Hello everybody,
I believe that the number of workers is only relevant if you use Storm's
default scheduler. You can also define your own scheduler by
implementing the IScheduler interface and assign the different executors
on the available slots as you see fit.
Ahmed
On 11/05/2014 06:20 PM, Tyson Norris wrote:
I agree the behavior is awkward.
The setNumWorkers config appears to behave as an upper limit of
workers that will be utilized (e.g. setNumWorkers(200) will not fail
to deploy when workers < 200), so this can also impact the ORDER of
topology deployment required when workers < sum of all setNumWorkers
configs for all topologies.
For example, I originally setNumWorkers(200) - artificially high - so
that we can scale our worker pool up to 200 without deploying new
code. However, when worker pool is 10, and this topology gets deployed
first, then no other topologies can be deployed, since all the workers
are assigned to this single topology.
Now, while this is awkward IMHO (I would rather have some additional
knobs, like “assign a percentage of workers” or “assign a topology
priority”), we can get around this by using rebalance and setting the
number of workers to reflect the current state of the cluster. Since
you MUST rebalance to increase/decrease the number of threads (which
would typically go hand in hand with changing the number of workers, I
think), then its not really extra work.
At some point it would be nice to have some pluggable logic that
controls the number of executors dynamically, so that when a worker is
added, the number of executors can be altered programmatically,
instead of requiring manual intervention.
Thanks
Tyson
On Nov 5, 2014, at 9:05 AM, Dan DeCapria, CivicScience
<[email protected] <mailto:[email protected]>>
wrote:
Hi Nathan,
Sounds like I need to just bite-the-bullet and manually define the
number of workers for each topology considering all topologies that
will be running concurrently. The secondary process you mentioned is
interesting wrt using Thrift to query the worker utilization and then
auto-balance all topologies during runtime - I'll have to look into
that process further.
Thanks for you help,
-Dan
On Wed, Nov 5, 2014 at 11:50 AM, Nathan Leung <[email protected]
<mailto:[email protected]>> wrote:
It doesn't make sense to automatically balance worker load
because you can get yourself into strange situations (e.g. 3
workers and each topology requests 4, or 4 workers and > 4
topologies, etc). It would be nice if the UI or the logs gave a
better indication that there were not enough workers to go around
though. You could write something that reads cluster information
from the nimbus over thrift and rebalances all topologies as
necessary, but I think it would be better to just make sure that
you have enough workers available (or even better, more than
enough workers) to satisfy the needs of your applications.
On Tue, Nov 4, 2014 at 4:45 PM, Dan DeCapria, CivicScience
<[email protected]
<mailto:[email protected]>> wrote:
Use Case:
I have a production storm cluster running with six workers.
Currently topology A is active and consuming all six workers
via conf.setNumWorkers(6). Now, launching Topology B with six
workers (again via conf.setNumWorkers(6)) states the topology
is active, but currently there are no available workers on
the cluster for Topology B to use (as Topology A has claimed
them all already) and hence Topology B is doing nothing. I
believe this is due to storm requiring a priori that the sum
of all topology's workers requested <= cluster worker capacity.
I am wondering why the sum of all topology workers is not
normalizing the allocations for the worker pool when capacity
is exceeded and auto-adjusting as new topologies come and go?
Meaning, from the use case, since both topologies requested
the same count of six workers, and given the finite capacity
of the cluster at six actual workers, the /implemented/
normalized proportion of the cluster resources for each
topology would be 50% split - such that Topology A gets three
actual workers and Topology B gets three actual workers as well.
How would I go about implementing a dynamic re-allocation of
topology workers based on the proportion of expected workers
given the cluster's finite worker capacity? An idea would be
allocating workers as desired for all topologies until the
cluster capacity is reached, at which point the actual number
of workers desired becomes a normalized proportion allocation
model over the cluster.
Many thanks,
-Dan
