Re: Spark on Mesos / Executor Memory

[Bharath Ravi Kumar]

David, Tom,

Thanks for the explanation. This confirms my suspicion that the executor
was holding on to memory  regardless of tasks in execution once it expands
to occupy memory in keeping with spark.executor.memory. There certainly is
scope for improvement here, though I realize there will substantial
overheads in implementing memory release without compromising RDD caching
and similar aspects. I'll explore alternatives / workarounds meanwhile.

Thanks,
Bharath


On Sat, Oct 17, 2015 at 3:33 PM, Tom Arnfeld  wrote:

> Hi Bharath,
>
> When running jobs in fine grained mode, each Spark task is sent to mesos
> as a task which allows the offers system to maintain fairness between
> different spark application (as you've described). Having said that, unless
> your memory per-node is hugely undersubscribed when running these jobs in
> parallel. This behaviour matches exactly what you've described.
>
> What you're seeing happens because even though there's a new mesos task
> for each Spark task (allowing CPU to be shared) the Spark executors don't
> get killed even when they aren't doing any work, which means the memory
> isn't released. The JVM doesn't allow for flexible memory re-allocation (as
> far as i'm aware) which make it impossible for spark to dynamically scale
> up the memory of the executor over time as tasks start and finish.
>
> As Dave pointed out, the simplest way to solve this is to use a higher
> level tool that can run your spark jobs through one mesos framework and
> then you can let spark distribute the resources more effectively.
>
> I hope that helps!
>
> Tom.
>
> On 17 Oct 2015, at 06:47, Bharath Ravi Kumar  wrote:
>
> Can someone respond if you're aware of the reason for such a memory
> footprint? It seems unintuitive and hard to reason about.
>
> Thanks,
> Bharath
>
> On Thu, Oct 15, 2015 at 12:29 PM, Bharath Ravi Kumar 
> wrote:
>
>> Resending since user@mesos bounced earlier. My apologies.
>>
>> On Thu, Oct 15, 2015 at 12:19 PM, Bharath Ravi Kumar > > wrote:
>>
>>> (Reviving this thread since I ran into similar issues...)
>>>
>>> I'm running two spark jobs (in mesos fine grained mode), each belonging
>>> to a different mesos role, say low and high. The low:high mesos weights are
>>> 1:10. On expected lines, I see that the low priority job occupies cluster
>>> resources to the maximum extent when running alone. However, when the high
>>> priority job is submitted, it does not start and continues to await cluster
>>> resources (as seen in the logs). Since the jobs run in fine grained mode
>>> and the low priority tasks begin to finish, the high priority job should
>>> ideally be able to start and gradually take over cluster resources as per
>>> the weights. However, I noticed that while the "low" job gives up CPU cores
>>> with each completing task (e.g. reduction from 72 -> 12 with default
>>> parallelism set to 72), the memory resources are held on (~500G out of
>>> 768G). The spark.executor.memory setting appears to directly impact the
>>> amount of memory that the job holds on to. In this case, it was set to 200G
>>> in the low priority task and 100G in the high priority task. The nature of
>>> these jobs is such that setting the numbers to smaller values (say 32g)
>>> resulted in job failures with outofmemoryerror.  It appears that the spark
>>> framework is retaining memory (across tasks)  proportional to
>>> spark.executor.memory for the duration of the job and not releasing memory
>>> as tasks complete. This defeats the purpose of fine grained mode execution
>>> as the memory occupancy is preventing the high priority job from accepting
>>> the prioritized cpu offers and beginning execution. Can this be explained /
>>> documented better please?
>>>
>>> Thanks,
>>> Bharath
>>>
>>> On Sat, Apr 11, 2015 at 10:59 PM, Tim Chen  wrote:
>>>
 (Adding spark user list)

 Hi Tom,

 If I understand correctly you're saying that you're running into memory
 problems because the scheduler is allocating too much CPUs and not enough
 memory to acoomodate them right?

 In the case of fine grain mode I don't think that's a problem since we
 have a fixed amount of CPU and memory per task.
 However, in coarse grain you can run into that problem if you're with
 in the spark.cores.max limit, and memory is a fixed number.

 I have a patch out to configure how much max cpus should coarse grain
 executor use, and it also allows multiple executors in coarse grain mode.
 So you could say try to launch multiples of max 4 cores with
 spark.executor.memory (+ overhead and etc) in a slave. (
 https://github.com/apache/spark/pull/4027)

 It also might be interesting to include a cores to memory multiplier so
 that with a larger amount of cores we try to scale the memory with some
 factor, but I'm not entirely sure that's 

Re: Spark on Mesos / Executor Memory

[Bharath Ravi Kumar]

To be precise, the MesosExecutorBackend's Xms & Xmx equal
spark.executor.memory. So there's no question of expanding or contracting
the memory held by the executor.

On Sat, Oct 17, 2015 at 5:38 PM, Bharath Ravi Kumar 
wrote:

> David, Tom,
>
> Thanks for the explanation. This confirms my suspicion that the executor
> was holding on to memory  regardless of tasks in execution once it expands
> to occupy memory in keeping with spark.executor.memory. There certainly is
> scope for improvement here, though I realize there will substantial
> overheads in implementing memory release without compromising RDD caching
> and similar aspects. I'll explore alternatives / workarounds meanwhile.
>
> Thanks,
> Bharath
>
>
>
> On Sat, Oct 17, 2015 at 3:33 PM, Tom Arnfeld  wrote:
>
>> Hi Bharath,
>>
>> When running jobs in fine grained mode, each Spark task is sent to mesos
>> as a task which allows the offers system to maintain fairness between
>> different spark application (as you've described). Having said that, unless
>> your memory per-node is hugely undersubscribed when running these jobs in
>> parallel. This behaviour matches exactly what you've described.
>>
>> What you're seeing happens because even though there's a new mesos task
>> for each Spark task (allowing CPU to be shared) the Spark executors don't
>> get killed even when they aren't doing any work, which means the memory
>> isn't released. The JVM doesn't allow for flexible memory re-allocation (as
>> far as i'm aware) which make it impossible for spark to dynamically scale
>> up the memory of the executor over time as tasks start and finish.
>>
>> As Dave pointed out, the simplest way to solve this is to use a higher
>> level tool that can run your spark jobs through one mesos framework and
>> then you can let spark distribute the resources more effectively.
>>
>> I hope that helps!
>>
>> Tom.
>>
>> On 17 Oct 2015, at 06:47, Bharath Ravi Kumar  wrote:
>>
>> Can someone respond if you're aware of the reason for such a memory
>> footprint? It seems unintuitive and hard to reason about.
>>
>> Thanks,
>> Bharath
>>
>> On Thu, Oct 15, 2015 at 12:29 PM, Bharath Ravi Kumar > > wrote:
>>
>>> Resending since user@mesos bounced earlier. My apologies.
>>>
>>> On Thu, Oct 15, 2015 at 12:19 PM, Bharath Ravi Kumar <
>>> reachb...@gmail.com> wrote:
>>>
 (Reviving this thread since I ran into similar issues...)

 I'm running two spark jobs (in mesos fine grained mode), each belonging
 to a different mesos role, say low and high. The low:high mesos weights are
 1:10. On expected lines, I see that the low priority job occupies cluster
 resources to the maximum extent when running alone. However, when the high
 priority job is submitted, it does not start and continues to await cluster
 resources (as seen in the logs). Since the jobs run in fine grained mode
 and the low priority tasks begin to finish, the high priority job should
 ideally be able to start and gradually take over cluster resources as per
 the weights. However, I noticed that while the "low" job gives up CPU cores
 with each completing task (e.g. reduction from 72 -> 12 with default
 parallelism set to 72), the memory resources are held on (~500G out of
 768G). The spark.executor.memory setting appears to directly impact the
 amount of memory that the job holds on to. In this case, it was set to 200G
 in the low priority task and 100G in the high priority task. The nature of
 these jobs is such that setting the numbers to smaller values (say 32g)
 resulted in job failures with outofmemoryerror.  It appears that the spark
 framework is retaining memory (across tasks)  proportional to
 spark.executor.memory for the duration of the job and not releasing memory
 as tasks complete. This defeats the purpose of fine grained mode execution
 as the memory occupancy is preventing the high priority job from accepting
 the prioritized cpu offers and beginning execution. Can this be explained /
 documented better please?

 Thanks,
 Bharath

 On Sat, Apr 11, 2015 at 10:59 PM, Tim Chen  wrote:

> (Adding spark user list)
>
> Hi Tom,
>
> If I understand correctly you're saying that you're running into
> memory problems because the scheduler is allocating too much CPUs and not
> enough memory to acoomodate them right?
>
> In the case of fine grain mode I don't think that's a problem since we
> have a fixed amount of CPU and memory per task.
> However, in coarse grain you can run into that problem if you're with
> in the spark.cores.max limit, and memory is a fixed number.
>
> I have a patch out to configure how much max cpus should coarse grain
> executor use, and it also allows multiple executors in coarse grain mode.
> So you could say try 

Re: Spark on Mesos / Executor Memory

[Bharath Ravi Kumar]

Can someone respond if you're aware of the reason for such a memory
footprint? It seems unintuitive and hard to reason about.

Thanks,
Bharath

On Thu, Oct 15, 2015 at 12:29 PM, Bharath Ravi Kumar 
wrote:

> Resending since user@mesos bounced earlier. My apologies.
>
> On Thu, Oct 15, 2015 at 12:19 PM, Bharath Ravi Kumar 
> wrote:
>
>> (Reviving this thread since I ran into similar issues...)
>>
>> I'm running two spark jobs (in mesos fine grained mode), each belonging
>> to a different mesos role, say low and high. The low:high mesos weights are
>> 1:10. On expected lines, I see that the low priority job occupies cluster
>> resources to the maximum extent when running alone. However, when the high
>> priority job is submitted, it does not start and continues to await cluster
>> resources (as seen in the logs). Since the jobs run in fine grained mode
>> and the low priority tasks begin to finish, the high priority job should
>> ideally be able to start and gradually take over cluster resources as per
>> the weights. However, I noticed that while the "low" job gives up CPU cores
>> with each completing task (e.g. reduction from 72 -> 12 with default
>> parallelism set to 72), the memory resources are held on (~500G out of
>> 768G). The spark.executor.memory setting appears to directly impact the
>> amount of memory that the job holds on to. In this case, it was set to 200G
>> in the low priority task and 100G in the high priority task. The nature of
>> these jobs is such that setting the numbers to smaller values (say 32g)
>> resulted in job failures with outofmemoryerror.  It appears that the spark
>> framework is retaining memory (across tasks)  proportional to
>> spark.executor.memory for the duration of the job and not releasing memory
>> as tasks complete. This defeats the purpose of fine grained mode execution
>> as the memory occupancy is preventing the high priority job from accepting
>> the prioritized cpu offers and beginning execution. Can this be explained /
>> documented better please?
>>
>> Thanks,
>> Bharath
>>
>> On Sat, Apr 11, 2015 at 10:59 PM, Tim Chen  wrote:
>>
>>> (Adding spark user list)
>>>
>>> Hi Tom,
>>>
>>> If I understand correctly you're saying that you're running into memory
>>> problems because the scheduler is allocating too much CPUs and not enough
>>> memory to acoomodate them right?
>>>
>>> In the case of fine grain mode I don't think that's a problem since we
>>> have a fixed amount of CPU and memory per task.
>>> However, in coarse grain you can run into that problem if you're with in
>>> the spark.cores.max limit, and memory is a fixed number.
>>>
>>> I have a patch out to configure how much max cpus should coarse grain
>>> executor use, and it also allows multiple executors in coarse grain mode.
>>> So you could say try to launch multiples of max 4 cores with
>>> spark.executor.memory (+ overhead and etc) in a slave. (
>>> https://github.com/apache/spark/pull/4027)
>>>
>>> It also might be interesting to include a cores to memory multiplier so
>>> that with a larger amount of cores we try to scale the memory with some
>>> factor, but I'm not entirely sure that's intuitive to use and what people
>>> know what to set it to, as that can likely change with different workload.
>>>
>>> Tim
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Sat, Apr 11, 2015 at 9:51 AM, Tom Arnfeld  wrote:
>>>
 We're running Spark 1.3.0 (with a couple of patches over the top for
 docker related bits).

 I don't think SPARK-4158 is related to what we're seeing, things do run
 fine on the cluster, given a ridiculously large executor memory
 configuration. As for SPARK-3535 although that looks useful I think we'e
 seeing something else.

 Put a different way, the amount of memory required at any given time by
 the spark JVM process is directly proportional to the amount of CPU it has,
 because more CPU means more tasks and more tasks means more memory. Even if
 we're using coarse mode, the amount of executor memory should be
 proportionate to the amount of CPUs in the offer.

 On 11 April 2015 at 17:39, Brenden Matthews 
 wrote:

> I ran into some issues with it a while ago, and submitted a couple PRs
> to fix it:
>
> https://github.com/apache/spark/pull/2401
> https://github.com/apache/spark/pull/3024
>
> Do these look relevant? What version of Spark are you running?
>
> On Sat, Apr 11, 2015 at 9:33 AM, Tom Arnfeld  wrote:
>
>> Hey,
>>
>> Not sure whether it's best to ask this on the spark mailing list or
>> the mesos one, so I'll try here first :-)
>>
>> I'm having a bit of trouble with out of memory errors in my spark
>> jobs... it seems fairly odd to me that memory resources can only be set 
>> at
>> the executor level, and not also at the task 

Re: Spark on Mesos / Executor Memory

[Bharath Ravi Kumar]

Resending since user@mesos bounced earlier. My apologies.

On Thu, Oct 15, 2015 at 12:19 PM, Bharath Ravi Kumar 
wrote:

> (Reviving this thread since I ran into similar issues...)
>
> I'm running two spark jobs (in mesos fine grained mode), each belonging to
> a different mesos role, say low and high. The low:high mesos weights are
> 1:10. On expected lines, I see that the low priority job occupies cluster
> resources to the maximum extent when running alone. However, when the high
> priority job is submitted, it does not start and continues to await cluster
> resources (as seen in the logs). Since the jobs run in fine grained mode
> and the low priority tasks begin to finish, the high priority job should
> ideally be able to start and gradually take over cluster resources as per
> the weights. However, I noticed that while the "low" job gives up CPU cores
> with each completing task (e.g. reduction from 72 -> 12 with default
> parallelism set to 72), the memory resources are held on (~500G out of
> 768G). The spark.executor.memory setting appears to directly impact the
> amount of memory that the job holds on to. In this case, it was set to 200G
> in the low priority task and 100G in the high priority task. The nature of
> these jobs is such that setting the numbers to smaller values (say 32g)
> resulted in job failures with outofmemoryerror.  It appears that the spark
> framework is retaining memory (across tasks)  proportional to
> spark.executor.memory for the duration of the job and not releasing memory
> as tasks complete. This defeats the purpose of fine grained mode execution
> as the memory occupancy is preventing the high priority job from accepting
> the prioritized cpu offers and beginning execution. Can this be explained /
> documented better please?
>
> Thanks,
> Bharath
>
> On Sat, Apr 11, 2015 at 10:59 PM, Tim Chen  wrote:
>
>> (Adding spark user list)
>>
>> Hi Tom,
>>
>> If I understand correctly you're saying that you're running into memory
>> problems because the scheduler is allocating too much CPUs and not enough
>> memory to acoomodate them right?
>>
>> In the case of fine grain mode I don't think that's a problem since we
>> have a fixed amount of CPU and memory per task.
>> However, in coarse grain you can run into that problem if you're with in
>> the spark.cores.max limit, and memory is a fixed number.
>>
>> I have a patch out to configure how much max cpus should coarse grain
>> executor use, and it also allows multiple executors in coarse grain mode.
>> So you could say try to launch multiples of max 4 cores with
>> spark.executor.memory (+ overhead and etc) in a slave. (
>> https://github.com/apache/spark/pull/4027)
>>
>> It also might be interesting to include a cores to memory multiplier so
>> that with a larger amount of cores we try to scale the memory with some
>> factor, but I'm not entirely sure that's intuitive to use and what people
>> know what to set it to, as that can likely change with different workload.
>>
>> Tim
>>
>>
>>
>>
>>
>>
>>
>> On Sat, Apr 11, 2015 at 9:51 AM, Tom Arnfeld  wrote:
>>
>>> We're running Spark 1.3.0 (with a couple of patches over the top for
>>> docker related bits).
>>>
>>> I don't think SPARK-4158 is related to what we're seeing, things do run
>>> fine on the cluster, given a ridiculously large executor memory
>>> configuration. As for SPARK-3535 although that looks useful I think we'e
>>> seeing something else.
>>>
>>> Put a different way, the amount of memory required at any given time by
>>> the spark JVM process is directly proportional to the amount of CPU it has,
>>> because more CPU means more tasks and more tasks means more memory. Even if
>>> we're using coarse mode, the amount of executor memory should be
>>> proportionate to the amount of CPUs in the offer.
>>>
>>> On 11 April 2015 at 17:39, Brenden Matthews 
>>> wrote:
>>>
 I ran into some issues with it a while ago, and submitted a couple PRs
 to fix it:

 https://github.com/apache/spark/pull/2401
 https://github.com/apache/spark/pull/3024

 Do these look relevant? What version of Spark are you running?

 On Sat, Apr 11, 2015 at 9:33 AM, Tom Arnfeld  wrote:

> Hey,
>
> Not sure whether it's best to ask this on the spark mailing list or
> the mesos one, so I'll try here first :-)
>
> I'm having a bit of trouble with out of memory errors in my spark
> jobs... it seems fairly odd to me that memory resources can only be set at
> the executor level, and not also at the task level. For example, as far as
> I can tell there's only a *spark.executor.memory* config option.
>
> Surely the memory requirements of a single executor are quite
> dramatically influenced by the number of concurrent tasks running? Given a
> shared cluster, I have no idea what % of an individual slave my executor 
> is

Re: Spark on Mesos / Executor Memory

[Bharath Ravi Kumar]

(Reviving this thread since I ran into similar issues...)

I'm running two spark jobs (in mesos fine grained mode), each belonging to
a different mesos role, say low and high. The low:high mesos weights are
1:10. On expected lines, I see that the low priority job occupies cluster
resources to the maximum extent when running alone. However, when the high
priority job is submitted, it does not start and continues to await cluster
resources (as seen in the logs). Since the jobs run in fine grained mode
and the low priority tasks begin to finish, the high priority job should
ideally be able to start and gradually take over cluster resources as per
the weights. However, I noticed that while the "low" job gives up CPU cores
with each completing task (e.g. reduction from 72 -> 12 with default
parallelism set to 72), the memory resources are held on (~500G out of
768G). The spark.executor.memory setting appears to directly impact the
amount of memory that the job holds on to. In this case, it was set to 200G
in the low priority task and 100G in the high priority task. The nature of
these jobs is such that setting the numbers to smaller values (say 32g)
resulted in job failures with outofmemoryerror.  It appears that the spark
framework is retaining memory (across tasks)  proportional to
spark.executor.memory for the duration of the job and not releasing memory
as tasks complete. This defeats the purpose of fine grained mode execution
as the memory occupancy is preventing the high priority job from accepting
the prioritized cpu offers and beginning execution. Can this be explained /
documented better please?

Thanks,
Bharath

On Sat, Apr 11, 2015 at 10:59 PM, Tim Chen  wrote:

> (Adding spark user list)
>
> Hi Tom,
>
> If I understand correctly you're saying that you're running into memory
> problems because the scheduler is allocating too much CPUs and not enough
> memory to acoomodate them right?
>
> In the case of fine grain mode I don't think that's a problem since we
> have a fixed amount of CPU and memory per task.
> However, in coarse grain you can run into that problem if you're with in
> the spark.cores.max limit, and memory is a fixed number.
>
> I have a patch out to configure how much max cpus should coarse grain
> executor use, and it also allows multiple executors in coarse grain mode.
> So you could say try to launch multiples of max 4 cores with
> spark.executor.memory (+ overhead and etc) in a slave. (
> https://github.com/apache/spark/pull/4027)
>
> It also might be interesting to include a cores to memory multiplier so
> that with a larger amount of cores we try to scale the memory with some
> factor, but I'm not entirely sure that's intuitive to use and what people
> know what to set it to, as that can likely change with different workload.
>
> Tim
>
>
>
>
>
>
>
> On Sat, Apr 11, 2015 at 9:51 AM, Tom Arnfeld  wrote:
>
>> We're running Spark 1.3.0 (with a couple of patches over the top for
>> docker related bits).
>>
>> I don't think SPARK-4158 is related to what we're seeing, things do run
>> fine on the cluster, given a ridiculously large executor memory
>> configuration. As for SPARK-3535 although that looks useful I think we'e
>> seeing something else.
>>
>> Put a different way, the amount of memory required at any given time by
>> the spark JVM process is directly proportional to the amount of CPU it has,
>> because more CPU means more tasks and more tasks means more memory. Even if
>> we're using coarse mode, the amount of executor memory should be
>> proportionate to the amount of CPUs in the offer.
>>
>> On 11 April 2015 at 17:39, Brenden Matthews  wrote:
>>
>>> I ran into some issues with it a while ago, and submitted a couple PRs
>>> to fix it:
>>>
>>> https://github.com/apache/spark/pull/2401
>>> https://github.com/apache/spark/pull/3024
>>>
>>> Do these look relevant? What version of Spark are you running?
>>>
>>> On Sat, Apr 11, 2015 at 9:33 AM, Tom Arnfeld  wrote:
>>>
 Hey,

 Not sure whether it's best to ask this on the spark mailing list or the
 mesos one, so I'll try here first :-)

 I'm having a bit of trouble with out of memory errors in my spark
 jobs... it seems fairly odd to me that memory resources can only be set at
 the executor level, and not also at the task level. For example, as far as
 I can tell there's only a *spark.executor.memory* config option.

 Surely the memory requirements of a single executor are quite
 dramatically influenced by the number of concurrent tasks running? Given a
 shared cluster, I have no idea what % of an individual slave my executor is
 going to get, so I basically have to set the executor memory to a value
 that's correct when the whole machine is in use...

 Has anyone else running Spark on Mesos come across this, or maybe
 someone could correct my understanding of the config options?

Re: Spark on Mesos / Executor Memory

[Tim Chen]

(Adding spark user list)

Hi Tom,

If I understand correctly you're saying that you're running into memory
problems because the scheduler is allocating too much CPUs and not enough
memory to acoomodate them right?

In the case of fine grain mode I don't think that's a problem since we have
a fixed amount of CPU and memory per task.
However, in coarse grain you can run into that problem if you're with in
the spark.cores.max limit, and memory is a fixed number.

I have a patch out to configure how much max cpus should coarse grain
executor use, and it also allows multiple executors in coarse grain mode.
So you could say try to launch multiples of max 4 cores with
spark.executor.memory (+ overhead and etc) in a slave. (
https://github.com/apache/spark/pull/4027)

It also might be interesting to include a cores to memory multiplier so
that with a larger amount of cores we try to scale the memory with some
factor, but I'm not entirely sure that's intuitive to use and what people
know what to set it to, as that can likely change with different workload.

Tim







On Sat, Apr 11, 2015 at 9:51 AM, Tom Arnfeld t...@duedil.com wrote:

 We're running Spark 1.3.0 (with a couple of patches over the top for
 docker related bits).

 I don't think SPARK-4158 is related to what we're seeing, things do run
 fine on the cluster, given a ridiculously large executor memory
 configuration. As for SPARK-3535 although that looks useful I think we'e
 seeing something else.

 Put a different way, the amount of memory required at any given time by
 the spark JVM process is directly proportional to the amount of CPU it has,
 because more CPU means more tasks and more tasks means more memory. Even if
 we're using coarse mode, the amount of executor memory should be
 proportionate to the amount of CPUs in the offer.

 On 11 April 2015 at 17:39, Brenden Matthews bren...@diddyinc.com wrote:

 I ran into some issues with it a while ago, and submitted a couple PRs to
 fix it:

 https://github.com/apache/spark/pull/2401
 https://github.com/apache/spark/pull/3024

 Do these look relevant? What version of Spark are you running?

 On Sat, Apr 11, 2015 at 9:33 AM, Tom Arnfeld t...@duedil.com wrote:

 Hey,

 Not sure whether it's best to ask this on the spark mailing list or the
 mesos one, so I'll try here first :-)

 I'm having a bit of trouble with out of memory errors in my spark
 jobs... it seems fairly odd to me that memory resources can only be set at
 the executor level, and not also at the task level. For example, as far as
 I can tell there's only a *spark.executor.memory* config option.

 Surely the memory requirements of a single executor are quite
 dramatically influenced by the number of concurrent tasks running? Given a
 shared cluster, I have no idea what % of an individual slave my executor is
 going to get, so I basically have to set the executor memory to a value
 that's correct when the whole machine is in use...

 Has anyone else running Spark on Mesos come across this, or maybe
 someone could correct my understanding of the config options?

 Thanks!

 Tom.