How to read only n rows of large CSV file on HDFS using spark-csv package?

Question

I have a big distributed file on HDFS and each time I use sqlContext with spark-csv package, it first loads the entire file which takes quite some time.

df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load("file_path")

now as I just want to do some quick check at times, all I need is few/ any n rows of the entire file.

df_n = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load("file_path").take(n)
df_n = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load("file_path").head(n)

but all these run after the file load is done. Can't I just restrict the number of rows while reading the file itself ? I am referring to n_rows equivalent of pandas in spark-csv, like:

pd_df = pandas.read_csv("file_path", nrows=20)

Or it might be the case that spark does not actually load the file, the first step, but in this case, why is my file load step taking too much time then?

I want

df.count()

to give me only n and not all rows, is it possible ?

Answer 1

You can use limit(n).

sqlContext.format('com.databricks.spark.csv') \
          .options(header='true', inferschema='true').load("file_path").limit(20)

This will just load 20 rows.

Answer 2

My understanding is that reading just a few lines is not supported by spark-csv module directly, and as a workaround you could just read the file as a text file, take as many lines as you want and save it to some temporary location. With the lines saved, you could use spark-csv to read the lines, including inferSchema option (that you may want to use given you are in exploration mode).

val numberOfLines = ...
spark.
  read.
  text("myfile.csv").
  limit(numberOfLines).
  write.
  text(s"myfile-$numberOfLines.csv")
val justFewLines = spark.
  read.
  option("inferSchema", true). // <-- you are in exploration mode, aren't you?
  csv(s"myfile-$numberOfLines.csv")