I am trying to use spark streaming in reading data from one HDFS location to another
Below is my code snippet on spark-shell
But I couldn't see the files created on HDFS output directory
Can some point point how to load the files on HDFS
scala> sc.stop()
scala> import org.apache.spark.SparkConf
scala> import org.apache.spark.streaming
scala> import org.apache.spark.streaming.{StreamingContext,Seconds}
scala> val conf = new SparkConf().setMaster("local[2]").setAppName("files_word_count")
scala> val ssc = new StreamingContext(conf,Seconds(10))
scala> val DF = ssc.textFileStream("/user/cloudera/streamingcontext_dir")
scala> val words_freq = DF.flatMap(x=>(x.split(" "))).map(y=>(y,1)).reduceByKey(_+_)
scala> words_freq.saveAsTextFiles("hdfs://localhost:8020/user/cloudera/streamingcontext_dir2")
scala> ssc.start()
I have placed files on HDFS "/user/cloudera/streamingcontext_dir" and created another directory "/user/cloudera/streamingcontext_dir2" for seeing the files written
But I couldn't see the files in the output directory
Can someone point what's wrong here ?
Thanks
Sumit
Try making use of RDD here and not the entire DStream perhaps:
words_freq.foreachRDD(rdd =>
rdd.saveAsTextFile("hdfs://localhost:8020/user/cloudera/streamingcontext_dir2")
Related
I'm trying to read a parquet file that I downloaded from the HDFS on my Jupyter notebook however it is showing up as empty. I know it is not empty because I had worked on it prior to saving it to the HDFS. Does anyone know why it is being read as empty?
The size of the file on the HDFS and cluster environment:
hadoop fs -du -s -h /user/some/test.parquet
1.2 M 3.5 M /user/some/test.parquet
val test = spark.read.parquet("hdfs:///user/some/test.parquet")
test.count()
res0: Long = 10
On an almond-kernel in Jupyter notebook to work in Scala.
import org.apache.log4j.{Level, Logger}
Logger.getLogger("org").setLevel(Level.OFF)
import org.apache.spark.sql._
val spark = {
SparkSession.builder()
.master("local[*]")
.getOrCreate()
}
def sc = spark.sparkContext
val test = spark.read.parquet("/Users/me/some/test.parquet")
test: DataFrame = [UnitId: string, GeoId: string ... 26 more fields]
test.count()
res28: Long = 0L
For anyone who's interested, I figured out the issue.
I had downloaded the parquet file from the HDFS using "hadoop fs -getmerge", resulting in the corruption of the file.
The right approach when dealing with the parquet file would be to "hadoop fs -get.
I am new to Scala and HDFS:
I am just wondering I am able to read local file from Scala code but how to read from HDFS:
import scala.io.source
object ReadLine {
def main(args:Array[String]) {
if (args.length>0) {
for (line <- Source.fromLine(args(0)).getLine())
println(line)
}
}
in Argument I have passed hdfs://localhost:9000/usr/local/log_data/file1.. But its giving FileNotFoundException error
I am definitely missing something.. can anyone help me out here ?
scala.io.source api cannot read from HDFS. Source is used to read from local file system.
Spark
If you want to read from hdfs then I would recommend to use spark where you would have to use sparkContext.
val lines = sc.textFile(args(0)) //args(0) should be hdfs:///usr/local/log_data/file1
No Spark
If you don't want to use spark then you should go with BufferedReader or StreamReader or hadoop filesystem api. for example
val hdfs = FileSystem.get(new URI("hdfs://yourUrl:port/"), new Configuration())
val path = new Path("/path/to/file/")
val stream = hdfs.open(path)
def readLines = Stream.cons(stream.readLine, Stream.continually( stream.readLine))
I am learning how to read and write from files in HDFS by using Spark/Scala.
I am unable to write in HDFS file, the file is created, but it's empty.
I don't know how to create a loop for writing in a file.
The code is:
import scala.collection.immutable.Map
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
// Read the adult CSV file
val logFile = "hdfs://zobbi01:9000/input/adult.csv"
val conf = new SparkConf().setAppName("Simple Application")
val sc = new SparkContext(conf)
val logData = sc.textFile(logFile, 2).cache()
//val logFile = sc.textFile("hdfs://zobbi01:9000/input/adult.csv")
val headerAndRows = logData.map(line => line.split(",").map(_.trim))
val header = headerAndRows.first
val data = headerAndRows.filter(_(0) != header(0))
val maps = data.map(splits => header.zip(splits).toMap)
val result = maps.filter(map => map("AGE") != "23")
result.foreach{
result.saveAsTextFile("hdfs://zobbi01:9000/input/test2.txt")
}
If I replace:
result.foreach{println}
Then it works!
but when using the method of (saveAsTextFile), then an error message is thrown as
<console>:76: error: type mismatch;
found : Unit
required: scala.collection.immutable.Map[String,String] => Unit
result.saveAsTextFile("hdfs://zobbi01:9000/input/test2.txt")
Any help please.
result.saveAsTextFile("hdfs://zobbi01:9000/input/test2.txt")
This is all what you need to do. You don't need to loop through all the rows.
Hope this helps!
What this does!!!
result.foreach{
result.saveAsTextFile("hdfs://zobbi01:9000/input/test2.txt")
}
RDD action cannot be triggered from RDD transformations unless special conf set.
Just use result.saveAsTextFile("hdfs://zobbi01:9000/input/test2.txt") to save to HDFS.
I f you need other formats in the file to be written, change in rdd itself before writing.
My problem is that, as I change my code into streaming mode and put my data frame into the foreach loop, the data frame shows empty table! I does't fill! I also can not put it into assembler.transform(). The error is:
Error:(38, 40) not enough arguments for method map: (mapFunc: String => U)(implicit evidence$2: scala.reflect.ClassTag[U])org.apache.spark.streaming.dstream.DStream[U].
Unspecified value parameter mapFunc.
val dataFrame = Train_DStream.map()
My train.csv file is like below:
Please help me.
Here is my code:
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.{LabeledPoint, StreamingLinearRegressionWithSGD}
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
import scala.util.Try
/**
* Created by saeedtkh on 5/22/17.
*/
object ML_Test {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setMaster("local").setAppName("HdfsWordCount")
val sc = new SparkContext(sparkConf)
// Create the context
val ssc = new StreamingContext(sc, Seconds(10))
val sqlContext = new SQLContext(sc)
val customSchema = StructType(Array(
StructField("column0", StringType, true),
StructField("column1", StringType, true),
StructField("column2", StringType, true)))
//val Test_DStream = ssc.textFileStream("/Users/saeedtkh/Desktop/sharedsaeed/train.csv").map(LabeledPoint.parse)
val Train_DStream = ssc.textFileStream("/Users/saeedtkh/Desktop/sharedsaeed/train.csv")
val DStream =Train_DStream.map(line => line.split(">")).map(array => {
val first = Try(array(0).trim.split(" ")(0)) getOrElse ""
val second = Try(array(1).trim.split(" ")(6)) getOrElse ""
val third = Try(array(2).trim.split(" ")(0).replace(":", "")) getOrElse ""
Row.fromSeq(Seq(first, second, third))
})
DStream.foreachRDD { Test_DStream =>
val dataFrame = sqlContext.createDataFrame(Test_DStream, customSchema)
dataFrame.groupBy("column1", "column2").count().show()
val numFeatures = 3
val model = new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.zeros(numFeatures))
val featureCol = Array("column1", "column2")
val assembler=new VectorAssembler().setInputCols(featureCol).setOutputCol("features")
dataFrame.show()
val df_new=assembler.transform(dataFrame)
}
ssc.start()
ssc.awaitTermination()
}
}
My guess is that all the files under /Users/saeedtkh/Desktop/sharedsaeed/train.csv directory have already been processed and so there are no files left and hence the DataFrame is empty.
Please note that the sole input parameter for StreamingContext.textFileStream is a directory not a file.
textFileStream(directory: String): DStream[String] Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files
Please also note that once a file has ever been processed in a Spark Streaming application, this file should not be changed (or appended to) since the file has already been marked as processed and Spark Streaming will ignore any modifications.
Quoting the official documentation of Spark Streaming in Basic Sources:
Spark Streaming will monitor the directory dataDirectory and process any files created in that directory (files written in nested directories not supported).
Note that
The files must have the same data format.
The files must be created in the dataDirectory by atomically moving or renaming them into the data directory.
Once moved, the files must not be changed. So if the files are being continuously appended, the new data will not be read.
For simple text files, there is an easier method streamingContext.textFileStream(dataDirectory). And file streams do not require running a receiver, hence does not require allocating cores.
Please also replace setMaster("local") with setMaster("local[*]") to make sure your Spark Streaming application will have enough threads to process incoming data (you have to have at least 2 threads).
I want to list all folders within a hdfs directory using Scala/Spark.
In Hadoop I can do this by using the command: hadoop fs -ls hdfs://sandbox.hortonworks.com/demo/
I tried it with:
val conf = new Configuration()
val fs = FileSystem.get(new URI("hdfs://sandbox.hortonworks.com/"), conf)
val path = new Path("hdfs://sandbox.hortonworks.com/demo/")
val files = fs.listFiles(path, false)
But it does not seem that he looks in the Hadoop directory as i cannot find my folders/files.
I also tried with:
FileSystem.get(sc.hadoopConfiguration).listFiles(new Path("hdfs://sandbox.hortonworks.com/demo/"), true)
But this also does not help.
Do you have any other idea?
PS: I also checked this thread: Spark iterate HDFS directory but it does not work for me as it does not seem to search on hdfs directory, instead only on the local file system with schema file//.
We are using hadoop 1.4 and it doesn't have listFiles method so we use listStatus to get directories. It doesn't have recursive option but it is easy to manage recursive lookup.
val fs = FileSystem.get(new Configuration())
val status = fs.listStatus(new Path(YOUR_HDFS_PATH))
status.foreach(x=> println(x.getPath))
In Spark 2.0+,
import org.apache.hadoop.fs.{FileSystem, Path}
val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
fs.listStatus(new Path(s"${hdfs-path}")).filter(_.isDir).map(_.getPath).foreach(println)
Hope this is helpful.
in Ajay Ahujas answer isDir is deprecated..
use isDirectory... pls see complete example and output below.
package examples
import org.apache.log4j.Level
import org.apache.spark.sql.SparkSession
object ListHDFSDirectories extends App{
val logger = org.apache.log4j.Logger.getLogger("org")
logger.setLevel(Level.WARN)
val spark = SparkSession.builder()
.appName(this.getClass.getName)
.config("spark.master", "local[*]").getOrCreate()
val hdfspath = "." // your path here
import org.apache.hadoop.fs.{FileSystem, Path}
val fs = org.apache.hadoop.fs.FileSystem.get(spark.sparkContext.hadoopConfiguration)
fs.listStatus(new Path(s"${hdfspath}")).filter(_.isDirectory).map(_.getPath).foreach(println)
}
Result :
file:/Users/user/codebase/myproject/target
file:/Users/user/codebase/myproject/Rel
file:/Users/user/codebase/myproject/spark-warehouse
file:/Users/user/codebase/myproject/metastore_db
file:/Users/user/codebase/myproject/.idea
file:/Users/user/codebase/myproject/src
I was looking for the same, however instead of HDFS, for S3.
I solved creating the FileSystem with my S3 path as below:
def getSubFolders(path: String)(implicit sparkContext: SparkContext): Seq[String] = {
val hadoopConf = sparkContext.hadoopConfiguration
val uri = new URI(path)
FileSystem.get(uri, hadoopConf).listStatus(new Path(path)).map {
_.getPath.toString
}
}
I know this question was related for HDFS, but maybe others like me will come here looking for S3 solution. Since without specifying the URI in FileSystem, it will look for HDFS ones.
java.lang.IllegalArgumentException: Wrong FS: s3://<bucket>/dummy_path
expected: hdfs://<ip-machine>.eu-west-1.compute.internal:8020
val listStatus = org.apache.hadoop.fs.FileSystem.get(new URI(url), sc.hadoopConfiguration)
.globStatus(new org.apache.hadoop.fs.Path(url))
for (urlStatus <- listStatus) {
println("urlStatus get Path:" + urlStatus.getPath())
}
val spark = SparkSession.builder().appName("Demo").getOrCreate()
val path = new Path("enter your directory path")
val fs:FileSystem = projects.getFileSystem(spark.sparkContext.hadoopConfiguration)
val it = fs.listLocatedStatus(path)
This will create an iterator it over org.apache.hadoop.fs.LocatedFileStatus that is your subdirectory
Azure Blog Storage is mapped to a HDFS location, so all the Hadoop Operations
On Azure Portal, go to Storage Account, you will find following details:
Storage account
Key -
Container -
Path pattern – /users/accountsdata/
Date format – yyyy-mm-dd
Event serialization format – json
Format – line separated
Path Pattern here is the HDFS path, you can login/putty to the Hadoop Edge Node and do:
hadoop fs -ls /users/accountsdata
Above command will list all the files. In Scala you can use
import scala.sys.process._
val lsResult = Seq("hadoop","fs","-ls","/users/accountsdata/").!!
object HDFSProgram extends App {
val uri = new URI("hdfs://HOSTNAME:PORT")
val fs = FileSystem.get(uri,new Configuration())
val filePath = new Path("/user/hive/")
val status = fs.listStatus(filePath)
status.map(sts => sts.getPath).foreach(println)
}
This is sample code to get list of hdfs files or folder present under /user/hive/
Because you're using Scala, you may also be interested in the following:
import scala.sys.process._
val lsResult = Seq("hadoop","fs","-ls","hdfs://sandbox.hortonworks.com/demo/").!!
This will, unfortunately, return the entire output of the command as a string, and so parsing down to just the filenames requires some effort. (Use fs.listStatus instead.) But if you find yourself needing to run other commands where you could do it in the command line easily and are unsure how to do it in Scala, just use the command line through scala.sys.process._. (Use a single ! if you want to just get the return code.)