Open Stack

I am struggling with python code profiling in general. It has its own
caveats like 100% plus overhead.
However, on a host with only nova services (DB on a different host), I see
cpu utilization spike up quickly with scale. The DB server is relatively
calm and never goes over 20%. On a system which relies on DB to fetch all
the data, this should not happen.

I could not find any analysis of nova performance either. Appreciate if
someone can point me to one.

Thanks,





On Tue, Aug 11, 2015 at 3:57 PM, Chris Friesen <chris.friesen at windriver.com>
wrote:

> Just curious...have you measured this consuming a significant amount of
> CPU time?  Or is it more a gut feel of "this looks like it might be
> expensive"?
>
> Chris
>
>
> On 08/11/2015 04:51 PM, Sachin Manpathak wrote:
>
>> Here are a few --
>> instance_get_all_by_filters joins manually with
>> instances_fill_metadata --
>>
>> https://github.com/openstack/nova/blob/master/nova/db/sqlalchemy/api.py#L1890
>>
>> https://github.com/openstack/nova/blob/master/nova/db/sqlalchemy/api.py#L1782
>>
>> Almost all instance query functions manually join with instance_metadata.
>>
>> Another example was compute_node_get_all function which joined
>> compute_node,
>> services and ip tables. But it is simplified  in current codebase (I am
>> working
>> on Juno)
>>
>>
>>
>>
>> On Tue, Aug 11, 2015 at 3:09 PM, Clint Byrum <clint at fewbar.com
>> <mailto:clint at fewbar.com>> wrote:
>>
>>     Excerpts from Sachin Manpathak's message of 2015-08-12 05:40:36 +0800:
>>     > Hi folks,
>>     > Nova codebase seems to follow manual joins model where all data
>> required by
>>     > an API is fetched from multiple tables and then joined manually by
>> using
>>     > (in most cases) python dictionary lookups.
>>     >
>>     > I was wondering about the basis reasoning for doing so. I usually
>> find
>>     > openstack services to be CPU bound in a medium sized environment and
>>     > non-trivial utilization seems to be from parts of code which do
>> manual
>>     > joins.
>>
>>     Could you please cite specific examples so we can follow along with
>> your
>>     thinking without having to repeat your analysis?
>>
>>     Thanks!
>>
>>
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