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> 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> > 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> > 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> >> 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> >> 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> >>> 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> >>>> 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. >>> >>> >>> >> >
