Best to check this in Spark GUI under storage and see what is causing the issue.
HTH LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* *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 Mon, 19 Oct 2020 at 19:12, Lalwani, Jayesh <jlalw...@amazon.com.invalid> wrote: > I was caching it because I didn't want to re-execute the DAG when I ran > the count query. If you have a spark application with multiple actions, > Spark reexecutes the entire DAG for each action unless there is a cache in > between. I was trying to avoid reloading 1/2 a terabyte of data. Also, > cache should use up executor memory, not driver memory. > > As it turns out cache was the problem. I didn't expect cache to take > Executor memory and spill over to disk. I don't know why it's taking driver > memory. The input data has millions of partitions which results in millions > of tasks. Perhaps the high memory usage is a side effect of caching the > results of lots of tasks. > > On 10/19/20, 1:27 PM, "Nicolas Paris" <nicolas.pa...@riseup.net> wrote: > > CAUTION: This email originated from outside of the organization. Do > not click links or open attachments unless you can confirm the sender and > know the content is safe. > > > > > Before I write the data frame to parquet, I do df.cache. After > writing > > the file out, I do df.countDistinct(“a”, “b”, “c”).collect() > if you write the df to parquet, why would you also cache it ? caching > by > default loads the memory. this might affect later use, such > collect. the resulting GC can be explained by both caching and collect > > > Lalwani, Jayesh <jlalw...@amazon.com.INVALID> writes: > > > I have a Dataframe with around 6 billion rows, and about 20 columns. > First of all, I want to write this dataframe out to parquet. The, Out of > the 20 columns, I have 3 columns of interest, and I want to find how many > distinct values of the columns are there in the file. I don’t need the > actual distinct values. I just need the count. I knoe that there are around > 10-16million distinct values > > > > Before I write the data frame to parquet, I do df.cache. After > writing the file out, I do df.countDistinct(“a”, “b”, “c”).collect() > > > > When I run this, I see that the memory usage on my driver steadily > increases until it starts getting future time outs. I guess it’s spending > time in GC. Does countDistinct cause this behavior? Does Spark try to get > all 10 million distinct values into the driver? Is countDistinct not > recommended for data frames with large number of distinct values? > > > > What’s the solution? Should I use approx._count_distinct? > > > -- > nicolas paris > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > > >
