25 Jul
2012
25 Jul
'12
8:56 a.m.
Hi,
A topic that was raised recently is the scalability issue of Moose. Indeed, it would be
great to have again some traction in this direction.
I think there are a couple of possibilities in this area:
- Gemstone. This is the straightforward idea and it would probably match quite well the
FAMIX object-oriented model. The realization might not be that easy because we would need
to go through all the details of the language independence. Also, we would need a remote
UI, or at least a bridge for Glamour. Perhaps the Seaside interface of Glamour, or another
Seaside-based one, would be a good direction.
- Graph DBs. http://neo4j.org/ would be an interesting target here. I have no experience
in this area, but I would be very interested in collaborating with someone on this.
- Relational DBs. I know that Marco D'Ambros worked on a solution to map FAMIX
meta-descriptions to Glorp in VW. And it seemed to work. I think Alberto Bacchelli is or
has used it.
- Fuel. Loading with Fuel is about one order of magnitude faster than loading plain MSE:
for example for ArgoUML 0.32 I got 108s for the MSE, and 17s for the Fuel based MSE
information. So, having the Fuel export next to the MSE file can be a significant
improvement. However, this solution only works when you have computations that treat one
model at a time (so, it's not so nice if you have to switch between models all the
time). I attached a simple script to analyze the difference between plain MSE loading and
Fuel load variations.
- Filtered loading. This solution works well when you have the full model, but you do not
need all of it. FMMetaRepositoryFilter offers the infrastructure for a filtered loading at
the level of FAME: if you want to load a sub part of your model, you construct a
sub-meta-model and then let Fame do the rest. This kind of works, but it is only available
at the moment in code (look at the tests). We need to move to the next step of integrating
this solution and replace the MooseImportingContext and possibly offer a UI as well.
If you want to pick up on any of these topics, let's open a separate thread and see
what we can do.
Cheers,
Doru
--
www.tudorgirba.com
"We can create beautiful models in a vacuum.
But, to get them effective we have to deal with the inconvenience of reality."
25 Jul
25 Jul
11:52 a.m.
Hi Doru,
When thinking about scalability of Moose, what scenarios do you have
in mind? Up to about half a terabyte, you can run out of main memory
on a single machine cost-effectively. The main limitation there is
the lack of a 64-bit vm and image. As far as I understand the access
patterns involved, a main memory based or distributed main memory
solution is far preferable for actually analyzing systems. What do you
hope to achieve by going to disk? When we did the data conversion project,
we thought about partitioning over multiple images but finally managed
with partial loading.
Stephan
4:58 p.m.
Hi Stef,
Sorry but the problem is not that there are no 64-bit vms and images
available. The problem is that the available 32-bit vm and image cannot be
pushed further 500MB. Even worst is that as far as I understood we are not
even sure why is like that.
For me a reasonable size for a Moose image containing an average size Java
Enterprise application is between 500MB and 1500MB. So a 32-bit vm\image
should be perfectly able to store the whole model and to have enough free
space for computations.
Partial loading could be a solution in some cases but we need tool support
for that. I cannot invest 2 weeks every time I need to script a 10 minutes
analysis trying to figure out how to partially load the information that I
"might" need. Without having a full model available the entire idea of
prototyping analysis behind Moose goes down the drain and so Moose itself
lose a lot of its meaning.
I think the whole point is to have all the data on the system in analysis
at hand. Either having 10GB model stored in an image or loading the needed
entities on demand it is not relevant as soon as it is transparent for the
user and the performances are not too bad.
Cheers,
Fabrizio
2012/7/25 <stephan(a)stack.nl>
Hi Doru,
When thinking about scalability of Moose, what scenarios do you have
in mind? Up to about half a terabyte, you can run out of main memory
on a single machine cost-effectively. The main limitation there is
the lack of a 64-bit vm and image. As far as I understand the access
patterns involved, a main memory based or distributed main memory
solution is far preferable for actually analyzing systems. What do you
hope to achieve by going to disk? When we did the data conversion project,
we thought about partitioning over multiple images but finally managed
with partial loading.
Stephan
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5:29 p.m.
Whoops.
Hi Stephan not Stef :)
2012/7/25 Fabrizio Perin <fabrizio.perin(a)gmail.com>
Hi Stef,
Sorry but the problem is not that there are no 64-bit vms and images
available. The problem is that the available 32-bit vm and image cannot be
pushed further 500MB. Even worst is that as far as I understood we are not
even sure why is like that.
For me a reasonable size for a Moose image containing an average size Java
Enterprise application is between 500MB and 1500MB. So a 32-bit vm\image
should be perfectly able to store the whole model and to have enough free
space for computations.
Partial loading could be a solution in some cases but we need tool support
for that. I cannot invest 2 weeks every time I need to script a 10 minutes
analysis trying to figure out how to partially load the information that I
"might" need. Without having a full model available the entire idea of
prototyping analysis behind Moose goes down the drain and so Moose itself
lose a lot of its meaning.
I think the whole point is to have all the data on the system in analysis
at hand. Either having 10GB model stored in an image or loading the needed
entities on demand it is not relevant as soon as it is transparent for the
user and the performances are not too bad.
Cheers,
Fabrizio
2012/7/25 <stephan(a)stack.nl>
Hi Doru,
When thinking about scalability of Moose, what scenarios do you have
in mind? Up to about half a terabyte, you can run out of main memory
on a single machine cost-effectively. The main limitation there is
the lack of a 64-bit vm and image. As far as I understand the access
patterns involved, a main memory based or distributed main memory
solution is far preferable for actually analyzing systems. What do you
hope to achieve by going to disk? When we did the data conversion project,
we thought about partitioning over multiple images but finally managed
with partial loading.
Stephan
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27 Jul
27 Jul
9:55 p.m.
On Jul 25, 2012, at 4:58 PM, Fabrizio Perin wrote:
Hi Stef,
Sorry but the problem is not that there are no 64-bit vms and images available. The
problem is that the available 32-bit vm and image cannot be pushed further 500MB. Even
worst is that as far as I understood we are not even sure why is like that.
For me a reasonable size for a Moose image containing an average size Java Enterprise
application is between 500MB and 1500MB. So a 32-bit vm\image should be perfectly able to
store the whole model and to have enough free space for computations.
I asked igor.
Esteban?
Partial loading could be a solution in some cases but
we need tool support for that. I cannot invest 2 weeks every time I need to script a 10
minutes analysis trying to figure out how to partially load the information that I
"might" need. Without having a full model available the entire idea of
prototyping analysis behind Moose goes down the drain and so Moose itself lose a lot of
its meaning.
We are talking about clients to which you would like to sell trend analysis. So for this
case this is not a question of memory optimization
because may be you want to have 50 or more version of a system and you have to think and
reduce the number of element (no need to represent self, this and other….)>
I think the whole point is to have all the data on the
system in analysis at hand. Either having 10GB model stored in an image or loading the
needed entities on demand it is not relevant as soon as it is transparent for the user and
the performances are not too bad.
May be but so far I do not know how I can get 10 GB at hand in Pharo.
Cheers,
Fabrizio
2012/7/25 <stephan(a)stack.nl>
Hi Doru,
When thinking about scalability of Moose, what scenarios do you have
in mind? Up to about half a terabyte, you can run out of main memory
on a single machine cost-effectively. The main limitation there is
the lack of a 64-bit vm and image. As far as I understand the access
patterns involved, a main memory based or distributed main memory
solution is far preferable for actually analyzing systems. What do you
hope to achieve by going to disk? When we did the data conversion project,
we thought about partitioning over multiple images but finally managed
with partial loading.
Stephan
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11:12 p.m.
Hi Fabrizio,
On Jul 25, 2012, at 4:58 PM, Fabrizio Perin wrote:
Sorry but the problem is not that there are no 64-bit
vms and images available. The problem is that the available 32-bit vm and image cannot be
pushed further 500MB. Even worst is that as far as I understood we are not even sure why
is like that.
Is that windows only? I run larger mac images.
For me a reasonable size for a Moose image containing
an average size Java Enterprise application is between 500MB and 1500MB. So a 32-bit
vm\image should be perfectly able to store the whole model and to have enough free space
for computations.
For me the difference between a 500 MB model and a 2GB model is not really meaningful. It
still provides
a significant limit to the size of models I can handle. I try to avoid loading as much as
possible. A 588 MB
image starts in 3 seconds on my smallest machine, so that is fast enough.
Partial loading could be a solution in some cases but
we need tool support for that. I cannot invest 2 weeks every time I need to script a 10
minutes analysis trying to figure out how to partially load the information that I
"might" need. Without having a full model available the entire idea of
prototyping analysis behind Moose goes down the drain and so Moose itself lose a lot of
its meaning.
+1
I think the whole point is to have all the data on the
system in analysis at hand. Either having 10GB model stored in an image or loading the
needed entities on demand it is not relevant as soon as it is transparent for the user and
the performances are not too bad.
I don't understand how performance can be good using a nosql or rdbms system. Gemstone
with enough ram,
or multiple pharo images with distributed processing, yes, but copying all that data
around sounds to me
like a non-starter.
Stephan
25 Jul
25 Jul
1:04 p.m.
Don't forget mongodb and riak. Both already have pharo support (especially mongo).
cheers,
Esteban
On Jul 25, 2012, at 8:56 AM, Tudor Girba wrote:
Hi,
A topic that was raised recently is the scalability issue of Moose. Indeed, it would be
great to have again some traction in this direction.
I think there are a couple of possibilities in this area:
- Gemstone. This is the straightforward idea and it would probably match quite well the
FAMIX object-oriented model. The realization might not be that easy because we would need
to go through all the details of the language independence. Also, we would need a remote
UI, or at least a bridge for Glamour. Perhaps the Seaside interface of Glamour, or another
Seaside-based one, would be a good direction.
- Graph DBs. http://neo4j.org/ would be an interesting target here. I have no experience
in this area, but I would be very interested in collaborating with someone on this.
- Relational DBs. I know that Marco D'Ambros worked on a solution to map FAMIX
meta-descriptions to Glorp in VW. And it seemed to work. I think Alberto Bacchelli is or
has used it.
- Fuel. Loading with Fuel is about one order of magnitude faster than loading plain MSE:
for example for ArgoUML 0.32 I got 108s for the MSE, and 17s for the Fuel based MSE
information. So, having the Fuel export next to the MSE file can be a significant
improvement. However, this solution only works when you have computations that treat one
model at a time (so, it's not so nice if you have to switch between models all the
time). I attached a simple script to analyze the difference between plain MSE loading and
Fuel load variations.
- Filtered loading. This solution works well when you have the full model, but you do not
need all of it. FMMetaRepositoryFilter offers the infrastructure for a filtered loading at
the level of FAME: if you want to load a sub part of your model, you construct a
sub-meta-model and then let Fame do the rest. This kind of works, but it is only available
at the moment in code (look at the tests). We need to move to the next step of integrating
this solution and replace the MooseImportingContext and possibly offer a UI as well.
If you want to pick up on any of these topics, let's open a separate thread and see
what we can do.
Cheers,
Doru
--
www.tudorgirba.com
"We can create beautiful models in a vacuum.
But, to get them effective we have to deal with the inconvenience of reality."
<mse-vs-fuel.txt>
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1:20 p.m.
Good point.
Anyone interested in giving any of these a try?
Doru
On Wed, Jul 25, 2012 at 1:04 PM, Esteban Lorenzano <estebanlm(a)gmail.com> wrote:
Don't forget mongodb and riak. Both already have
pharo support (especially mongo).
cheers,
Esteban
On Jul 25, 2012, at 8:56 AM, Tudor Girba wrote:
--
www.tudorgirba.com
"Every thing has its own flow"
Hi,
A topic that was raised recently is the scalability issue of Moose. Indeed, it would be
great to have again some traction in this direction.
I think there are a couple of possibilities in this area:
- Gemstone. This is the straightforward idea and it would probably match quite well the
FAMIX object-oriented model. The realization might not be that easy because we would need
to go through all the details of the language independence. Also, we would need a remote
UI, or at least a bridge for Glamour. Perhaps the Seaside interface of Glamour, or another
Seaside-based one, would be a good direction.
- Graph DBs. http://neo4j.org/ would be an interesting target here. I have no experience
in this area, but I would be very interested in collaborating with someone on this.
- Relational DBs. I know that Marco D'Ambros worked on a solution to map FAMIX
meta-descriptions to Glorp in VW. And it seemed to work. I think Alberto Bacchelli is or
has used it.
- Fuel. Loading with Fuel is about one order of magnitude faster than loading plain MSE:
for example for ArgoUML 0.32 I got 108s for the MSE, and 17s for the Fuel based MSE
information. So, having the Fuel export next to the MSE file can be a significant
improvement. However, this solution only works when you have computations that treat one
model at a time (so, it's not so nice if you have to switch between models all the
time). I attached a simple script to analyze the difference between plain MSE loading and
Fuel load variations.
- Filtered loading. This solution works well when you have the full model, but you do not
need all of it. FMMetaRepositoryFilter offers the infrastructure for a filtered loading at
the level of FAME: if you want to load a sub part of your model, you construct a
sub-meta-model and then let Fame do the rest. This kind of works, but it is only available
at the moment in code (look at the tests). We need to move to the next step of integrating
this solution and replace the MooseImportingContext and possibly offer a UI as well.
If you want to pick up on any of these topics, let's open a separate thread and see
what we can do.
Cheers,
Doru
--
www.tudorgirba.com
"We can create beautiful models in a vacuum.
But, to get them effective we have to deal with the inconvenience of reality."
<mse-vs-fuel.txt>
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7 comments
6 participants
participants (6)
-
Esteban Lorenzano -
Fabrizio Perin -
Stephan Eggermont -
stephan＠stack.nl
-
Stéphane Ducasse -
Tudor Girba