.getSimpleName)
val sc = new SparkContext(conf)
val ssc = new StreamingContext(sc, Seconds(intervalSecs))
val tweetStream = TwitterUtils.createStream(ssc, Utils.getAuth)
.map(gson.toJson(_))
tweetStream.foreachRDD((rdd, time) => {
val count = rdd.count()
if (count > 0) {
val outputRDD = rdd.repartition(partitionsEachInterval)
outputRDD.saveAsTextFile(outputDirectory + "/tweets_" + time.milliseconds.toString)
numTweetsCollected += count
if (numTweetsCollected > numTweetsToCollect) {
System.exit(0)
}
}
})
ssc.start()
ssc.awaitTermination()
}
}
Try removing the Utils.IntParam(.. from your pattern matched values. Extract the values, then parse them separately.
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.
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()
}
}
I'm using the new mapWithState function in spark streaming (1.6) with a timing out state. I want to use the timing out state and add it to another rdd in order to use it for calculations further down the road:
val aggedlogs = sc.emptyRDD[MyLog];
val mappingFunc = (key: String, newlog: Option[MyLog], state: State[MyLog]) => {
val _newLog = newlog.getOrElse(null)
if ((state.exists())&&(_newLog!=null))
{
val stateLog = state.get()
val combinedLog = LogUtil.CombineLogs(_newLog, stateLog);
state.update(combinedLog)
}
else if (_newLog !=null) {
state.update(_newLog);
}
if (state.isTimingOut())
{
val stateLog = state.get();
aggedlogs.union(sc.parallelize(List(stateLog), 1))
}
val stateLog = state.get();
(key,stateLog);
}
val stateDstream = reducedlogs.mapWithState(StateSpec.function(mappingFunc).timeout(Seconds(10)))
but when I try to add it to an rdd in the StateSpec function, I get an error that the function is not serializable. Any thoughts on how I can get pass this?
EDIT:
After drilling deeper i found that my approach was wrong. before trying this solution i tried to get the timing out logs from the statesnapeshot(), but they were not there anymore, changing the mapping function to :
def mappingFunc(key: String, newlog: Option[MyLog], state: State[KomoonaLog]) : Option[(String, MyLog)] = {
val _newLog = newlog.getOrElse(null)
if ((state.exists())&&(_newLog!=null))
{
val stateLog = state.get()
val combinedLog = LogUtil.CombineLogs(_newLog, stateLog);
state.update(combinedLog)
Some(key,combinedLog);
}
else if (_newLog !=null) {
state.update(_newLog);
Some(key,_newLog);
}
if (state.isTimingOut())
{
val stateLog = state.get();
stateLog.timinigOut = true;
System.out.println("timinigOut : " +key );
Some(key, stateLog);
}
val stateLog = state.get();
Some(key,stateLog);
}
i managed to filter the mapedwithstatedstream for the logs that are timing out in each batch:
val stateDstream = reducedlogs.mapWithState(
StateSpec.function(mappingFunc _).timeout(Seconds(60)))
val tiningoutlogs= stateDstream.filter (filtertimingout)
I was trying to code some utilities to bulk load data through HFiles from Spark RDDs.
I was taking the pattern of CSVBulkLoadTool from phoenix. I managed to generate some HFiles and load them into HBase, but i can't see the rows using sqlline(e.g using hbase shell it is possible). I would be more than grateful for any suggestions.
BulkPhoenixLoader.scala:
class BulkPhoenixLoader[A <: ImmutableBytesWritable : ClassTag, T <: KeyValue : ClassTag](rdd: RDD[(A, T)]) {
def createConf(tableName: String, inConf: Option[Configuration] = None): Configuration = {
val conf = inConf.map(HBaseConfiguration.create).getOrElse(HBaseConfiguration.create())
val job: Job = Job.getInstance(conf, "Phoenix bulk load")
job.setMapOutputKeyClass(classOf[ImmutableBytesWritable])
job.setMapOutputValueClass(classOf[KeyValue])
// initialize credentials to possibily run in a secure env
TableMapReduceUtil.initCredentials(job)
val htable: HTable = new HTable(conf, tableName)
// Auto configure partitioner and reducer according to the Main Data table
HFileOutputFormat2.configureIncrementalLoad(job, htable)
conf
}
def bulkSave(tableName: String, outputPath: String, conf:
Option[Configuration]) = {
val configuration: Configuration = createConf(tableName, conf)
rdd.saveAsNewAPIHadoopFile(
outputPath,
classOf[ImmutableBytesWritable],
classOf[Put],
classOf[HFileOutputFormat2],
configuration)
}
}
ExtendedProductRDDFunctions.scala:
class ExtendedProductRDDFunctions[A <: scala.Product](data: org.apache.spark.rdd.RDD[A]) extends
ProductRDDFunctions[A](data) with Serializable {
def toHFile(tableName: String,
columns: Seq[String],
conf: Configuration = new Configuration,
zkUrl: Option[String] =
None): RDD[(ImmutableBytesWritable, KeyValue)] = {
val config = ConfigurationUtil.getOutputConfiguration(tableName, columns, zkUrl, Some(conf))
val tableBytes = Bytes.toBytes(tableName)
val encodedColumns = ConfigurationUtil.encodeColumns(config)
val jdbcUrl = zkUrl.map(getJdbcUrl).getOrElse(getJdbcUrl(config))
val conn = DriverManager.getConnection(jdbcUrl)
val query = QueryUtil.constructUpsertStatement(tableName,
columns.toList.asJava,
null)
data.flatMap(x => mapRow(x, jdbcUrl, encodedColumns, tableBytes, query))
}
def mapRow(product: Product,
jdbcUrl: String,
encodedColumns: String,
tableBytes: Array[Byte],
query: String): List[(ImmutableBytesWritable, KeyValue)] = {
val conn = DriverManager.getConnection(jdbcUrl)
val preparedStatement = conn.prepareStatement(query)
val columnsInfo = ConfigurationUtil.decodeColumns(encodedColumns)
columnsInfo.zip(product.productIterator.toList).zipWithIndex.foreach(setInStatement(preparedStatement))
preparedStatement.execute()
val uncommittedDataIterator = PhoenixRuntime.getUncommittedDataIterator(conn, true)
val hRows = uncommittedDataIterator.asScala.filter(kvPair =>
Bytes.compareTo(tableBytes, kvPair.getFirst) == 0
).flatMap(kvPair => kvPair.getSecond.asScala.map(
kv => {
val byteArray = kv.getRowArray.slice(kv.getRowOffset, kv.getRowOffset + kv.getRowLength - 1) :+ 1.toByte
(new ImmutableBytesWritable(byteArray, 0, kv.getRowLength), kv)
}))
conn.rollback()
conn.close()
hRows.toList
}
def setInStatement(statement: PreparedStatement): (((ColumnInfo, Any), Int)) => Unit = {
case ((c, v), i) =>
if (v != null) {
// Both Java and Joda dates used to work in 4.2.3, but now they must be java.sql.Date
val (finalObj, finalType) = v match {
case dt: DateTime => (new Date(dt.getMillis), PDate.INSTANCE.getSqlType)
case d: util.Date => (new Date(d.getTime), PDate.INSTANCE.getSqlType)
case _ => (v, c.getSqlType)
}
statement.setObject(i + 1, finalObj, finalType)
} else {
statement.setNull(i + 1, c.getSqlType)
}
}
private def getIndexTables(conn: Connection, qualifiedTableName: String) : List[(String, String)]
= {
val table: PTable = PhoenixRuntime.getTable(conn, qualifiedTableName)
val tables = table.getIndexes.asScala.map(x => x.getIndexType match {
case IndexType.LOCAL => (x.getTableName.getString, MetaDataUtil.getLocalIndexTableName(qualifiedTableName))
case _ => (x.getTableName.getString, x.getTableName.getString)
}).toList
tables
}
}
The generated HFiles I load with the utility tool from hbase as follows:
hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles path/to/hfile tableName
You could just convert your csv file to an RDD of Product and use the .saveToPhoenix method. This is generally how I load csv data into phoenix.
Please see: https://phoenix.apache.org/phoenix_spark.html