How to export data from Spark SQL to CSV - hadoop

Question

This command works with HiveQL:
insert overwrite directory '/data/home.csv' select * from testtable;
But with Spark SQL I'm getting an error with an org.apache.spark.sql.hive.HiveQl stack trace:
java.lang.RuntimeException: Unsupported language features in query:
insert overwrite directory '/data/home.csv' select * from testtable
Please guide me to write export to CSV feature in Spark SQL.

Answer 1

You can use below statement to write the contents of dataframe in CSV format
df.write.csv("/data/home/csv")
If you need to write the whole dataframe into a single CSV file, then use
df.coalesce(1).write.csv("/data/home/sample.csv")
For spark 1.x, you can use spark-csv to write the results into CSV files
Below scala snippet would help
import org.apache.spark.sql.hive.HiveContext
// sc - existing spark context
val sqlContext = new HiveContext(sc)
val df = sqlContext.sql("SELECT * FROM testtable")
df.write.format("com.databricks.spark.csv").save("/data/home/csv")
To write the contents into a single file
import org.apache.spark.sql.hive.HiveContext
// sc - existing spark context
val sqlContext = new HiveContext(sc)
val df = sqlContext.sql("SELECT * FROM testtable")
df.coalesce(1).write.format("com.databricks.spark.csv").save("/data/home/sample.csv")

Answer 2

Since Spark 2.X spark-csv is integrated as native datasource. Therefore, the necessary statement simplifies to (windows)
df.write
.option("header", "true")
.csv("file:///C:/out.csv")
or UNIX
df.write
.option("header", "true")
.csv("/var/out.csv")
Notice: as the comments say, it is creating the directory by that name with the partitions in it, not a standard CSV file. This, however, is most likely what you want since otherwise your either crashing your driver (out of RAM) or you could be working with a non distributed environment.

Answer 3

The answer above with spark-csv is correct but there is an issue - the library creates several files based on the data frame partitioning. And this is not what we usually need. So, you can combine all partitions to one:
df.coalesce(1).
write.
format("com.databricks.spark.csv").
option("header", "true").
save("myfile.csv")
and rename the output of the lib (name "part-00000") to a desire filename.
This blog post provides more details: https://fullstackml.com/2015/12/21/how-to-export-data-frame-from-apache-spark/

Answer 4

The simplest way is to map over the DataFrame's RDD and use mkString:
df.rdd.map(x=>x.mkString(","))
As of Spark 1.5 (or even before that)
df.map(r=>r.mkString(",")) would do the same
if you want CSV escaping you can use apache commons lang for that. e.g. here's the code we're using
def DfToTextFile(path: String,
df: DataFrame,
delimiter: String = ",",
csvEscape: Boolean = true,
partitions: Int = 1,
compress: Boolean = true,
header: Option[String] = None,
maxColumnLength: Option[Int] = None) = {
def trimColumnLength(c: String) = {
val col = maxColumnLength match {
case None => c
case Some(len: Int) => c.take(len)
}
if (csvEscape) StringEscapeUtils.escapeCsv(col) else col
}
def rowToString(r: Row) = {
val st = r.mkString("~-~").replaceAll("[\\p{C}|\\uFFFD]", "") //remove control characters
st.split("~-~").map(trimColumnLength).mkString(delimiter)
}
def addHeader(r: RDD[String]) = {
val rdd = for (h <- header;
if partitions == 1; //headers only supported for single partitions
tmpRdd = sc.parallelize(Array(h))) yield tmpRdd.union(r).coalesce(1)
rdd.getOrElse(r)
}
val rdd = df.map(rowToString).repartition(partitions)
val headerRdd = addHeader(rdd)
if (compress)
headerRdd.saveAsTextFile(path, classOf[GzipCodec])
else
headerRdd.saveAsTextFile(path)
}

Answer 5

With the help of spark-csv we can write to a CSV file.
val dfsql = sqlContext.sql("select * from tablename")
dfsql.write.format("com.databricks.spark.csv").option("header","true").save("output.csv")`

Answer 6

The error message suggests this is not a supported feature in the query language. But you can save a DataFrame in any format as usual through the RDD interface (df.rdd.saveAsTextFile). Or you can check out https://github.com/databricks/spark-csv.

Answer 7

enter code here IN DATAFRAME:
val p=spark.read.format("csv").options(Map("header"->"true","delimiter"->"^")).load("filename.csv")