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mgr/balancer: make crush-compat mode work!

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- it does multiple iterations, like the upmap optimizer.
- it decreases the step size if it isn't improving, in the hope
that it is overshooting the minimum
- debug output is cleaned up a bit (the info level should be
genuinely useful)

Signed-off-by: Sage Weil <sage@redhat.com>
This commit is contained in:
Sage Weil 2017-09-26 18:00:08 -04:00
parent 2912364ff8
commit d9a31595ba
1 changed files with 62 additions and 24 deletions
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86
src/pybind/mgr/balancer/module.py
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@ -3,6 +3,7 @@
Balance PG distribution across OSDs.
"""
import copy
import errno
import json
import math
@ -160,7 +161,8 @@ class Eval:
r[t] = {
'avg': avg,
'stddev': stddev,
'score': sum_weight,
'sum_weight': sum_weight,
'score': score,
}
return r
@ -577,11 +579,19 @@ class Module(MgrModule):
def do_crush_compat(self, plan):
self.log.info('do_crush_compat')
max_iterations = self.get_config('crush_compat_max_iterations', 25)
if max_iterations < 1:
return False
step = self.get_config('crush_compat_step', .2)
if step <= 0 or step >= 1.0:
return False
osdmap = self.get_osdmap()
crush = osdmap.get_crush()
# get current compat weight-set weights
old_ws = self.get_compat_weight_set_weights()
orig_ws = self.get_compat_weight_set_weights()
orig_ws = { a: b for a, b in orig_ws.iteritems() if a >= 0 }
ms = plan.initial
pe = self.calc_eval(ms)
@ -606,28 +616,56 @@ class Module(MgrModule):
key = 'pgs' # pgs objects or bytes
# go
random.shuffle(roots)
for root in roots:
pools = pe.root_pools[root]
self.log.info('Balancing root %s (pools %s) by %s' %
(root, pools, key))
target = pe.target_by_root[root]
actual = pe.actual_by_root[root][key]
queue = sorted(actual.keys(),
key=lambda osd: -abs(target[osd] - actual[osd]))
self.log.debug('queue %s' % queue)
for osd in queue:
deviation = target[osd] - actual[osd]
if deviation == 0:
break
self.log.debug('osd.%d deviation %f', osd, deviation)
weight = old_ws[osd]
calc_weight = target[osd] / actual[osd] * weight
new_weight = weight * .7 + calc_weight * .3
self.log.debug('Reweight osd.%d %f -> %f', osd, weight,
new_weight)
plan.compat_ws[osd] = new_weight
return True
best_ws = copy.deepcopy(orig_ws)
cur_pe = pe
left = max_iterations
while left > 0:
# adjust
self.log.debug('best_ws %s' % best_ws)
next_ws = copy.deepcopy(best_ws)
random.shuffle(roots)
for root in roots:
pools = cur_pe.root_pools[root]
self.log.info('Balancing root %s (pools %s) by %s' %
(root, pools, key))
target = cur_pe.target_by_root[root]
actual = cur_pe.actual_by_root[root][key]
queue = sorted(actual.keys(),
key=lambda osd: -abs(target[osd] - actual[osd]))
for osd in queue:
deviation = target[osd] - actual[osd]
if deviation == 0:
break
self.log.debug('osd.%d deviation %f', osd, deviation)
weight = best_ws[osd]
calc_weight = target[osd] / actual[osd] * weight
new_weight = weight * (1.0 - step) + calc_weight * step
self.log.debug('Reweight osd.%d %f -> %f', osd, weight,
new_weight)
next_ws[osd] = new_weight
# recalc
plan.compat_ws = copy.deepcopy(next_ws)
next_ms = plan.final_state()
next_pe = self.calc_eval(next_ms)
self.log.debug('Step result score %f -> %f', cur_pe.score,
next_pe.score)
if next_pe.score > cur_pe.score * 1.01:
step /= 2.0
self.log.debug('Score got worse, trying smaller step %f' % step)
else:
cur_pe = next_pe
best_ws = next_ws
left -= 1
if cur_pe.score < pe.score:
self.log.info('Success, score %f -> %f', pe.score, cur_pe.score)
plan.compat_ws = best_ws
return True
else:
self.log.info('Failed to find further optimization, score %f',
pe.score)
return False
def compat_weight_set_reweight(self, osd, new_weight):
self.log.debug('ceph osd crush weight-set reweight-compat')