[jira] [Updated] (SPARK-6334) spark-local dir not getting cleared during ALS
[
https://issues.apache.org/jira/browse/SPARK-6334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antony Mayi updated SPARK-6334:
---
Attachment: gc.png
spark-local dir not getting cleared during ALS
--
Key: SPARK-6334
URL: https://issues.apache.org/jira/browse/SPARK-6334
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.2.0
Reporter: Antony Mayi
Attachments: als-diskusage.png, gc.png
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about
6 hours the ALS will consume all 12TB of disk space in local-dir data and
gets killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using
37 executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all
eaten from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6334) spark-local dir not getting cleared during ALS
[
https://issues.apache.org/jira/browse/SPARK-6334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antony Mayi updated SPARK-6334:
---
Attachment: als-diskusage.png
this is the disk usage pattern during ALS - 90% is when YARN kills the
container:
!als-diskusage.png!
spark-local dir not getting cleared during ALS
--
Key: SPARK-6334
URL: https://issues.apache.org/jira/browse/SPARK-6334
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.2.0
Reporter: Antony Mayi
Attachments: als-diskusage.png
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of disk space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code:python}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about
6 hours the ALS will consume all 12TB of disk space in local-dir data and
gets killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using
37 executors of 4 cores/28+4GB RAM each. if possible I'll try attaching the
graph of disk consumption pattern showing the space being all eaten from 7%
to 90% during the ALS.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6334) spark-local dir not getting cleared during ALS
[
https://issues.apache.org/jira/browse/SPARK-6334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antony Mayi updated SPARK-6334:
---
Description:
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of space (in my case I have 12TB of available disk capacity before kicking
off the ALS but it all gets used (and yarn kills the containers when reaching
90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about 6
hours the ALS will consume all 12TB of disk space in local-dir data and gets
killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using 37
executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all eaten
from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
was:
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of disk space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code:python}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about 6
hours the ALS will consume all 12TB of disk space in local-dir data and gets
killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using 37
executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all eaten
from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
spark-local dir not getting cleared during ALS
--
Key: SPARK-6334
URL: https://issues.apache.org/jira/browse/SPARK-6334
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.2.0
Reporter: Antony Mayi
Attachments: als-diskusage.png
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about
6 hours the ALS will consume all 12TB of disk space in local-dir data and
gets killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using
37 executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all
eaten from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-6334) spark-local dir not getting cleared during ALS
[
https://issues.apache.org/jira/browse/SPARK-6334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antony Mayi updated SPARK-6334:
---
Description:
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of disk space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code:python}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about 6
hours the ALS will consume all 12TB of disk space in local-dir data and gets
killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using 37
executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all eaten
from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
was:
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of disk space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code:python}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about 6
hours the ALS will consume all 12TB of disk space in local-dir data and gets
killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using 37
executors of 4 cores/28+4GB RAM each. if possible I'll try attaching the graph
of disk consumption pattern showing the space being all eaten from 7% to 90%
during the ALS.
spark-local dir not getting cleared during ALS
--
Key: SPARK-6334
URL: https://issues.apache.org/jira/browse/SPARK-6334
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.2.0
Reporter: Antony Mayi
Attachments: als-diskusage.png
when running bigger ALS training spark spills loads of temp data into the
local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
out of disk space (in my case I have 12TB of available disk capacity before
kicking off the ALS but it all gets used (and yarn kills the containers when
reaching 90%).
even with all recommended options (configuring checkpointing and forcing GC
when possible) it still doesn't get cleared.
here is my (pseudo)code (pyspark):
{code:python}
sc.setCheckpointDir('/tmp')
training =
sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
sc._jvm.System.gc()
{code}
the training RDD has about 3.5 billions of items (~60GB on disk). after about
6 hours the ALS will consume all 12TB of disk space in local-dir data and
gets killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using
37 executors of 4 cores/28+4GB RAM each.
this is the graph of disk consumption pattern showing the space being all
eaten from 7% to 90% during the ALS (90% is when YARN kills the container):
!als-diskusage.png!
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
