I am writing a Scala code that requires me to write to a file in HDFS.
When I use Filewriter.write on local, it works. The same thing does not work on HDFS.
Upon checking, I found that there are the following options to write in Apache Spark-
RDD.saveAsTextFile and DataFrame.write.format.
My question is: what if I just want to write an int or string to a file in Apache Spark?
Follow up:
I need to write to an output file a header, DataFrame contents and then append some string.
Does sc.parallelize(Seq(<String>)) help?
create RDD with your data (int/string) using Seq: see parallelized-collections for details:
sc.parallelize(Seq(5)) //for writing int (5)
sc.parallelize(Seq("Test String")) // for writing string
val conf = new SparkConf().setAppName("Writing Int to File").setMaster("local")
val sc = new SparkContext(conf)
val intRdd= sc.parallelize(Seq(5))
intRdd.saveAsTextFile("out\\int\\test")
val conf = new SparkConf().setAppName("Writing string to File").setMaster("local")
val sc = new SparkContext(conf)
val stringRdd = sc.parallelize(Seq("Test String"))
stringRdd.saveAsTextFile("out\\string\\test")
Follow up Example: (Tested as below)
val conf = new SparkConf().setAppName("Total Countries having Icon").setMaster("local")
val sc = new SparkContext(conf)
val headerRDD= sc.parallelize(Seq("HEADER"))
//Replace BODY part with your DF
val bodyRDD= sc.parallelize(Seq("BODY"))
val footerRDD = sc.parallelize(Seq("FOOTER"))
//combine all rdds to final
val finalRDD = headerRDD ++ bodyRDD ++ footerRDD
//finalRDD.foreach(line => println(line))
//output to one file
finalRDD.coalesce(1, true).saveAsTextFile("test")
output:
HEADER
BODY
FOOTER
more examples here. . .
Related
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.
Perhaps this question may seem a bit abstract, here it is:
val originalAvroSchema : Schema = // read from a file
val rdd : RDD[GenericData.Record] = // From some streaming source
// Looking for a handy:
val df: DataFrame = rdd.toDF(schema)
I explore spark-avro but it has support only to read from a file, not from existing RDD.
import com.databricks.spark.avro._
val sqlContext = new SQLContext(sc)
val rdd : RDD[MyAvroRecord] = ...
val df = rdd.toAvroDF(sqlContext)
Strangely this doesnt work. Can someone explain the background? I want to understand why it doesnt take this.
The Inputfiles are parquet files spread across multiple folders. When I print the results, they are structured as I want to. When I use a dataframe.count() on the joined dataframe, the job will run forever. Can anyone help with the Details on that
import org.apache.spark.{SparkContext, SparkConf}
object TEST{
def main(args: Array[String] ) {
val appName = args(0)
val threadMaster = args(1)
val inputPathSent = args(2)
val inputPathClicked = args(3)
// pass spark configuration
val conf = new SparkConf()
.setMaster(threadMaster)
.setAppName(appName)
// Create a new spark context
val sc = new SparkContext(conf)
// Specify a SQL context and pass in the spark context we created
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// Create two dataframes for sent and clicked files
val dfSent = sqlContext.read.parquet(inputPathSent)
val dfClicked = sqlContext.read.parquet(inputPathClicked)
// Join them
val dfJoin = dfSent.join(dfClicked, dfSent.col("customer_id")
===dfClicked.col("customer_id") && dfSent.col("campaign_id")===
dfClicked.col("campaign_id"), "left_outer")
dfJoin.show(20) // perfectly shows the first 20 rows
dfJoin.count() //Here we run into trouble and it runs forever
}
}
Use println(dfJoin.count())
You will be able to see the count in your screen.
I'm trying to read an avro file using scala.
I've extracted the file's schema using avro-tools and saved it to a file, I then try to read it using the following code:
val zibi= scala.io.Source.fromFile("/home/wasabi/schema").mkString
val schema_obj = new Schema.Parser
val schema2 = schema_obj.parse(zibi)
val READER2 = new GenericDatumReader[GenericRecord](schema2)
val myFile = Files.readAllBytes(Paths.get("/tmp/check/CMRF_80_1442744555901-1_1_2_1_1_1_4_10_1.avro"))
val datum = READER2.read(null, DecoderFactory.defaultFactory.createBinaryDecoder(myFile,null))
But I keep hitting IOExceptions as such:
java.io.IOException: Invalid int encoding
at org.apache.avro.io.BinaryDecoder.readInt(BinaryDecoder.java:145)
at org.apache.avro.io.ValidatingDecoder.readInt(ValidatingDecoder.java:83)
at org.apache.avro.generic.GenericDatumReader.readInt(GenericDatumReader.java:444)
at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:159)
at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:193)
at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:183)
at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:151)
at org.apache.avro.generic.GenericDatumReader.readArray(GenericDatumReader.java:219)
at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:193)
at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:183)
at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:151)
at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:142)
When I'm reading the file through avro-tools it reads just fine.
What am I doing wrong?
Try using a DataFileReader instead of using a BinaryDecoder.
While Encoder/Decoders are used for writing and reading raw avros, I suspect that they are choking on the header info found in avro datafiles.
import org.apache.avro.generic.{ GenericDatumReader, GenericRecord }
import org.apache.avro.file.DataFileReader
val zibi= scala.io.Source.fromFile("/home/wasabi/schema").mkString
val schema_obj = new Schema.Parser
val schema2 = schema_obj.parse(zibi)
val READER2 = new GenericDatumReader[GenericRecord](schema2)
val myFile = new File("/tmp/check/CMRF_80_1442744555901-1_1_2_1_1_1_4_10_1.avro")
val dataFileReader = new DataFileReader[GenericRecord](myFile, READER2)
val datum = dataFileReader.next()
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.