Have you tried catching error when you are creating a dataframe?
import scala.util.{Try, Success, Failure}
val df = Try(spark.read.
format("com.databricks.spark.xml").
option("rootTag", "hierarchy").
option("rowTag", "sms_request").
load("/tmp/broadcast.xml")) match {
case Success(df) => df
case Failure(exception) => throw new Exception("foo")
}
HTH
Dr Mich Talebzadeh
LinkedIn *
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
<https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
http://talebzadehmich.wordpress.com
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Sat, 9 May 2020 at 22:51, Chetan Khatri <chetan.opensou...@gmail.com>
wrote:
> Hi Spark Users,
>
> I've a spark job where I am reading the parquet path, and that parquet
> path data is generated by other systems, some of the parquet paths doesn't
> contains any data which is possible. is there a any way to read the parquet
> if no data found I can create a dummy dataframe and go ahead.
>
> One way is to check path exists like
>
> val conf = spark.sparkContext.hadoopConfiguration
> val fs = org.apache.hadoop.fs.FileSystem.get(conf)
> val currentAreaExists = fs.exists(new
> org.apache.hadoop.fs.Path(consumableCurrentArea))
>
> But I don't want to check this for 300 parquets, just if data doesn't
> exist in the parquet path go with the dummy parquet / custom DataFrame
>
> AnalysisException: u'Unable to infer schema for Parquet. It must be
> specified manually.;'
>
> Thanks
>
