How can one list all csv files in an HDFS location within the Spark Scala shell? - scala

The purpose of this is in order to manipulate and save a copy of each data file in a second location in HDFS. I will be using
RddName.coalesce(1).saveAsTextFile(pathName)
to save the result to HDFS.
This is why I want to do each file separately even though I am sure the performance will not be as efficient. However, I have yet to determine how to store the list of CSV file paths into an array of strings and then loop through each one with a separate RDD.
Let us use the following anonymous example as the HDFS source locations:
/data/email/click/date=2015-01-01/sent_20150101.csv
/data/email/click/date=2015-01-02/sent_20150102.csv
/data/email/click/date=2015-01-03/sent_20150103.csv
I know how to list the file paths using Hadoop FS Shell:
HDFS DFS -ls /data/email/click/*/*.csv
I know how to create one RDD for all the data:
val sentRdd = sc.textFile( "/data/email/click/*/*.csv" )

I haven't tested it thoroughly but something like this seems to work:
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.hadoop.fs.{FileSystem, Path, LocatedFileStatus, RemoteIterator}
import java.net.URI
val path: String = ???
val hconf = SparkHadoopUtil.get.newConfiguration(sc.getConf)
val hdfs = FileSystem.get(hconf)
val iter = hdfs.listFiles(new Path(path), false)
def listFiles(iter: RemoteIterator[LocatedFileStatus]) = {
def go(iter: RemoteIterator[LocatedFileStatus], acc: List[URI]): List[URI] = {
if (iter.hasNext) {
val uri = iter.next.getPath.toUri
go(iter, uri :: acc)
} else {
acc
}
}
go(iter, List.empty[java.net.URI])
}
listFiles(iter).filter(_.toString.endsWith(".csv"))

This is what ultimately worked for me:
import org.apache.hadoop.fs._
import org.apache.spark.deploy.SparkHadoopUtil
import java.net.URI
val hdfs_conf = SparkHadoopUtil.get.newConfiguration(sc.getConf)
val hdfs = FileSystem.get(hdfs_conf)
// source data in HDFS
val sourcePath = new Path("/<source_location>/<filename_pattern>")
hdfs.globStatus( sourcePath ).foreach{ fileStatus =>
val filePathName = fileStatus.getPath().toString()
val fileName = fileStatus.getPath().getName()
// < DO STUFF HERE>
} // end foreach loop

sc.wholeTextFiles(path) should help. It gives an rdd of (filepath, filecontent).

Related

Spark-Scala read multiple files and move to other directory

I have multiple Csv files in hdfs and some of them not in good format, I would like to read the directory of csv files and then if successfully move the files to other directory. How can I achieve this using spark-scala
You need something like that :
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.SparkContext
val conf = sc.hadoopConfiguration
val fs = FileSystem.get(conf)
val srcPath = "dbfs:/src/"
val dest = "dbfs:/dest/"
val ls = fs.listStatus(new Path(srcPath))
ls.foreach{ p => {
if(true) spark.read.csv(p.getPath.toString).write.csv(dest + p.getName)
else println("File ${p.getName} got wrong format")
}}

What is the correct way to identify if the folder exist on ADLS gen 2 account or not

I am working in scala and spark environment where I want to read parquet file. Before I read, I want to check if the file exists or not. I am writing the following code in jupyter notebook but it does not work - meaning it does not show any frame because the function testDirExist returns false
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path
val hadoopfs: FileSystem = FileSystem.get(spark.sparkContext.hadoopConfiguration)
def testDirExist(path: String): Boolean = {
val p = new Path(path)
hadoopfs.exists(p) && hadoopfs.getFileStatus(p).isDirectory
}
val pt = "abfss://container#account.dfs.core.windows.net/blah/blah/blah
val exists = testDirExist(pt)
if(exists)
{
val dataframe = spark.read.parquet(pt)
dataframe.show()
}
However, the following code works. It shows data frame
val k = spark.read.parquet("abfss://container#account.dfs.core.windows.net/blah/blah/blah)
k.show()
Can anyone help me how can I check if the file exists or not?
Thanks
You just need to set the default filesystem to your storage account:
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path
import java.io.PrintWriter
val conf = new Configuration()
conf.set("fs.defaultFS", "abfss://<container_name>#<account_name>.dfs.core.windows.net")
conf.set("fs.azure.account.auth.type.<container_name>.dfs.core.windows.net", "OAuth")
conf.set("fs.azure.account.oauth.provider.type.<container_name>.dfs.core.windows.net", "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider")
conf.set("fs.azure.account.oauth2.client.id.<container_name>.dfs.core.windows.net", "<client_id>")
conf.set("fs.azure.account.oauth2.client.secret.<container_name>.dfs.core.windows.net", "<secret>")
conf.set("fs.azure.account.oauth2.client.endpoint.<container_name>.dfs.core.windows.net", "https://login.microsoftonline.com/<tenant_id>/oauth2/token")
val fs= FileSystem.get(conf)
val ostream = fs.create(new Path("/abfss_test.out"))
val pwriter = new PrintWriter(ostream)
try {
pwriter.write("Azure Datalake Gen2 test")
pwriter.write("\n")
}
finally {
pwriter.close()
}
// check if the file we've just created exists
println(fs.exists(new Path("/abfss_test.out")))

Spark Scala list folders in directory

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.)

Spark: read csv file from s3 using scala

I am writing a spark job, trying to read a text file using scala, the following works fine on my local machine.
val myFile = "myLocalPath/myFile.csv"
for (line <- Source.fromFile(myFile).getLines()) {
val data = line.split(",")
myHashMap.put(data(0), data(1).toDouble)
}
Then I tried to make it work on AWS, I did the following, but it didn't seem to read the entire file properly. What should be the proper way to read such text file on s3? Thanks a lot!
val credentials = new BasicAWSCredentials("myKey", "mySecretKey");
val s3Client = new AmazonS3Client(credentials);
val s3Object = s3Client.getObject(new GetObjectRequest("myBucket", "myFile.csv"));
val reader = new BufferedReader(new InputStreamReader(s3Object.getObjectContent()));
var line = ""
while ((line = reader.readLine()) != null) {
val data = line.split(",")
myHashMap.put(data(0), data(1).toDouble)
println(line);
}
I think I got it work like below:
val s3Object= s3Client.getObject(new GetObjectRequest("myBucket", "myPath/myFile.csv"));
val myData = Source.fromInputStream(s3Object.getObjectContent()).getLines()
for (line <- myData) {
val data = line.split(",")
myMap.put(data(0), data(1).toDouble)
}
println(" my map : " + myMap.toString())
Read in csv-file with sc.textFile("s3://myBucket/myFile.csv"). That will give you an RDD[String]. Get that into a map
val myHashMap = data.collect
.map(line => {
val substrings = line.split(" ")
(substrings(0), substrings(1).toDouble)})
.toMap
You can the use sc.broadcast to broadcast your map, so that it is readily available on all your worker nodes.
(Note that you can of course also use the Databricks "spark-csv" package to read in the csv-file if you prefer.)
This can be acheived even withoutout importing amazons3 libraries using SparkContext textfile. Use the below code
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.conf.Configuration
val s3Login = "s3://AccessKey:Securitykey#Externalbucket"
val filePath = s3Login + "/Myfolder/myscv.csv"
for (line <- sc.textFile(filePath).collect())
{
var data = line.split(",")
var value1 = data(0)
var value2 = data(1).toDouble
}
In the above code, sc.textFile will read the data from your file and store in the line RDD. It then split each line with , to a different RDD data inside the loop. Then you can access values from this RDD with the index.

Use Spark to list all files in a Hadoop HDFS directory?

I want to loop through all text files in a Hadoop dir and count all the occurrences of the word "error". Is there a way to do a hadoop fs -ls /users/ubuntu/ to list all the files in a dir with the Apache Spark Scala API?
From the given first example, the spark context seems to only access files individually through something like:
val file = spark.textFile("hdfs://target_load_file.txt")
In my problem, I do not know how many nor the names of the files in the HDFS folder beforehand. Looked at the spark context docs but couldn't find this kind of functionality.
You can use a wildcard:
val errorCount = sc.textFile("hdfs://some-directory/*")
.flatMap(_.split(" ")).filter(_ == "error").count
import org.apache.hadoop.fs.{FileSystem, FileUtil, Path}
import scala.collection.mutable.Stack
val fs = FileSystem.get( sc.hadoopConfiguration )
var dirs = Stack[String]()
val files = scala.collection.mutable.ListBuffer.empty[String]
val fs = FileSystem.get(sc.hadoopConfiguration)
dirs.push("/user/username/")
while(!dirs.isEmpty){
val status = fs.listStatus(new Path(dirs.pop()))
status.foreach(x=> if(x.isDirectory) dirs.push(x.getPath.toString) else
files+= x.getPath.toString)
}
files.foreach(println)
For a local installation, (the hdfs default path fs.defaultFS can be found by reading /etc/hadoop/core.xml):
For instance,
import org.apache.hadoop.fs.{FileSystem, Path}
val conf = sc.hadoopConfiguration
conf.set("fs.defaultFS", "hdfs://localhost:9000")
val hdfs: org.apache.hadoop.fs.FileSystem = org.apache.hadoop.fs.FileSystem.get(conf)
val fileStatus = hdfs.listStatus(new Path("hdfs://localhost:9000/foldername/"))
val fileList = fileStatus.map(x => x.getPath.toString)
fileList.foreach(println)