Write/store dataframe in text file

save dataframe as text file pyspark
spark write text file
spark write dataframe to local file system
spark dataframe write
dataframe write to file
write dataframe to file scala
sparkr write text
pyspark dataframe write to file

I am trying to write dataframe to text file. If a file contains single column then I am able to write in text file. If file contains multiple column then I a facing some error

Text data source supports only a single column, and you have 2 columns.

object replace {

  def main(args:Array[String]): Unit = {

    Logger.getLogger("org").setLevel(Level.ERROR)

    val spark = SparkSession.builder.master("local[1]").appName("Decimal Field Validation").getOrCreate()

    var sourcefile = spark.read.option("header","true").text("C:/Users/phadpa01/Desktop/inputfiles/decimalvalues.txt")

     val rowRDD = sourcefile.rdd.zipWithIndex().map(indexedRow => Row.fromSeq((indexedRow._2.toLong+1) +: indexedRow._1.toSeq)) //adding prgrefnbr               
                         //add column for prgrefnbr in schema
     val newstructure = StructType(Array(StructField("PRGREFNBR",LongType)).++(sourcefile.schema.fields))

     //create new dataframe containing prgrefnbr

     sourcefile = spark.createDataFrame(rowRDD, newstructure)
     val op= sourcefile.write.mode("overwrite").format("text").save("C:/Users/phadpa01/Desktop/op")

  }

}

you can convert the dataframe to rdd and covert the row to string and write the last line as

 val op= sourcefile.rdd.map(_.toString()).saveAsTextFile("C:/Users/phadpa01/Desktop/op")

Edited

As @philantrovert and @Pravinkumar have pointed that the above would append [ and ] in the output file, which is true. The solution would be to replace them with empty character as

val op= sourcefile.rdd.map(_.toString().replace("[","").replace("]", "")).saveAsTextFile("C:/Users/phadpa01/Desktop/op")

One can even use regex

Solved: How to save dataframe as text file, Solved: How to save the data inside a dataframe to text file in csv format in HDFS df.write.format("com.databricks.spark.csv").option("header",  Write/store dataframe in text file. 1. Sum the column of a data frame in Spark 2.2.0 and Scala. 0. Compare each columns of two data frames and output only diff

I would recommend using a csv or other delimited formats. The following is an example with the most concise/elegant way to write to .tsv in Spark 2+

val tsvWithHeaderOptions: Map[String, String] = Map(
  ("delimiter", "\t"), // Uses "\t" delimiter instead of default ","
  ("header", "true"))  // Writes a header record with column names

df.coalesce(1)         // Writes to a single file
  .write
  .mode(SaveMode.Overwrite)
  .options(tsvWithHeaderOptions)
  .csv("output/path")

How can a DataFrame be directly saved as a textFile in scala on , You can set the following option(s) for writing text files: compression (default null ): compression codec to use when saving to file. This can be one of the  is converted to os.linesep for files opened in text-mode. So when you write os.linesep to a text-mode file on Windows, you write \r , and the gets converted resulting in \r\r . See also the docs: Do not use os.linesep as a line terminator when writing files opened in text mode (the default); use a single ' ' instead, on all platforms.

I think using "substring" is more appropriate for all scenarios I feel.

Please check below code.

sourcefile.rdd
.map(r =>  { val x = r.toString; x.substring(1, x.length-1)})
.saveAsTextFile("C:/Users/phadpa01/Desktop/op")

Data frames -- writing it in text format - Apache Spark, Hi, When I try to write the dataframes which has two colums it throws the below error. So can't we write more than one column as textfile? Commands: scala> val This is how I am converting and storing as textfile. Teams. Q&A for Work. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

You can save as text CSV file (.format("csv"))

The result will be a text file in a CSV format, each column will be separated by a comma.

val op = sourcefile.write.mode("overwrite").format("csv").save("C:/Users/phadpa01/Desktop/op")

More info can be found in the spark programming guide

Write/store dataframe in text file - scala - html, Write/store dataframe in text file - scala. you can convert the dataframe to rdd and covert the row to string and write the last line as val op= sourcefile.rdd.map(_​  The recommended way to store xarray data structures is netCDF, which is a binary file format for self-described datasets that originated in the geosciences. xarray is based on the netCDF data model, so netCDF files on disk directly correspond to Dataset objects (more accurately, a group in a netCDF file directly corresponds to a to Dataset object.

I use databricks api to save my DF output into text file.

myDF.write.format("com.databricks.spark.csv").option("header", "true").save("output.csv")

Save the content of SparkDataFrame in a text file at the, Each row becomes a new line in the output file. Usage. ## S4 method for signature 'SparkDataFrame,character' write.text(x, path, mode = "error", .. 2. Each file in the batch will be read, written to another folder and removed from original folder. 3. When a batch is processed, repeat from step 1. So each file has 3 io operations which i am doing using file system api. I am running into performance issues.

write from a Dataframe to a CSV file, CSV file is blank, 3. you can rename files after saving them. Here is solution for you. ###### Write your data frame to a single file with default name to a temp  The following examples show how to use org.apache.spark.sql.SaveMode.These examples are extracted from open source projects. You can vote up the examples you like and your votes will be used in our system to produce more good examples.

Reading a text file through spark data frame, How can I write a text file in HDFS not from an RDD, in Spark program? Scala: Convert text file data into ORC format using data frame. Read PDF or TEXT file. (For PDF files, each page is treated as a single document which helps in weighting the keywords) Tokenization: The text is then tokenized using RegexpTokenizer from nltk.tokenize, allowing only Alphabets, [A-Z] and [a-z] thus also removing puncutations from the text.

IO Tools (Text, CSV, HDF5, ), Writing to CSV format¶. The Series and DataFrame objects have an instance method to_csv which allows storing the contents of the object as a  Transforms json to text and puts text file in a folder. Input: <root:str> - path to the cssi_folder It checks if json file is empty, if it can be read and

Comments
  • I think this will add [ and ] at both ends of each row.
  • but its adding "[ ]" to each record for each line.eg:[2,12.2,12.2]
  • yes it does you can replace it with empty. let me update the answer
  • yes I am able to read but "EURO' sign reading as a garbej value.input value = €|€,output value= "�|�". after conversion
  • I am guessing its a serialization and deserialization issue . This can be another question in SO . What do you say @PravinkumarHadpad?
  • This does not write a tsv file but a csv
  • NOTE the "delimiter", "\t" option. It should work (works for me)
  • I have exactly the same code in Spark 2.4 and it writes a CSV. Moreover, I did not find a solution to this.
  • Works for me in 2.3. I wonder if something else is going on here though...
  • I want file extension should be .txt by above solution file extension is .csv
  • How do you want each row to be printed? Comma-separated or something else?
  • @PravinkumarHadpad - why do you care if the output file extension is .txt or .csv?
  • its appending double quotes for the values available in dataframes before adding seq number.eg:- 3,"12.20,12.2-" but I want output file data like 3,12.20,12.2
  • basically I want double quotes free out file thats why I want to store it in text file.