Thanks Todd. This is what I did before creating DF on top of that file
var exists = true
exists = xmlDirExists(broadcastStagingConfig.xmlFilePath)
if(!exists) {
println(s"\n Error: The xml file ${ broadcastStagingConfig.xmlFilePath}
does not exist, aborting!\n")
sys.exit(1)
}
.
.
def xmlFileExists(hdfsDirectory: String): Boolean = {
val hadoopConf = new org.apache.hadoop.conf.Configuration()
val fs = org.apache.hadoop.fs.FileSystem.get(hadoopConf)
fs.exists(new org.apache.hadoop.fs.Path(hdfsDirectory))
}
And checked it. It works.
Dr Mich Talebzadeh
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On Tue, 5 May 2020 at 17:54, Todd Nist <tsind...@gmail.com> wrote:
> Could you do something like this prior to calling the action.
>
> // Create FileSystem object from Hadoop Configuration
> val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
> // This methods returns Boolean (true - if file exists, false - if file
> doesn't exist
> val fileExists = fs.exists(new Path("<parh_to_file>"))
> if (fileExists) println("File exists!")
> else println("File doesn't exist!")
>
> Not sure that will help you or not, just a thought.
>
> -Todd
>
>
>
>
> On Tue, May 5, 2020 at 11:45 AM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Thanks Brandon!
>>
>> i should have remembered that.
>>
>> basically the code gets out with sys.exit(1) if it cannot find the file
>>
>> I guess there is no easy way of validating DF except actioning it by
>> show(1,0) etc and checking if it works?
>>
>> Regards,
>>
>> Dr Mich Talebzadeh
>>
>>
>>
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>> *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.
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>>
>>
>>
>> On Tue, 5 May 2020 at 16:41, Brandon Geise <brandonge...@gmail.com>
>> wrote:
>>
>>> 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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>>>
>>>
>>
