Hi,
I agree with Gourav that just measuring execution time is a simplistic approach that may lead you to miss important details, in particular when running distributed computations. WebUI, REST API, and metrics instrumentation in Spark can be quite useful for further drill down. See https://spark.apache.org/docs/latest/monitoring.html You can also have a look at this tool that takes care of automating collecting and aggregating some executor task metrics: https://github.com/LucaCanali/sparkMeasure Best, Luca From: Gourav Sengupta <gourav.sengu...@gmail.com> Sent: Thursday, December 23, 2021 14:23 To: bit...@bitfox.top Cc: user <user@spark.apache.org> Subject: Re: measure running time Hi, I do not think that such time comparisons make any sense at all in distributed computation. Just saying that an operation in RDD and Dataframe can be compared based on their start and stop time may not provide any valid information. You will have to look into the details of timing and the steps. For example, please look at the SPARK UI to see how timings are calculated in distributed computing mode, there are several well written papers on this. Thanks and Regards, Gourav Sengupta On Thu, Dec 23, 2021 at 10:57 AM <bit...@bitfox.top <mailto:bit...@bitfox.top> > wrote: hello community, In pyspark how can I measure the running time to the command? I just want to compare the running time of the RDD API and dataframe API, in my this blog: https://bitfoxtop.wordpress.com/2021/12/23/count-email-addresses-using-sparks-rdd-and-dataframe/ I tried spark.time() it doesn't work. Thank you. --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org <mailto:user-unsubscr...@spark.apache.org>
