--- /dev/null
+
+"""
+Balance PG distribution across OSDs.
+"""
+
+import copy
+import errno
+import json
+import math
+import random
+import time
+from mgr_module import MgrModule, CommandResult
+from threading import Event
+
+# available modes: 'none', 'crush', 'crush-compat', 'upmap', 'osd_weight'
+default_mode = 'none'
+default_sleep_interval = 60 # seconds
+default_max_misplaced = .05 # max ratio of pgs replaced at a time
+
+TIME_FORMAT = '%Y-%m-%d_%H:%M:%S'
+
+
+class MappingState:
+ def __init__(self, osdmap, pg_dump, desc=''):
+ self.desc = desc
+ self.osdmap = osdmap
+ self.osdmap_dump = self.osdmap.dump()
+ self.crush = osdmap.get_crush()
+ self.crush_dump = self.crush.dump()
+ self.pg_dump = pg_dump
+ self.pg_stat = {
+ i['pgid']: i['stat_sum'] for i in pg_dump.get('pg_stats', [])
+ }
+ self.poolids = [p['pool'] for p in self.osdmap_dump.get('pools', [])]
+ self.pg_up = {}
+ self.pg_up_by_poolid = {}
+ for poolid in self.poolids:
+ self.pg_up_by_poolid[poolid] = osdmap.map_pool_pgs_up(poolid)
+ for a,b in self.pg_up_by_poolid[poolid].iteritems():
+ self.pg_up[a] = b
+
+ def calc_misplaced_from(self, other_ms):
+ num = len(other_ms.pg_up)
+ misplaced = 0
+ for pgid, before in other_ms.pg_up.iteritems():
+ if before != self.pg_up.get(pgid, []):
+ misplaced += 1
+ if num > 0:
+ return float(misplaced) / float(num)
+ return 0.0
+
+class Plan:
+ def __init__(self, name, ms):
+ self.mode = 'unknown'
+ self.name = name
+ self.initial = ms
+
+ self.osd_weights = {}
+ self.compat_ws = {}
+ self.inc = ms.osdmap.new_incremental()
+
+ def final_state(self):
+ self.inc.set_osd_reweights(self.osd_weights)
+ self.inc.set_crush_compat_weight_set_weights(self.compat_ws)
+ return MappingState(self.initial.osdmap.apply_incremental(self.inc),
+ self.initial.pg_dump,
+ 'plan %s final' % self.name)
+
+ def dump(self):
+ return json.dumps(self.inc.dump(), indent=4)
+
+ def show(self):
+ ls = []
+ ls.append('# starting osdmap epoch %d' % self.initial.osdmap.get_epoch())
+ ls.append('# starting crush version %d' %
+ self.initial.osdmap.get_crush_version())
+ ls.append('# mode %s' % self.mode)
+ if len(self.compat_ws) and \
+ '-1' not in self.initial.crush_dump.get('choose_args', {}):
+ ls.append('ceph osd crush weight-set create-compat')
+ for osd, weight in self.compat_ws.iteritems():
+ ls.append('ceph osd crush weight-set reweight-compat %s %f' %
+ (osd, weight))
+ for osd, weight in self.osd_weights.iteritems():
+ ls.append('ceph osd reweight osd.%d %f' % (osd, weight))
+ incdump = self.inc.dump()
+ for pgid in incdump.get('old_pg_upmap_items', []):
+ ls.append('ceph osd rm-pg-upmap-items %s' % pgid)
+ for item in incdump.get('new_pg_upmap_items', []):
+ osdlist = []
+ for m in item['mappings']:
+ osdlist += [m['from'], m['to']]
+ ls.append('ceph osd pg-upmap-items %s %s' %
+ (item['pgid'], ' '.join([str(a) for a in osdlist])))
+ return '\n'.join(ls)
+
+
+class Eval:
+ root_ids = {} # root name -> id
+ pool_name = {} # pool id -> pool name
+ pool_id = {} # pool name -> id
+ pool_roots = {} # pool name -> root name
+ root_pools = {} # root name -> pools
+ target_by_root = {} # root name -> target weight map
+ count_by_pool = {}
+ count_by_root = {}
+ actual_by_pool = {} # pool -> by_* -> actual weight map
+ actual_by_root = {} # pool -> by_* -> actual weight map
+ total_by_pool = {} # pool -> by_* -> total
+ total_by_root = {} # root -> by_* -> total
+ stats_by_pool = {} # pool -> by_* -> stddev or avg -> value
+ stats_by_root = {} # root -> by_* -> stddev or avg -> value
+
+ score_by_pool = {}
+ score_by_root = {}
+
+ score = 0.0
+
+ def __init__(self, ms):
+ self.ms = ms
+
+ def show(self, verbose=False):
+ if verbose:
+ r = self.ms.desc + '\n'
+ r += 'target_by_root %s\n' % self.target_by_root
+ r += 'actual_by_pool %s\n' % self.actual_by_pool
+ r += 'actual_by_root %s\n' % self.actual_by_root
+ r += 'count_by_pool %s\n' % self.count_by_pool
+ r += 'count_by_root %s\n' % self.count_by_root
+ r += 'total_by_pool %s\n' % self.total_by_pool
+ r += 'total_by_root %s\n' % self.total_by_root
+ r += 'stats_by_root %s\n' % self.stats_by_root
+ r += 'score_by_pool %s\n' % self.score_by_pool
+ r += 'score_by_root %s\n' % self.score_by_root
+ else:
+ r = self.ms.desc + ' '
+ r += 'score %f (lower is better)\n' % self.score
+ return r
+
+ def calc_stats(self, count, target, total):
+ num = max(len(target), 1)
+ r = {}
+ for t in ('pgs', 'objects', 'bytes'):
+ avg = float(total[t]) / float(num)
+ dev = 0.0
+
+ # score is a measure of how uneven the data distribution is.
+ # score lies between [0, 1), 0 means perfect distribution.
+ score = 0.0
+ sum_weight = 0.0
+
+ for k, v in count[t].iteritems():
+ # adjust/normalize by weight
+ if target[k]:
+ adjusted = float(v) / target[k] / float(num)
+ else:
+ adjusted = 0.0
+
+ # Overweighted devices and their weights are factors to calculate reweight_urgency.
+ # One 10% underfilled device with 5 2% overfilled devices, is arguably a better
+ # situation than one 10% overfilled with 5 2% underfilled devices
+ if adjusted > avg:
+ '''
+ F(x) = 2*phi(x) - 1, where phi(x) = cdf of standard normal distribution
+ x = (adjusted - avg)/avg.
+ Since, we're considering only over-weighted devices, x >= 0, and so phi(x) lies in [0.5, 1).
+ To bring range of F(x) in range [0, 1), we need to make the above modification.
+
+ In general, we need to use a function F(x), where x = (adjusted - avg)/avg
+ 1. which is bounded between 0 and 1, so that ultimately reweight_urgency will also be bounded.
+ 2. A larger value of x, should imply more urgency to reweight.
+ 3. Also, the difference between F(x) when x is large, should be minimal.
+ 4. The value of F(x) should get close to 1 (highest urgency to reweight) with steeply.
+
+ Could have used F(x) = (1 - e^(-x)). But that had slower convergence to 1, compared to the one currently in use.
+
+ cdf of standard normal distribution: https://stackoverflow.com/a/29273201
+ '''
+ score += target[k] * (math.erf(((adjusted - avg)/avg) / math.sqrt(2.0)))
+ sum_weight += target[k]
+ dev += (avg - adjusted) * (avg - adjusted)
+ stddev = math.sqrt(dev / float(max(num - 1, 1)))
+ score = score / max(sum_weight, 1)
+ r[t] = {
+ 'avg': avg,
+ 'stddev': stddev,
+ 'sum_weight': sum_weight,
+ 'score': score,
+ }
+ return r
+
+class Module(MgrModule):
+ COMMANDS = [
+ {
+ "cmd": "balancer status",
+ "desc": "Show balancer status",
+ "perm": "r",
+ },
+ {
+ "cmd": "balancer mode name=mode,type=CephChoices,strings=none|crush-compat|upmap",
+ "desc": "Set balancer mode",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer on",
+ "desc": "Enable automatic balancing",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer off",
+ "desc": "Disable automatic balancing",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer eval name=plan,type=CephString,req=false",
+ "desc": "Evaluate data distribution for the current cluster or specific plan",
+ "perm": "r",
+ },
+ {
+ "cmd": "balancer eval-verbose name=plan,type=CephString,req=false",
+ "desc": "Evaluate data distribution for the current cluster or specific plan (verbosely)",
+ "perm": "r",
+ },
+ {
+ "cmd": "balancer optimize name=plan,type=CephString",
+ "desc": "Run optimizer to create a new plan",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer show name=plan,type=CephString",
+ "desc": "Show details of an optimization plan",
+ "perm": "r",
+ },
+ {
+ "cmd": "balancer rm name=plan,type=CephString",
+ "desc": "Discard an optimization plan",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer reset",
+ "desc": "Discard all optimization plans",
+ "perm": "rw",
+ },
+ {
+ "cmd": "balancer dump name=plan,type=CephString",
+ "desc": "Show an optimization plan",
+ "perm": "r",
+ },
+ {
+ "cmd": "balancer execute name=plan,type=CephString",
+ "desc": "Execute an optimization plan",
+ "perm": "r",
+ },
+ ]
+ active = False
+ run = True
+ plans = {}
+ mode = ''
+
+ def __init__(self, *args, **kwargs):
+ super(Module, self).__init__(*args, **kwargs)
+ self.event = Event()
+
+ def handle_command(self, command):
+ self.log.warn("Handling command: '%s'" % str(command))
+ if command['prefix'] == 'balancer status':
+ s = {
+ 'plans': self.plans.keys(),
+ 'active': self.active,
+ 'mode': self.get_config('mode', default_mode),
+ }
+ return (0, json.dumps(s, indent=4), '')
+ elif command['prefix'] == 'balancer mode':
+ self.set_config('mode', command['mode'])
+ return (0, '', '')
+ elif command['prefix'] == 'balancer on':
+ if not self.active:
+ self.set_config('active', '1')
+ self.active = True
+ self.event.set()
+ return (0, '', '')
+ elif command['prefix'] == 'balancer off':
+ if self.active:
+ self.set_config('active', '')
+ self.active = False
+ self.event.set()
+ return (0, '', '')
+ elif command['prefix'] == 'balancer eval' or command['prefix'] == 'balancer eval-verbose':
+ verbose = command['prefix'] == 'balancer eval-verbose'
+ if 'plan' in command:
+ plan = self.plans.get(command['plan'])
+ if not plan:
+ return (-errno.ENOENT, '', 'plan %s not found' %
+ command['plan'])
+ ms = plan.final_state()
+ else:
+ ms = MappingState(self.get_osdmap(),
+ self.get("pg_dump"),
+ 'current cluster')
+ return (0, self.evaluate(ms, verbose=verbose), '')
+ elif command['prefix'] == 'balancer optimize':
+ plan = self.plan_create(command['plan'])
+ self.optimize(plan)
+ return (0, '', '')
+ elif command['prefix'] == 'balancer rm':
+ self.plan_rm(command['name'])
+ return (0, '', '')
+ elif command['prefix'] == 'balancer reset':
+ self.plans = {}
+ return (0, '', '')
+ elif command['prefix'] == 'balancer dump':
+ plan = self.plans.get(command['plan'])
+ if not plan:
+ return (-errno.ENOENT, '', 'plan %s not found' % command['plan'])
+ return (0, plan.dump(), '')
+ elif command['prefix'] == 'balancer show':
+ plan = self.plans.get(command['plan'])
+ if not plan:
+ return (-errno.ENOENT, '', 'plan %s not found' % command['plan'])
+ return (0, plan.show(), '')
+ elif command['prefix'] == 'balancer execute':
+ plan = self.plans.get(command['plan'])
+ if not plan:
+ return (-errno.ENOENT, '', 'plan %s not found' % command['plan'])
+ self.execute(plan)
+ self.plan_rm(plan)
+ return (0, '', '')
+ else:
+ return (-errno.EINVAL, '',
+ "Command not found '{0}'".format(command['prefix']))
+
+ def shutdown(self):
+ self.log.info('Stopping')
+ self.run = False
+ self.event.set()
+
+ def time_in_interval(self, tod, begin, end):
+ if begin <= end:
+ return tod >= begin and tod < end
+ else:
+ return tod >= begin or tod < end
+
+ def serve(self):
+ self.log.info('Starting')
+ while self.run:
+ self.active = self.get_config('active', '') is not ''
+ begin_time = self.get_config('begin_time') or '0000'
+ end_time = self.get_config('end_time') or '2400'
+ timeofday = time.strftime('%H%M', time.localtime())
+ self.log.debug('Waking up [%s, scheduled for %s-%s, now %s]',
+ "active" if self.active else "inactive",
+ begin_time, end_time, timeofday)
+ sleep_interval = float(self.get_config('sleep_interval',
+ default_sleep_interval))
+ if self.active and self.time_in_interval(timeofday, begin_time, end_time):
+ self.log.debug('Running')
+ name = 'auto_%s' % time.strftime(TIME_FORMAT, time.gmtime())
+ plan = self.plan_create(name)
+ if self.optimize(plan):
+ self.execute(plan)
+ self.plan_rm(name)
+ self.log.debug('Sleeping for %d', sleep_interval)
+ self.event.wait(sleep_interval)
+ self.event.clear()
+
+ def plan_create(self, name):
+ plan = Plan(name, MappingState(self.get_osdmap(),
+ self.get("pg_dump"),
+ 'plan %s initial' % name))
+ self.plans[name] = plan
+ return plan
+
+ def plan_rm(self, name):
+ if name in self.plans:
+ del self.plans[name]
+
+ def calc_eval(self, ms):
+ pe = Eval(ms)
+ pool_rule = {}
+ pool_info = {}
+ for p in ms.osdmap_dump.get('pools',[]):
+ pe.pool_name[p['pool']] = p['pool_name']
+ pe.pool_id[p['pool_name']] = p['pool']
+ pool_rule[p['pool_name']] = p['crush_rule']
+ pe.pool_roots[p['pool_name']] = []
+ pool_info[p['pool_name']] = p
+ pools = pe.pool_id.keys()
+ if len(pools) == 0:
+ return pe
+ self.log.debug('pool_name %s' % pe.pool_name)
+ self.log.debug('pool_id %s' % pe.pool_id)
+ self.log.debug('pools %s' % pools)
+ self.log.debug('pool_rule %s' % pool_rule)
+
+ osd_weight = { a['osd']: a['weight']
+ for a in ms.osdmap_dump.get('osds',[]) }
+
+ # get expected distributions by root
+ actual_by_root = {}
+ rootids = ms.crush.find_takes()
+ roots = []
+ for rootid in rootids:
+ root = ms.crush.get_item_name(rootid)
+ pe.root_ids[root] = rootid
+ roots.append(root)
+ ls = ms.osdmap.get_pools_by_take(rootid)
+ pe.root_pools[root] = []
+ for poolid in ls:
+ pe.pool_roots[pe.pool_name[poolid]].append(root)
+ pe.root_pools[root].append(pe.pool_name[poolid])
+ weight_map = ms.crush.get_take_weight_osd_map(rootid)
+ adjusted_map = {
+ osd: cw * osd_weight.get(osd, 1.0)
+ for osd,cw in weight_map.iteritems()
+ }
+ sum_w = sum(adjusted_map.values()) or 1.0
+ pe.target_by_root[root] = { osd: w / sum_w
+ for osd,w in adjusted_map.iteritems() }
+ actual_by_root[root] = {
+ 'pgs': {},
+ 'objects': {},
+ 'bytes': {},
+ }
+ for osd in pe.target_by_root[root].iterkeys():
+ actual_by_root[root]['pgs'][osd] = 0
+ actual_by_root[root]['objects'][osd] = 0
+ actual_by_root[root]['bytes'][osd] = 0
+ pe.total_by_root[root] = {
+ 'pgs': 0,
+ 'objects': 0,
+ 'bytes': 0,
+ }
+ self.log.debug('pool_roots %s' % pe.pool_roots)
+ self.log.debug('root_pools %s' % pe.root_pools)
+ self.log.debug('target_by_root %s' % pe.target_by_root)
+
+ # pool and root actual
+ for pool, pi in pool_info.iteritems():
+ poolid = pi['pool']
+ pm = ms.pg_up_by_poolid[poolid]
+ pgs = 0
+ objects = 0
+ bytes = 0
+ pgs_by_osd = {}
+ objects_by_osd = {}
+ bytes_by_osd = {}
+ for root in pe.pool_roots[pool]:
+ for osd in pe.target_by_root[root].iterkeys():
+ pgs_by_osd[osd] = 0
+ objects_by_osd[osd] = 0
+ bytes_by_osd[osd] = 0
+ for pgid, up in pm.iteritems():
+ for osd in [int(osd) for osd in up]:
+ pgs_by_osd[osd] += 1
+ objects_by_osd[osd] += ms.pg_stat[pgid]['num_objects']
+ bytes_by_osd[osd] += ms.pg_stat[pgid]['num_bytes']
+ # pick a root to associate this pg instance with.
+ # note that this is imprecise if the roots have
+ # overlapping children.
+ # FIXME: divide bytes by k for EC pools.
+ for root in pe.pool_roots[pool]:
+ if osd in pe.target_by_root[root]:
+ actual_by_root[root]['pgs'][osd] += 1
+ actual_by_root[root]['objects'][osd] += ms.pg_stat[pgid]['num_objects']
+ actual_by_root[root]['bytes'][osd] += ms.pg_stat[pgid]['num_bytes']
+ pgs += 1
+ objects += ms.pg_stat[pgid]['num_objects']
+ bytes += ms.pg_stat[pgid]['num_bytes']
+ pe.total_by_root[root]['pgs'] += 1
+ pe.total_by_root[root]['objects'] += ms.pg_stat[pgid]['num_objects']
+ pe.total_by_root[root]['bytes'] += ms.pg_stat[pgid]['num_bytes']
+ break
+ pe.count_by_pool[pool] = {
+ 'pgs': {
+ k: v
+ for k, v in pgs_by_osd.iteritems()
+ },
+ 'objects': {
+ k: v
+ for k, v in objects_by_osd.iteritems()
+ },
+ 'bytes': {
+ k: v
+ for k, v in bytes_by_osd.iteritems()
+ },
+ }
+ pe.actual_by_pool[pool] = {
+ 'pgs': {
+ k: float(v) / float(max(pgs, 1))
+ for k, v in pgs_by_osd.iteritems()
+ },
+ 'objects': {
+ k: float(v) / float(max(objects, 1))
+ for k, v in objects_by_osd.iteritems()
+ },
+ 'bytes': {
+ k: float(v) / float(max(bytes, 1))
+ for k, v in bytes_by_osd.iteritems()
+ },
+ }
+ pe.total_by_pool[pool] = {
+ 'pgs': pgs,
+ 'objects': objects,
+ 'bytes': bytes,
+ }
+ for root, m in pe.total_by_root.iteritems():
+ pe.count_by_root[root] = {
+ 'pgs': {
+ k: float(v)
+ for k, v in actual_by_root[root]['pgs'].iteritems()
+ },
+ 'objects': {
+ k: float(v)
+ for k, v in actual_by_root[root]['objects'].iteritems()
+ },
+ 'bytes': {
+ k: float(v)
+ for k, v in actual_by_root[root]['bytes'].iteritems()
+ },
+ }
+ pe.actual_by_root[root] = {
+ 'pgs': {
+ k: float(v) / float(max(pe.total_by_root[root]['pgs'], 1))
+ for k, v in actual_by_root[root]['pgs'].iteritems()
+ },
+ 'objects': {
+ k: float(v) / float(max(pe.total_by_root[root]['objects'], 1))
+ for k, v in actual_by_root[root]['objects'].iteritems()
+ },
+ 'bytes': {
+ k: float(v) / float(max(pe.total_by_root[root]['bytes'], 1))
+ for k, v in actual_by_root[root]['bytes'].iteritems()
+ },
+ }
+ self.log.debug('actual_by_pool %s' % pe.actual_by_pool)
+ self.log.debug('actual_by_root %s' % pe.actual_by_root)
+
+ # average and stddev and score
+ pe.stats_by_root = {
+ a: pe.calc_stats(
+ b,
+ pe.target_by_root[a],
+ pe.total_by_root[a]
+ ) for a, b in pe.count_by_root.iteritems()
+ }
+
+ # the scores are already normalized
+ pe.score_by_root = {
+ r: {
+ 'pgs': pe.stats_by_root[r]['pgs']['score'],
+ 'objects': pe.stats_by_root[r]['objects']['score'],
+ 'bytes': pe.stats_by_root[r]['bytes']['score'],
+ } for r in pe.total_by_root.keys()
+ }
+
+ # total score is just average of normalized stddevs
+ pe.score = 0.0
+ for r, vs in pe.score_by_root.iteritems():
+ for k, v in vs.iteritems():
+ pe.score += v
+ pe.score /= 3 * len(roots)
+ return pe
+
+ def evaluate(self, ms, verbose=False):
+ pe = self.calc_eval(ms)
+ return pe.show(verbose=verbose)
+
+ def optimize(self, plan):
+ self.log.info('Optimize plan %s' % plan.name)
+ plan.mode = self.get_config('mode', default_mode)
+ max_misplaced = float(self.get_config('max_misplaced',
+ default_max_misplaced))
+ self.log.info('Mode %s, max misplaced %f' %
+ (plan.mode, max_misplaced))
+
+ info = self.get('pg_status')
+ unknown = info.get('unknown_pgs_ratio', 0.0)
+ degraded = info.get('degraded_ratio', 0.0)
+ inactive = info.get('inactive_pgs_ratio', 0.0)
+ misplaced = info.get('misplaced_ratio', 0.0)
+ self.log.debug('unknown %f degraded %f inactive %f misplaced %g',
+ unknown, degraded, inactive, misplaced)
+ if unknown > 0.0:
+ self.log.info('Some PGs (%f) are unknown; waiting', unknown)
+ elif degraded > 0.0:
+ self.log.info('Some objects (%f) are degraded; waiting', degraded)
+ elif inactive > 0.0:
+ self.log.info('Some PGs (%f) are inactive; waiting', inactive)
+ elif misplaced >= max_misplaced:
+ self.log.info('Too many objects (%f > %f) are misplaced; waiting',
+ misplaced, max_misplaced)
+ else:
+ if plan.mode == 'upmap':
+ return self.do_upmap(plan)
+ elif plan.mode == 'crush-compat':
+ return self.do_crush_compat(plan)
+ elif plan.mode == 'none':
+ self.log.info('Idle')
+ else:
+ self.log.info('Unrecognized mode %s' % plan.mode)
+ return False
+
+ ##
+
+ def do_upmap(self, plan):
+ self.log.info('do_upmap')
+ max_iterations = self.get_config('upmap_max_iterations', 10)
+ max_deviation = self.get_config('upmap_max_deviation', .01)
+
+ ms = plan.initial
+ pools = [str(i['pool_name']) for i in ms.osdmap_dump.get('pools',[])]
+ if len(pools) == 0:
+ self.log.info('no pools, nothing to do')
+ return False
+ # shuffle pool list so they all get equal (in)attention
+ random.shuffle(pools)
+ self.log.info('pools %s' % pools)
+
+ inc = plan.inc
+ total_did = 0
+ left = max_iterations
+ for pool in pools:
+ did = ms.osdmap.calc_pg_upmaps(inc, max_deviation, left, [pool])
+ total_did += did
+ left -= did
+ if left <= 0:
+ break
+ self.log.info('prepared %d/%d changes' % (total_did, max_iterations))
+ return True
+
+ 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', .5)
+ if step <= 0 or step >= 1.0:
+ return False
+ max_misplaced = float(self.get_config('max_misplaced',
+ default_max_misplaced))
+ min_pg_per_osd = 2
+
+ ms = plan.initial
+ osdmap = ms.osdmap
+ crush = osdmap.get_crush()
+ pe = self.calc_eval(ms)
+ if pe.score == 0:
+ self.log.info('Distribution is already perfect')
+ return False
+
+ # get current osd reweights
+ orig_osd_weight = { a['osd']: a['weight']
+ for a in ms.osdmap_dump.get('osds',[]) }
+ reweighted_osds = [ a for a,b in orig_osd_weight.iteritems()
+ if b < 1.0 and b > 0.0 ]
+
+ # get current 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 }
+
+ # Make sure roots don't overlap their devices. If so, we
+ # can't proceed.
+ roots = pe.target_by_root.keys()
+ self.log.debug('roots %s', roots)
+ visited = {}
+ overlap = {}
+ root_ids = {}
+ for root, wm in pe.target_by_root.iteritems():
+ for osd in wm.iterkeys():
+ if osd in visited:
+ overlap[osd] = 1
+ visited[osd] = 1
+ if len(overlap) > 0:
+ self.log.err('error: some osds belong to multiple subtrees: %s' %
+ overlap)
+ return False
+
+ key = 'pgs' # pgs objects or bytes
+
+ # go
+ best_ws = copy.deepcopy(orig_ws)
+ best_ow = copy.deepcopy(orig_osd_weight)
+ best_pe = pe
+ left = max_iterations
+ bad_steps = 0
+ next_ws = copy.deepcopy(best_ws)
+ next_ow = copy.deepcopy(best_ow)
+ while left > 0:
+ # adjust
+ self.log.debug('best_ws %s' % best_ws)
+ random.shuffle(roots)
+ for root in roots:
+ pools = best_pe.root_pools[root]
+ pgs = len(best_pe.target_by_root[root])
+ min_pgs = pgs * min_pg_per_osd
+ if best_pe.total_by_root[root] < min_pgs:
+ self.log.info('Skipping root %s (pools %s), total pgs %d '
+ '< minimum %d (%d per osd)',
+ root, pools, pgs, min_pgs, min_pg_per_osd)
+ continue
+ self.log.info('Balancing root %s (pools %s) by %s' %
+ (root, pools, key))
+ target = best_pe.target_by_root[root]
+ actual = best_pe.actual_by_root[root][key]
+ queue = sorted(actual.keys(),
+ key=lambda osd: -abs(target[osd] - actual[osd]))
+ for osd in queue:
+ if orig_osd_weight[osd] == 0:
+ self.log.debug('skipping out osd.%d', osd)
+ else:
+ deviation = target[osd] - actual[osd]
+ if deviation == 0:
+ break
+ self.log.debug('osd.%d deviation %f', osd, deviation)
+ weight = best_ws[osd]
+ ow = orig_osd_weight[osd]
+ if actual[osd] > 0:
+ calc_weight = target[osd] / actual[osd] * weight * ow
+ else:
+ # not enough to go on here... keep orig weight
+ calc_weight = weight / orig_osd_weight[osd]
+ 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
+ if ow < 1.0:
+ new_ow = min(1.0, max(step + (1.0 - step) * ow,
+ ow + .005))
+ self.log.debug('Reweight osd.%d reweight %f -> %f',
+ osd, ow, new_ow)
+ next_ow[osd] = new_ow
+
+ # normalize weights under this root
+ root_weight = crush.get_item_weight(pe.root_ids[root])
+ root_sum = sum(b for a,b in next_ws.iteritems()
+ if a in target.keys())
+ if root_sum > 0 and root_weight > 0:
+ factor = root_sum / root_weight
+ self.log.debug('normalizing root %s %d, weight %f, '
+ 'ws sum %f, factor %f',
+ root, pe.root_ids[root], root_weight,
+ root_sum, factor)
+ for osd in actual.keys():
+ next_ws[osd] = next_ws[osd] / factor
+
+ # recalc
+ plan.compat_ws = copy.deepcopy(next_ws)
+ next_ms = plan.final_state()
+ next_pe = self.calc_eval(next_ms)
+ next_misplaced = next_ms.calc_misplaced_from(ms)
+ self.log.debug('Step result score %f -> %f, misplacing %f',
+ best_pe.score, next_pe.score, next_misplaced)
+
+ if next_misplaced > max_misplaced:
+ if best_pe.score < pe.score:
+ self.log.debug('Step misplaced %f > max %f, stopping',
+ next_misplaced, max_misplaced)
+ break
+ step /= 2.0
+ next_ws = copy.deepcopy(best_ws)
+ next_ow = copy.deepcopy(best_ow)
+ self.log.debug('Step misplaced %f > max %f, reducing step to %f',
+ next_misplaced, max_misplaced, step)
+ else:
+ if next_pe.score > best_pe.score * 1.0001:
+ if bad_steps < 5 and random.randint(0, 100) < 70:
+ self.log.debug('Score got worse, taking another step')
+ else:
+ step /= 2.0
+ next_ws = copy.deepcopy(best_ws)
+ next_ow = copy.deepcopy(best_ow)
+ self.log.debug('Score got worse, trying smaller step %f',
+ step)
+ else:
+ bad_steps = 0
+ best_pe = next_pe
+ best_ws = next_ws
+ best_ow = next_ow
+ if best_pe.score == 0:
+ break
+ left -= 1
+
+ # allow a small regression if we are phasing out osd weights
+ fudge = 0
+ if next_ow != orig_osd_weight:
+ fudge = .001
+
+ if best_pe.score < pe.score + fudge:
+ self.log.info('Success, score %f -> %f', pe.score, best_pe.score)
+ plan.compat_ws = best_ws
+ for osd, w in best_ow.iteritems():
+ if w != orig_osd_weight[osd]:
+ self.log.debug('osd.%d reweight %f', osd, w)
+ plan.osd_weights[osd] = w
+ return True
+ else:
+ self.log.info('Failed to find further optimization, score %f',
+ pe.score)
+ return False
+
+ def get_compat_weight_set_weights(self):
+ # enable compat weight-set
+ self.log.debug('ceph osd crush weight-set create-compat')
+ result = CommandResult('')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd crush weight-set create-compat',
+ 'format': 'json',
+ }), '')
+ r, outb, outs = result.wait()
+ if r != 0:
+ self.log.error('Error creating compat weight-set')
+ return
+
+ result = CommandResult('')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd crush dump',
+ 'format': 'json',
+ }), '')
+ r, outb, outs = result.wait()
+ if r != 0:
+ self.log.error('Error dumping crush map')
+ return
+ try:
+ crushmap = json.loads(outb)
+ except:
+ raise RuntimeError('unable to parse crush map')
+
+ raw = crushmap.get('choose_args',{}).get('-1', [])
+ weight_set = {}
+ for b in raw:
+ bucket = None
+ for t in crushmap['buckets']:
+ if t['id'] == b['bucket_id']:
+ bucket = t
+ break
+ if not bucket:
+ raise RuntimeError('could not find bucket %s' % b['bucket_id'])
+ self.log.debug('bucket items %s' % bucket['items'])
+ self.log.debug('weight set %s' % b['weight_set'][0])
+ if len(bucket['items']) != len(b['weight_set'][0]):
+ raise RuntimeError('weight-set size does not match bucket items')
+ for pos in range(len(bucket['items'])):
+ weight_set[bucket['items'][pos]['id']] = b['weight_set'][0][pos]
+
+ self.log.debug('weight_set weights %s' % weight_set)
+ return weight_set
+
+ def do_crush(self):
+ self.log.info('do_crush (not yet implemented)')
+
+ def do_osd_weight(self):
+ self.log.info('do_osd_weight (not yet implemented)')
+
+ def execute(self, plan):
+ self.log.info('Executing plan %s' % plan.name)
+
+ commands = []
+
+ # compat weight-set
+ if len(plan.compat_ws) and \
+ '-1' not in plan.initial.crush_dump.get('choose_args', {}):
+ self.log.debug('ceph osd crush weight-set create-compat')
+ result = CommandResult('')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd crush weight-set create-compat',
+ 'format': 'json',
+ }), '')
+ r, outb, outs = result.wait()
+ if r != 0:
+ self.log.error('Error creating compat weight-set')
+ return
+
+ for osd, weight in plan.compat_ws.iteritems():
+ self.log.info('ceph osd crush weight-set reweight-compat osd.%d %f',
+ osd, weight)
+ result = CommandResult('foo')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd crush weight-set reweight-compat',
+ 'format': 'json',
+ 'item': 'osd.%d' % osd,
+ 'weight': [weight],
+ }), 'foo')
+ commands.append(result)
+
+ # new_weight
+ reweightn = {}
+ for osd, weight in plan.osd_weights.iteritems():
+ reweightn[str(osd)] = str(int(weight * float(0x10000)))
+ if len(reweightn):
+ self.log.info('ceph osd reweightn %s', reweightn)
+ result = CommandResult('foo')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd reweightn',
+ 'format': 'json',
+ 'weights': json.dumps(reweightn),
+ }), 'foo')
+ commands.append(result)
+
+ # upmap
+ incdump = plan.inc.dump()
+ for pgid in incdump.get('old_pg_upmap_items', []):
+ self.log.info('ceph osd rm-pg-upmap-items %s', pgid)
+ result = CommandResult('foo')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd rm-pg-upmap-items',
+ 'format': 'json',
+ 'pgid': pgid,
+ }), 'foo')
+ commands.append(result)
+
+ for item in incdump.get('new_pg_upmap_items', []):
+ self.log.info('ceph osd pg-upmap-items %s mappings %s', item['pgid'],
+ item['mappings'])
+ osdlist = []
+ for m in item['mappings']:
+ osdlist += [m['from'], m['to']]
+ result = CommandResult('foo')
+ self.send_command(result, 'mon', '', json.dumps({
+ 'prefix': 'osd pg-upmap-items',
+ 'format': 'json',
+ 'pgid': item['pgid'],
+ 'id': osdlist,
+ }), 'foo')
+ commands.append(result)
+
+ # wait for commands
+ self.log.debug('commands %s' % commands)
+ for result in commands:
+ r, outb, outs = result.wait()
+ if r != 0:
+ self.log.error('Error on command')
+ return
+ self.log.debug('done')
Code Review / stor4nfv.git / blobdiff