Hi,
Referring to spark 2.x documentation, in
org.apache.spark.sql.DataFrameReader you have this function:
def csv(paths: String*): DataFrame
<http://spark.apache.org/docs/2.1.0/api/scala/org/apache/spark/sql/package.html#DataFrame=org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]>
So you can unpack your Array of paths like this:
val sources = paths.split(',').toSeq
spark.read.option("header", "false")
.schema(custom_schema)
.option('delimiter', '\t')
.option('mode', 'DROPMALFORMED')
.csv(sources: _*)
In spark 1.6.x I think this may work with spark-csv
<https://github.com/databricks/spark-csv> :
spark.read.format("com.databricks.spark.csv").option("header", "false")
.schema(custom_schema)
.option('delimiter', '\t')
.option('mode', 'DROPMALFORMED')
.load(sources: _*)
Cheers,
Radhwane Chebaane
2017-07-05 16:08 GMT+02:00 Didac Gil <didacgil9...@gmail.com>:
> Hi,
>
> Do you know any simple way to load multiple csv files (same schema) that
> are in different paths?
> Wildcards are not a solution, as I want to load specific csv files from
> different folders.
>
> I came across a solution (https://stackoverflow.com/
> questions/37639956/how-to-import-multiple-csv-files-in-a-single-load) that
> suggests something like
>
> spark.read.format("csv").option("header", "false")
> .schema(custom_schema)
> .option('delimiter', '\t')
> .option('mode', 'DROPMALFORMED')
> .load(paths.split(','))
>
> However, even it mentions that this approach would work in Spark 2.x, I
> don’t find an implementation of load that accepts an Array[String] as an
> input parameter.
>
> Thanks in advance for your help.
>
>
> Didac Gil de la Iglesia
> PhD in Computer Science
> didacg...@gmail.com
> Spain: +34 696 285 544 <+34%20696%2028%2055%2044>
> Sweden: +46 (0)730229737 <+46%2073%20022%2097%2037>
> Skype: didac.gil.de.la.iglesia
>
>
--
[image: photo] Radhwane Chebaane
Distributed systems engineer, Mindlytix
Mail: radhw...@mindlytix.com <radhw...@mindlytix.com>
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