Hi Sean, According to the plan I’m observing, this is what happens indeed. There’s exchange operation that sends data to a single partition/task in toPandas() + PyArrow enabled case.
> 12 нояб. 2021 г., в 16:31, Sean Owen <sro...@gmail.com> написал(а): > > Yes, none of the responses are addressing your question. > I do not think it's a bug necessarily; do you end up with one partition in > your execution somewhere? > > On Fri, Nov 12, 2021 at 3:38 AM Sergey Ivanychev <sergeyivanyc...@gmail.com > <mailto:sergeyivanyc...@gmail.com>> wrote: > Of course if I give 64G of ram to each executor they will work. But what’s > the point? Collecting results in the driver should cause a high RAM usage in > the driver and that’s what is happening in collect() case. In the case where > pyarrow serialization is enabled all the data is being collected on a single > executor, which is clearly a wrong way to collect the result on the driver. > > I guess I’ll open an issue about it in Spark Jira. It clearly looks like a > bug. > >> 12 нояб. 2021 г., в 11:59, Mich Talebzadeh <mich.talebza...@gmail.com >> <mailto:mich.talebza...@gmail.com>> написал(а): >> >> OK, your findings do not imply those settings are incorrect. Those settings >> will work if you set-up your k8s cluster in peer-to-peer mode with equal >> amounts of RAM for each node which is common practice. >> >> HTH >> >> >> view my Linkedin profile >> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >> >> 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 Thu, 11 Nov 2021 at 21:39, Sergey Ivanychev <sergeyivanyc...@gmail.com >> <mailto:sergeyivanyc...@gmail.com>> wrote: >> Yes, in fact those are the settings that cause this behaviour. If set to >> false, everything goes fine since the implementation in spark sources in >> this case is >> >> pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns) >> >> Best regards, >> >> >> Sergey Ivanychev >> >>> 11 нояб. 2021 г., в 13:58, Mich Talebzadeh <mich.talebza...@gmail.com >>> <mailto:mich.talebza...@gmail.com>> написал(а): >>> >>> >>> Have you tried the following settings: >>> >>> spark.conf.set("spark.sql.execution.arrow.pysppark.enabled", "true") >>> spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled","true") >>> >>> HTH >>> >>> view my Linkedin profile >>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>> >>> 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 Thu, 4 Nov 2021 at 18:06, Mich Talebzadeh <mich.talebza...@gmail.com >>> <mailto:mich.talebza...@gmail.com>> wrote: >>> Ok so it boils down on how spark does create toPandas() DF under the >>> bonnet. How many executors are involved in k8s cluster. In this model spark >>> will create executors = no of nodes - 1 >>> >>> On Thu, 4 Nov 2021 at 17:42, Sergey Ivanychev <sergeyivanyc...@gmail.com >>> <mailto:sergeyivanyc...@gmail.com>> wrote: >>> > Just to confirm with Collect() alone, this is all on the driver? >>> >>> I shared the screenshot with the plan in the first email. In the collect() >>> case the data gets fetched to the driver without problems. >>> >>> Best regards, >>> >>> >>> Sergey Ivanychev >>> >>>> 4 нояб. 2021 г., в 20:37, Mich Talebzadeh <mich.talebza...@gmail.com >>>> <mailto:mich.talebza...@gmail.com>> написал(а): >>>> >>> >>>> Just to confirm with Collect() alone, this is all on the driver? >>> -- >>> >>> >>> view my Linkedin profile >>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>> >>> 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. >>> >
