Please note that limit drops the partitions to 1.
If it is only 100 records you might be able to fit it in one executor , so limit followed by a write is okay. From: Brandon Geise <brandonge...@gmail.com> Sent: Sunday, April 14, 2019 9:54 AM To: Chetan Khatri <chetan.opensou...@gmail.com> Cc: Nuthan Reddy <nut...@sigmoidanalytics.com>; user <user@spark.apache.org> Subject: Re: How to print DataFrame.show(100) to text file at HDFS Use .limit on the dataframe followed by .write On Apr 14, 2019, at 5:10 AM, Chetan Khatri <chetan.opensou...@gmail.com <mailto:chetan.opensou...@gmail.com> > wrote: Nuthan, Thank you for reply. the solution proposed will give everything. for me is like one Dataframe show(100) in 3000 lines of Scala Spark code. However, yarn logs --applicationId <APPLICAION_ID> > 1.log also gives all stdout and stderr. Thanks On Sun, Apr 14, 2019 at 10:30 AM Nuthan Reddy < nut...@sigmoidanalytics.com <mailto:nut...@sigmoidanalytics.com> > wrote: Hi Chetan, You can use spark-submit showDF.py | hadoop fs -put - showDF.txt showDF.py: from pyspark.sql import SparkSession spark = SparkSession.builder.appName("Write stdout").getOrCreate() spark.sparkContext.setLogLevel("OFF") spark.table("<yourdf>").show(100,truncate=false) But is there any specific reason you want to write it to hdfs? Is this for human consumption? Regards, Nuthan On Sat, Apr 13, 2019 at 6:41 PM Chetan Khatri < chetan.opensou...@gmail.com <mailto:chetan.opensou...@gmail.com> > wrote: Hello Users, In spark when I have a DataFrame and do .show(100) the output which gets printed, I wants to save as it is content to txt file in HDFS. How can I do this? Thanks -- Nuthan Reddy Sigmoid Analytics Disclaimer: This is not a mass e-mail and my intention here is purely from a business perspective, and not to spam or encroach your privacy. I am writing with a specific agenda to build a personal business connection. Being a reputed and genuine organization, Sigmoid respects the digital security of every prospect and tries to comply with GDPR and other regional laws. Please let us know if you feel otherwise and we will rectify the misunderstanding and adhere to comply in the future. In case we have missed any of the compliance, it is completely unintentional.
