Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
superb, when I said increase the number of partitions, I was implying
shuffle partitions because you are doing de duplicates by default I think
that should be around 200, which can create issues in case your data volume
is large.
I always prefer to SPARK SQL instead of SPARK dataframes. And the number of
records per file configuration should be mentioned in the following link as
maxrecordsperfile or something like that :
https://spark.apache.org/docs/latest/configuration.html#runtime-sql-configuration
.
Regards,
Gourav Sengupta
On Sat, Mar 5, 2022 at 5:09 PM Anil Dasari wrote:
> I am not sure how to set the records limit. Let me check. I couldn’t find
> parquet row group size configuration in spark.
>
> For now, I increased the number if shuffle partitions to reduce the
> records processed by task to avoid OOM.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Gourav Sengupta
> *Date: *Saturday, March 5, 2022 at 1:59 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> any chance you tried setting the limit on the number of records to be
> written out at a time?
>
>
>
> Regards,
>
> Gourav
>
>
>
> On Thu, Mar 3, 2022 at 3:12 PM Anil Dasari wrote:
>
> Hi Gourav,
>
> Tried increasing shuffle partitions number and higher executor memory.
> Both didn’t work.
>
>
>
> Regards
>
>
>
> *From: *Gourav Sengupta
> *Date: *Thursday, March 3, 2022 at 2:24 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi,
>
>
>
> I do not think that you are doing anything very particularly concerning
> here.
>
>
>
> There is a setting in SPARK which limits the number of records that we can
> write out at a time you can try that. The other thing that you can try is
> to ensure that the number of partitions are more (just like you suggested)
> let me know how things are giong on your end
>
>
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
>
>
> On Thu, Mar 3, 2022 at 8:37 AM Anil Dasari wrote:
>
> Answers in the context. Thanks.
>
>
>
> *From: *Gourav Sengupta
> *Date: *Thursday, March 3, 2022 at 12:13 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> I was trying to work out things for a while yesterday, but may need your
> kind help.
>
>
>
> Can you please share the code for the following steps?
>
> ·
> Create DF from hive (from step #c)
>
> [AD] sparkSession.table()
>
>
>
> · Deduplicate spark DF by primary key
>
> [AD] dataFrame.dropDuplicates()
>
>
>
> · Write DF to s3 in parquet format
>
> [AD] dataFrame.write.mode(saveMode).parquet(path)
>
>
>
> · Write metadata to s3
>
> [AD] metadata in json written to s3 using aws sdk
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
> On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari wrote:
>
> 2nd attempt..
>
>
>
> Any suggestions to troubleshoot and fix the problem ? thanks in advance.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Anil Dasari
> *Date: *Wednesday, March 2, 2022 at 7:00 AM
> *To: *Gourav Sengupta , Yang,Jie(INF) <
> yangji...@baidu.com>
> *Cc: *user@spark.apache.org
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Gourav and Yang
>
> Thanks for the response.
>
>
>
> Please find the answers below.
>
>
>
> 1. What is the version of SPARK you are using?
>
> [AD] : Spark 2.4.7 (EMR 5.33.1)
>
>
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> [AD] No. Only one new column is added. Our flow is
>
>1. Read avro data from kafka
>2. Avro deserialization and add new colum to RDD
>3. Create spark dataframe (DF) against to latest schema (avro evolved
>schema) and persist to hive (checkpointing)
>4. Create DF from hive (from step #c)
>5. Deduplicate spark DF by primary key
>6. Write DF to s3 in parquet format
>7. Write metadata to s3
>
>
>
> The failure is from spark batch job
>
>
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> [AD] : Data volume Is fixed as it is batch job.
>
>
>
> 4
Re: {EXT} Re: Spark Parquet write OOM
I am not sure how to set the records limit. Let me check. I couldn’t find
parquet row group size configuration in spark.
For now, I increased the number if shuffle partitions to reduce the records
processed by task to avoid OOM.
Regards,
Anil
From: Gourav Sengupta
Date: Saturday, March 5, 2022 at 1:59 AM
To: Anil Dasari
Cc: Yang,Jie(INF) , user@spark.apache.org
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
any chance you tried setting the limit on the number of records to be written
out at a time?
Regards,
Gourav
On Thu, Mar 3, 2022 at 3:12 PM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
Hi Gourav,
Tried increasing shuffle partitions number and higher executor memory. Both
didn’t work.
Regards
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Thursday, March 3, 2022 at 2:24 AM
To: Anil Dasari mailto:adas...@guidewire.com>>
Cc: Yang,Jie(INF) mailto:yangji...@baidu.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi,
I do not think that you are doing anything very particularly concerning here.
There is a setting in SPARK which limits the number of records that we can
write out at a time you can try that. The other thing that you can try is to
ensure that the number of partitions are more (just like you suggested) let me
know how things are giong on your end
Regards,
Gourav Sengupta
On Thu, Mar 3, 2022 at 8:37 AM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
Answers in the context. Thanks.
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Thursday, March 3, 2022 at 12:13 AM
To: Anil Dasari mailto:adas...@guidewire.com>>
Cc: Yang,Jie(INF) mailto:yangji...@baidu.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
I was trying to work out things for a while yesterday, but may need your kind
help.
Can you please share the code for the following steps?
*
Create DF from hive (from step #c)
[AD] sparkSession.table()
* Deduplicate spark DF by primary key
[AD] dataFrame.dropDuplicates()
* Write DF to s3 in parquet format
[AD] dataFrame.write.mode(saveMode).parquet(path)
* Write metadata to s3
[AD] metadata in json written to s3 using aws sdk
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
2nd attempt..
Any suggestions to troubleshoot and fix the problem ? thanks in advance.
Regards,
Anil
From: Anil Dasari mailto:adas...@guidewire.com>>
Date: Wednesday, March 2, 2022 at 7:00 AM
To: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>, Yang,Jie(INF)
mailto:yangji...@baidu.com>>
Cc: user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav and Yang
Thanks for the response.
Please find the answers below.
1. What is the version of SPARK you are using?
[AD] : Spark 2.4.7 (EMR 5.33.1)
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
[AD] No. Only one new column is added. Our flow is
1. Read avro data from kafka
2. Avro deserialization and add new colum to RDD
3. Create spark dataframe (DF) against to latest schema (avro evolved
schema) and persist to hive (checkpointing)
4. Create DF from hive (from step #c)
5. Deduplicate spark DF by primary key
6. Write DF to s3 in parquet format
7. Write metadata to s3
The failure is from spark batch job
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
[AD] : Data volume Is fixed as it is batch job.
4. What is the memory that you are having in your executors, and drivers?
[AD] We running one core node and 50 task nodes .. i.e total 51 nodes ..each
node can create 2 executors (2 core cpu and 8 gb memory)
5. Can you show the list of transformations that you are running ?
[AD] No explicit transformations other than basic map transformations required
to create dataframe from avor record rdd.
Please let me know if yo have any questions.
Regards,
Anil
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Wednesday, March 2, 2022 at 1:07 AM
To: Yang,Jie(INF) mailto:yangji...@baidu.com>>
Cc: Anil Dasari mailto:adas...@guidewire.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: {EXT} Re: Spark Parquet write OOM
Hi Anil,
before jumping to the quick symptomatic fix, can we try to understand the
issues?
1. What is the version of SPARK you are using?
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the si
Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
any chance you tried setting the limit on the number of records to be
written out at a time?
Regards,
Gourav
On Thu, Mar 3, 2022 at 3:12 PM Anil Dasari wrote:
> Hi Gourav,
>
> Tried increasing shuffle partitions number and higher executor memory.
> Both didn’t work.
>
>
>
> Regards
>
>
>
> *From: *Gourav Sengupta
> *Date: *Thursday, March 3, 2022 at 2:24 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi,
>
>
>
> I do not think that you are doing anything very particularly concerning
> here.
>
>
>
> There is a setting in SPARK which limits the number of records that we can
> write out at a time you can try that. The other thing that you can try is
> to ensure that the number of partitions are more (just like you suggested)
> let me know how things are giong on your end
>
>
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
>
>
> On Thu, Mar 3, 2022 at 8:37 AM Anil Dasari wrote:
>
> Answers in the context. Thanks.
>
>
>
> *From: *Gourav Sengupta
> *Date: *Thursday, March 3, 2022 at 12:13 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> I was trying to work out things for a while yesterday, but may need your
> kind help.
>
>
>
> Can you please share the code for the following steps?
>
> ·
> Create DF from hive (from step #c)
>
> [AD] sparkSession.table()
>
>
>
> · Deduplicate spark DF by primary key
>
> [AD] dataFrame.dropDuplicates()
>
>
>
> · Write DF to s3 in parquet format
>
> [AD] dataFrame.write.mode(saveMode).parquet(path)
>
>
>
> · Write metadata to s3
>
> [AD] metadata in json written to s3 using aws sdk
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
> On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari wrote:
>
> 2nd attempt..
>
>
>
> Any suggestions to troubleshoot and fix the problem ? thanks in advance.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Anil Dasari
> *Date: *Wednesday, March 2, 2022 at 7:00 AM
> *To: *Gourav Sengupta , Yang,Jie(INF) <
> yangji...@baidu.com>
> *Cc: *user@spark.apache.org
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Gourav and Yang
>
> Thanks for the response.
>
>
>
> Please find the answers below.
>
>
>
> 1. What is the version of SPARK you are using?
>
> [AD] : Spark 2.4.7 (EMR 5.33.1)
>
>
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> [AD] No. Only one new column is added. Our flow is
>
>1. Read avro data from kafka
>2. Avro deserialization and add new colum to RDD
>3. Create spark dataframe (DF) against to latest schema (avro evolved
>schema) and persist to hive (checkpointing)
>4. Create DF from hive (from step #c)
>5. Deduplicate spark DF by primary key
>6. Write DF to s3 in parquet format
>7. Write metadata to s3
>
>
>
> The failure is from spark batch job
>
>
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> [AD] : Data volume Is fixed as it is batch job.
>
>
>
> 4. What is the memory that you are having in your executors, and drivers?
>
> [AD] We running one core node and 50 task nodes .. i.e total 51 nodes
> ..each node can create 2 executors (2 core cpu and 8 gb memory)
>
>
>
> 5. Can you show the list of transformations that you are running ?
>
> [AD] No explicit transformations other than basic map transformations
> required to create dataframe from avor record rdd.
>
>
>
> Please let me know if yo have any questions.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Gourav Sengupta
> *Date: *Wednesday, March 2, 2022 at 1:07 AM
> *To: *Yang,Jie(INF)
> *Cc: *Anil Dasari , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *{EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> before jumping to the quick symptomatic fix, can we try to understand the
> issues?
>
>
>
> 1. What is the version of SPARK you are using?
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> 3. Is your pipeline going to change or evolve soon, or the da
Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav,
Tried increasing shuffle partitions number and higher executor memory. Both
didn’t work.
Regards
From: Gourav Sengupta
Date: Thursday, March 3, 2022 at 2:24 AM
To: Anil Dasari
Cc: Yang,Jie(INF) , user@spark.apache.org
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi,
I do not think that you are doing anything very particularly concerning here.
There is a setting in SPARK which limits the number of records that we can
write out at a time you can try that. The other thing that you can try is to
ensure that the number of partitions are more (just like you suggested) let me
know how things are giong on your end
Regards,
Gourav Sengupta
On Thu, Mar 3, 2022 at 8:37 AM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
Answers in the context. Thanks.
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Thursday, March 3, 2022 at 12:13 AM
To: Anil Dasari mailto:adas...@guidewire.com>>
Cc: Yang,Jie(INF) mailto:yangji...@baidu.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
I was trying to work out things for a while yesterday, but may need your kind
help.
Can you please share the code for the following steps?
*
Create DF from hive (from step #c)
[AD] sparkSession.table()
* Deduplicate spark DF by primary key
[AD] dataFrame.dropDuplicates()
* Write DF to s3 in parquet format
[AD] dataFrame.write.mode(saveMode).parquet(path)
* Write metadata to s3
[AD] metadata in json written to s3 using aws sdk
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
2nd attempt..
Any suggestions to troubleshoot and fix the problem ? thanks in advance.
Regards,
Anil
From: Anil Dasari mailto:adas...@guidewire.com>>
Date: Wednesday, March 2, 2022 at 7:00 AM
To: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>, Yang,Jie(INF)
mailto:yangji...@baidu.com>>
Cc: user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav and Yang
Thanks for the response.
Please find the answers below.
1. What is the version of SPARK you are using?
[AD] : Spark 2.4.7 (EMR 5.33.1)
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
[AD] No. Only one new column is added. Our flow is
1. Read avro data from kafka
2. Avro deserialization and add new colum to RDD
3. Create spark dataframe (DF) against to latest schema (avro evolved
schema) and persist to hive (checkpointing)
4. Create DF from hive (from step #c)
5. Deduplicate spark DF by primary key
6. Write DF to s3 in parquet format
7. Write metadata to s3
The failure is from spark batch job
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
[AD] : Data volume Is fixed as it is batch job.
4. What is the memory that you are having in your executors, and drivers?
[AD] We running one core node and 50 task nodes .. i.e total 51 nodes ..each
node can create 2 executors (2 core cpu and 8 gb memory)
5. Can you show the list of transformations that you are running ?
[AD] No explicit transformations other than basic map transformations required
to create dataframe from avor record rdd.
Please let me know if yo have any questions.
Regards,
Anil
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Wednesday, March 2, 2022 at 1:07 AM
To: Yang,Jie(INF) mailto:yangji...@baidu.com>>
Cc: Anil Dasari mailto:adas...@guidewire.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: {EXT} Re: Spark Parquet write OOM
Hi Anil,
before jumping to the quick symptomatic fix, can we try to understand the
issues?
1. What is the version of SPARK you are using?
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
4. What is the memory that you are having in your executors, and drivers?
5. Can you show the list of transformations that you are running ?
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF)
mailto:yangji...@baidu.com>> wrote:
This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase the
capacity of DirectByteBuffer size by configuring `-XX:MaxDirectMemorySize` and
this is a Java opts.
However, we'd better check the length of memory to be allocated, because
`-XX:MaxDirectMemorySize` and `-Xmx` should have the same capacity by default.
发件人: Anil Dasari mailto:adas...@guidewire.com>>
日期: 2022年3
Re: {EXT} Re: Spark Parquet write OOM
Hi,
I do not think that you are doing anything very particularly concerning
here.
There is a setting in SPARK which limits the number of records that we can
write out at a time you can try that. The other thing that you can try is
to ensure that the number of partitions are more (just like you suggested)
let me know how things are giong on your end
Regards,
Gourav Sengupta
On Thu, Mar 3, 2022 at 8:37 AM Anil Dasari wrote:
> Answers in the context. Thanks.
>
>
>
> *From: *Gourav Sengupta
> *Date: *Thursday, March 3, 2022 at 12:13 AM
> *To: *Anil Dasari
> *Cc: *Yang,Jie(INF) , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> I was trying to work out things for a while yesterday, but may need your
> kind help.
>
>
>
> Can you please share the code for the following steps?
>
> ·
> Create DF from hive (from step #c)
>
> [AD] sparkSession.table()
>
>
>
> · Deduplicate spark DF by primary key
>
> [AD] dataFrame.dropDuplicates()
>
>
>
> · Write DF to s3 in parquet format
>
> [AD] dataFrame.write.mode(saveMode).parquet(path)
>
>
>
> · Write metadata to s3
>
> [AD] metadata in json written to s3 using aws sdk
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
> On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari wrote:
>
> 2nd attempt..
>
>
>
> Any suggestions to troubleshoot and fix the problem ? thanks in advance.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Anil Dasari
> *Date: *Wednesday, March 2, 2022 at 7:00 AM
> *To: *Gourav Sengupta , Yang,Jie(INF) <
> yangji...@baidu.com>
> *Cc: *user@spark.apache.org
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Gourav and Yang
>
> Thanks for the response.
>
>
>
> Please find the answers below.
>
>
>
> 1. What is the version of SPARK you are using?
>
> [AD] : Spark 2.4.7 (EMR 5.33.1)
>
>
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> [AD] No. Only one new column is added. Our flow is
>
>1. Read avro data from kafka
>2. Avro deserialization and add new colum to RDD
>3. Create spark dataframe (DF) against to latest schema (avro evolved
>schema) and persist to hive (checkpointing)
>4. Create DF from hive (from step #c)
>5. Deduplicate spark DF by primary key
>6. Write DF to s3 in parquet format
>7. Write metadata to s3
>
>
>
> The failure is from spark batch job
>
>
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> [AD] : Data volume Is fixed as it is batch job.
>
>
>
> 4. What is the memory that you are having in your executors, and drivers?
>
> [AD] We running one core node and 50 task nodes .. i.e total 51 nodes
> ..each node can create 2 executors (2 core cpu and 8 gb memory)
>
>
>
> 5. Can you show the list of transformations that you are running ?
>
> [AD] No explicit transformations other than basic map transformations
> required to create dataframe from avor record rdd.
>
>
>
> Please let me know if yo have any questions.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Gourav Sengupta
> *Date: *Wednesday, March 2, 2022 at 1:07 AM
> *To: *Yang,Jie(INF)
> *Cc: *Anil Dasari , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *{EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> before jumping to the quick symptomatic fix, can we try to understand the
> issues?
>
>
>
> 1. What is the version of SPARK you are using?
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> 4. What is the memory that you are having in your executors, and drivers?
>
> 5. Can you show the list of transformations that you are running ?
>
>
>
>
>
>
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
>
>
> On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF) wrote:
>
> This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase
> the capacity of DirectByteBuffer size by configuring
> `-XX:MaxDirectMemorySize` and this is a Java opts.
>
>
>
> However, we'd better check the length of memory to be allocated, because
> `-XX:MaxDirectMemorySi
Re: {EXT} Re: Spark Parquet write OOM
Answers in the context. Thanks.
From: Gourav Sengupta
Date: Thursday, March 3, 2022 at 12:13 AM
To: Anil Dasari
Cc: Yang,Jie(INF) , user@spark.apache.org
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
I was trying to work out things for a while yesterday, but may need your kind
help.
Can you please share the code for the following steps?
*
Create DF from hive (from step #c)
[AD] sparkSession.table()
* Deduplicate spark DF by primary key
[AD] dataFrame.dropDuplicates()
* Write DF to s3 in parquet format
[AD] dataFrame.write.mode(saveMode).parquet(path)
* Write metadata to s3
[AD] metadata in json written to s3 using aws sdk
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari
mailto:adas...@guidewire.com>> wrote:
2nd attempt..
Any suggestions to troubleshoot and fix the problem ? thanks in advance.
Regards,
Anil
From: Anil Dasari mailto:adas...@guidewire.com>>
Date: Wednesday, March 2, 2022 at 7:00 AM
To: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>, Yang,Jie(INF)
mailto:yangji...@baidu.com>>
Cc: user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav and Yang
Thanks for the response.
Please find the answers below.
1. What is the version of SPARK you are using?
[AD] : Spark 2.4.7 (EMR 5.33.1)
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
[AD] No. Only one new column is added. Our flow is
1. Read avro data from kafka
2. Avro deserialization and add new colum to RDD
3. Create spark dataframe (DF) against to latest schema (avro evolved
schema) and persist to hive (checkpointing)
4. Create DF from hive (from step #c)
5. Deduplicate spark DF by primary key
6. Write DF to s3 in parquet format
7. Write metadata to s3
The failure is from spark batch job
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
[AD] : Data volume Is fixed as it is batch job.
4. What is the memory that you are having in your executors, and drivers?
[AD] We running one core node and 50 task nodes .. i.e total 51 nodes ..each
node can create 2 executors (2 core cpu and 8 gb memory)
5. Can you show the list of transformations that you are running ?
[AD] No explicit transformations other than basic map transformations required
to create dataframe from avor record rdd.
Please let me know if yo have any questions.
Regards,
Anil
From: Gourav Sengupta
mailto:gourav.sengu...@gmail.com>>
Date: Wednesday, March 2, 2022 at 1:07 AM
To: Yang,Jie(INF) mailto:yangji...@baidu.com>>
Cc: Anil Dasari mailto:adas...@guidewire.com>>,
user@spark.apache.org<mailto:user@spark.apache.org>
mailto:user@spark.apache.org>>
Subject: {EXT} Re: Spark Parquet write OOM
Hi Anil,
before jumping to the quick symptomatic fix, can we try to understand the
issues?
1. What is the version of SPARK you are using?
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
4. What is the memory that you are having in your executors, and drivers?
5. Can you show the list of transformations that you are running ?
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF)
mailto:yangji...@baidu.com>> wrote:
This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase the
capacity of DirectByteBuffer size by configuring `-XX:MaxDirectMemorySize` and
this is a Java opts.
However, we'd better check the length of memory to be allocated, because
`-XX:MaxDirectMemorySize` and `-Xmx` should have the same capacity by default.
发件人: Anil Dasari mailto:adas...@guidewire.com>>
日期: 2022年3月2日 星期三 09:45
收件人: "user@spark.apache.org<mailto:user@spark.apache.org>"
mailto:user@spark.apache.org>>
主题: Spark Parquet write OOM
Hello everyone,
We are writing Spark Data frame to s3 in parquet and it is failing with below
exception.
I wanted to try following to avoid OOM
1. increase the default sql shuffle partitions to reduce load on parquet
writer tasks to avoid OOM and
2. Increase user memory (reduce memory fraction) to have more memory for
other data structures assuming parquet writer uses user memory.
I am not sure if these fixes the OOM issue. So wanted to reach out community
for any suggestions. Please let me know.
Exception:
org.apache.spark.SparkException: Task failed while writing rows.
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:
Re: {EXT} Re: Spark Parquet write OOM
Hi Anil,
I was trying to work out things for a while yesterday, but may need your
kind help.
Can you please share the code for the following steps?
-
Create DF from hive (from step #c)
- Deduplicate spark DF by primary key
- Write DF to s3 in parquet format
- Write metadata to s3
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 11:25 PM Anil Dasari wrote:
> 2nd attempt..
>
>
>
> Any suggestions to troubleshoot and fix the problem ? thanks in advance.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Anil Dasari
> *Date: *Wednesday, March 2, 2022 at 7:00 AM
> *To: *Gourav Sengupta , Yang,Jie(INF) <
> yangji...@baidu.com>
> *Cc: *user@spark.apache.org
> *Subject: *Re: {EXT} Re: Spark Parquet write OOM
>
> Hi Gourav and Yang
>
> Thanks for the response.
>
>
>
> Please find the answers below.
>
>
>
> 1. What is the version of SPARK you are using?
>
> [AD] : Spark 2.4.7 (EMR 5.33.1)
>
>
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> [AD] No. Only one new column is added. Our flow is
>
>1. Read avro data from kafka
>2. Avro deserialization and add new colum to RDD
>3. Create spark dataframe (DF) against to latest schema (avro evolved
>schema) and persist to hive (checkpointing)
>4. Create DF from hive (from step #c)
>5. Deduplicate spark DF by primary key
>6. Write DF to s3 in parquet format
>7. Write metadata to s3
>
>
>
> The failure is from spark batch job
>
>
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> [AD] : Data volume Is fixed as it is batch job.
>
>
>
> 4. What is the memory that you are having in your executors, and drivers?
>
> [AD] We running one core node and 50 task nodes .. i.e total 51 nodes
> ..each node can create 2 executors (2 core cpu and 8 gb memory)
>
>
>
> 5. Can you show the list of transformations that you are running ?
>
> [AD] No explicit transformations other than basic map transformations
> required to create dataframe from avor record rdd.
>
>
>
> Please let me know if yo have any questions.
>
>
>
> Regards,
>
> Anil
>
>
>
> *From: *Gourav Sengupta
> *Date: *Wednesday, March 2, 2022 at 1:07 AM
> *To: *Yang,Jie(INF)
> *Cc: *Anil Dasari , user@spark.apache.org <
> user@spark.apache.org>
> *Subject: *{EXT} Re: Spark Parquet write OOM
>
> Hi Anil,
>
>
>
> before jumping to the quick symptomatic fix, can we try to understand the
> issues?
>
>
>
> 1. What is the version of SPARK you are using?
>
> 2. Are you doing a lot of in-memory transformations like adding columns,
> or running joins, or UDFs thus increasing the size of the data before
> writing out?
>
> 3. Is your pipeline going to change or evolve soon, or the data volumes
> going to vary, or particularly increase, over time?
>
> 4. What is the memory that you are having in your executors, and drivers?
>
> 5. Can you show the list of transformations that you are running ?
>
>
>
>
>
>
>
>
>
> Regards,
>
> Gourav Sengupta
>
>
>
>
>
> On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF) wrote:
>
> This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase
> the capacity of DirectByteBuffer size by configuring
> `-XX:MaxDirectMemorySize` and this is a Java opts.
>
>
>
> However, we'd better check the length of memory to be allocated, because
> `-XX:MaxDirectMemorySize` and `-Xmx` should have the same capacity by
> default.
>
>
>
>
>
> *发件人**: *Anil Dasari
> *日期**: *2022年3月2日 星期三 09:45
> *收件人**: *"user@spark.apache.org"
> *主题**: *Spark Parquet write OOM
>
>
>
> Hello everyone,
>
>
>
> We are writing Spark Data frame to s3 in parquet and it is failing with
> below exception.
>
>
>
> I wanted to try following to avoid OOM
>
>
>
>1. increase the default sql shuffle partitions to reduce load on
>parquet writer tasks to avoid OOM and
>2. Increase user memory (reduce memory fraction) to have more memory
>for other data structures assuming parquet writer uses user memory.
>
>
>
> I am not sure if these fixes the OOM issue. So wanted to reach out
> community for any suggestions. Please let me know.
>
>
>
> Exception:
>
>
>
> org.apache.spark.SparkException: Task failed while writing rows.
>
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql
Re: {EXT} Re: Spark Parquet write OOM
2nd attempt..
Any suggestions to troubleshoot and fix the problem ? thanks in advance.
Regards,
Anil
From: Anil Dasari
Date: Wednesday, March 2, 2022 at 7:00 AM
To: Gourav Sengupta , Yang,Jie(INF)
Cc: user@spark.apache.org
Subject: Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav and Yang
Thanks for the response.
Please find the answers below.
1. What is the version of SPARK you are using?
[AD] : Spark 2.4.7 (EMR 5.33.1)
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
[AD] No. Only one new column is added. Our flow is
1. Read avro data from kafka
2. Avro deserialization and add new colum to RDD
3. Create spark dataframe (DF) against to latest schema (avro evolved
schema) and persist to hive (checkpointing)
4. Create DF from hive (from step #c)
5. Deduplicate spark DF by primary key
6. Write DF to s3 in parquet format
7. Write metadata to s3
The failure is from spark batch job
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
[AD] : Data volume Is fixed as it is batch job.
4. What is the memory that you are having in your executors, and drivers?
[AD] We running one core node and 50 task nodes .. i.e total 51 nodes ..each
node can create 2 executors (2 core cpu and 8 gb memory)
5. Can you show the list of transformations that you are running ?
[AD] No explicit transformations other than basic map transformations required
to create dataframe from avor record rdd.
Please let me know if yo have any questions.
Regards,
Anil
From: Gourav Sengupta
Date: Wednesday, March 2, 2022 at 1:07 AM
To: Yang,Jie(INF)
Cc: Anil Dasari , user@spark.apache.org
Subject: {EXT} Re: Spark Parquet write OOM
Hi Anil,
before jumping to the quick symptomatic fix, can we try to understand the
issues?
1. What is the version of SPARK you are using?
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
4. What is the memory that you are having in your executors, and drivers?
5. Can you show the list of transformations that you are running ?
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF)
mailto:yangji...@baidu.com>> wrote:
This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase the
capacity of DirectByteBuffer size by configuring `-XX:MaxDirectMemorySize` and
this is a Java opts.
However, we'd better check the length of memory to be allocated, because
`-XX:MaxDirectMemorySize` and `-Xmx` should have the same capacity by default.
发件人: Anil Dasari mailto:adas...@guidewire.com>>
日期: 2022年3月2日 星期三 09:45
收件人: "user@spark.apache.org<mailto:user@spark.apache.org>"
mailto:user@spark.apache.org>>
主题: Spark Parquet write OOM
Hello everyone,
We are writing Spark Data frame to s3 in parquet and it is failing with below
exception.
I wanted to try following to avoid OOM
1. increase the default sql shuffle partitions to reduce load on parquet
writer tasks to avoid OOM and
2. Increase user memory (reduce memory fraction) to have more memory for
other data structures assuming parquet writer uses user memory.
I am not sure if these fixes the OOM issue. So wanted to reach out community
for any suggestions. Please let me know.
Exception:
org.apache.spark.SparkException: Task failed while writing rows.
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1405)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)
Caused by: java.lang.OutOfMemoryError
at sun.misc.Unsafe.allocateMemory(Native Method)
at
java.nio.DirectByteBuffer.(http://DirectByteBuffer.java:127<http://DirectByteBuffer.java:127>)
at java.nio.ByteB
Re: {EXT} Re: Spark Parquet write OOM
Hi Gourav and Yang
Thanks for the response.
Please find the answers below.
1. What is the version of SPARK you are using?
[AD] : Spark 2.4.7 (EMR 5.33.1)
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
[AD] No. Only one new column is added. Our flow is
1. Read avro data from kafka
2. Avro deserialization and add new colum to RDD
3. Create spark dataframe (DF) against to latest schema (avro evolved
schema) and persist to hive (checkpointing)
4. Create DF from hive (from step #c)
5. Deduplicate spark DF by primary key
6. Write DF to s3 in parquet format
7. Write metadata to s3
The failure is from spark batch job
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
[AD] : Data volume Is fixed as it is batch job.
4. What is the memory that you are having in your executors, and drivers?
[AD] We running one core node and 50 task nodes .. i.e total 51 nodes ..each
node can create 2 executors (2 core cpu and 8 gb memory)
5. Can you show the list of transformations that you are running ?
[AD] No explicit transformations other than basic map transformations required
to create dataframe from avor record rdd.
Please let me know if yo have any questions.
Regards,
Anil
From: Gourav Sengupta
Date: Wednesday, March 2, 2022 at 1:07 AM
To: Yang,Jie(INF)
Cc: Anil Dasari , user@spark.apache.org
Subject: {EXT} Re: Spark Parquet write OOM
Hi Anil,
before jumping to the quick symptomatic fix, can we try to understand the
issues?
1. What is the version of SPARK you are using?
2. Are you doing a lot of in-memory transformations like adding columns, or
running joins, or UDFs thus increasing the size of the data before writing out?
3. Is your pipeline going to change or evolve soon, or the data volumes going
to vary, or particularly increase, over time?
4. What is the memory that you are having in your executors, and drivers?
5. Can you show the list of transformations that you are running ?
Regards,
Gourav Sengupta
On Wed, Mar 2, 2022 at 3:18 AM Yang,Jie(INF)
mailto:yangji...@baidu.com>> wrote:
This is a DirectByteBuffer OOM，so plan 2 may not work, we can increase the
capacity of DirectByteBuffer size by configuring `-XX:MaxDirectMemorySize` and
this is a Java opts.
However, we'd better check the length of memory to be allocated, because
`-XX:MaxDirectMemorySize` and `-Xmx` should have the same capacity by default.
发件人: Anil Dasari mailto:adas...@guidewire.com>>
日期: 2022年3月2日 星期三 09:45
收件人: "user@spark.apache.org"
mailto:user@spark.apache.org>>
主题: Spark Parquet write OOM
Hello everyone,
We are writing Spark Data frame to s3 in parquet and it is failing with below
exception.
I wanted to try following to avoid OOM
1. increase the default sql shuffle partitions to reduce load on parquet
writer tasks to avoid OOM and
2. Increase user memory (reduce memory fraction) to have more memory for
other data structures assuming parquet writer uses user memory.
I am not sure if these fixes the OOM issue. So wanted to reach out community
for any suggestions. Please let me know.
Exception:
org.apache.spark.SparkException: Task failed while writing rows.
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1405)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)
Caused by: java.lang.OutOfMemoryError
at sun.misc.Unsafe.allocateMemory(Native Method)
at
java.nio.DirectByteBuffer.(http://DirectByteBuffer.java:127)
at java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:311)
at
org.apache.parquet.hadoop.codec.SnappyCompressor.setInput(SnappyCompressor.java:97)
at
org.apache.parquet.hadoop.codec.NonBlockedCompressorStream.write(NonBlockedCompressorStream.java:48)
at
