You could use the Hadoop API and check if the file exists.
From: Mich Talebzadeh <mich.talebza...@gmail.com>
Date: Tuesday, May 5, 2020 at 11:25 AM
To: "user @spark" <user@spark.apache.org>
Subject: Exception handling in Spark
Hi,
As I understand exception handling in Spark only makes sense if one attempts an
action as opposed to lazy transformations?
Let us assume that I am reading an XML file from the HDFS directory and create
a dataframe DF on it
val broadcastValue = "123456789" // I assume this will be sent as a constant
for the batch
// Create a DF on top of XML
val df = spark.read.
format("com.databricks.spark.xml").
option("rootTag", "hierarchy").
option("rowTag", "sms_request").
load("/tmp/broadcast.xml")
val newDF = df.withColumn("broadcastid", lit(broadcastValue))
newDF.createOrReplaceTempView("tmp")
// Put data in Hive table
//
sqltext = """
INSERT INTO TABLE michtest.BroadcastStaging PARTITION (broadcastid="123456",
brand)
SELECT
ocis_party_id AS partyId
, target_mobile_no AS phoneNumber
, brand
, broadcastid
FROM tmp
"""
//
// Here I am performing a collection
try {
spark.sql(sqltext)
} catch {
case e: SQLException => e.printStackTrace
sys.exit()
}
Now the issue I have is that what if the xml file /tmp/broadcast.xml does not
exist or deleted? I won't be able to catch the error until the hive table is
populated. Of course I can write a shell script to check if the file exist
before running the job or put small collection like df.show(1,0). Are there
more general alternatives?
Thanks
Dr Mich Talebzadeh
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