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Update arc_summary and arcstat outputs #14320

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Jan 5, 2023
Merged

Update arc_summary and arcstat outputs #14320

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173 changes: 115 additions & 58 deletions cmd/arc_summary
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Original file line number Diff line number Diff line change
Expand Up @@ -558,8 +558,12 @@ def section_arc(kstats_dict):
arc_target_size = arc_stats['c']
arc_max = arc_stats['c_max']
arc_min = arc_stats['c_min']
anon_size = arc_stats['anon_size']
mfu_size = arc_stats['mfu_size']
mru_size = arc_stats['mru_size']
mfug_size = arc_stats['mfu_ghost_size']
mrug_size = arc_stats['mru_ghost_size']
unc_size = arc_stats['uncached_size']
meta_limit = arc_stats['arc_meta_limit']
meta_size = arc_stats['arc_meta_used']
dnode_limit = arc_stats['arc_dnode_limit']
Expand All @@ -574,11 +578,17 @@ def section_arc(kstats_dict):
f_perc(arc_min, arc_max), f_bytes(arc_min))
prt_i2('Max size (high water):',
target_size_ratio, f_bytes(arc_max))
caches_size = int(mfu_size)+int(mru_size)
caches_size = int(anon_size)+int(mfu_size)+int(mru_size)+int(unc_size)
prt_i2('Anonymouns data size:',
f_perc(anon_size, caches_size), f_bytes(anon_size))
prt_i2('Most Frequently Used (MFU) cache size:',
f_perc(mfu_size, caches_size), f_bytes(mfu_size))
prt_i2('Most Recently Used (MRU) cache size:',
f_perc(mru_size, caches_size), f_bytes(mru_size))
prt_i1('Most Frequently Used (MFU) ghost size:', f_bytes(mfug_size))
prt_i1('Most Recently Used (MRU) ghost size:', f_bytes(mrug_size))
prt_i2('Uncached data size:',
f_perc(unc_size, caches_size), f_bytes(unc_size))
prt_i2('Metadata cache size (hard limit):',
f_perc(meta_limit, arc_max), f_bytes(meta_limit))
prt_i2('Metadata cache size (current):',
Expand Down Expand Up @@ -626,78 +636,119 @@ def section_archits(kstats_dict):
"""

arc_stats = isolate_section('arcstats', kstats_dict)
all_accesses = int(arc_stats['hits'])+int(arc_stats['misses'])
actual_hits = int(arc_stats['mfu_hits'])+int(arc_stats['mru_hits'])

prt_1('ARC total accesses (hits + misses):', f_hits(all_accesses))
ta_todo = (('Cache hit ratio:', arc_stats['hits']),
('Cache miss ratio:', arc_stats['misses']),
('Actual hit ratio (MFU + MRU hits):', actual_hits))
all_accesses = int(arc_stats['hits'])+int(arc_stats['iohits'])+\
int(arc_stats['misses'])

prt_1('ARC total accesses:', f_hits(all_accesses))
ta_todo = (('Total hits:', arc_stats['hits']),
('Total I/O hits:', arc_stats['iohits']),
('Total misses:', arc_stats['misses']))
for title, value in ta_todo:
prt_i2(title, f_perc(value, all_accesses), f_hits(value))
print()

dd_total = int(arc_stats['demand_data_hits']) +\
int(arc_stats['demand_data_iohits']) +\
int(arc_stats['demand_data_misses'])
prt_i2('Data demand efficiency:',
f_perc(arc_stats['demand_data_hits'], dd_total),
f_hits(dd_total))
prt_2('ARC demand data accesses:', f_perc(dd_total, all_accesses),
f_hits(dd_total))
dd_todo = (('Demand data hits:', arc_stats['demand_data_hits']),
('Demand data I/O hits:', arc_stats['demand_data_iohits']),
('Demand data misses:', arc_stats['demand_data_misses']))
for title, value in dd_todo:
prt_i2(title, f_perc(value, dd_total), f_hits(value))
print()

dm_total = int(arc_stats['demand_metadata_hits']) +\
int(arc_stats['demand_metadata_iohits']) +\
int(arc_stats['demand_metadata_misses'])
prt_2('ARC demand metadata accesses:', f_perc(dm_total, all_accesses),
f_hits(dm_total))
dm_todo = (('Demand metadata hits:', arc_stats['demand_metadata_hits']),
('Demand metadata I/O hits:',
arc_stats['demand_metadata_iohits']),
('Demand metadata misses:', arc_stats['demand_metadata_misses']))
for title, value in dm_todo:
prt_i2(title, f_perc(value, dm_total), f_hits(value))
print()

dp_total = int(arc_stats['prefetch_data_hits']) +\
pd_total = int(arc_stats['prefetch_data_hits']) +\
int(arc_stats['prefetch_data_iohits']) +\
int(arc_stats['prefetch_data_misses'])
prt_i2('Data prefetch efficiency:',
f_perc(arc_stats['prefetch_data_hits'], dp_total),
f_hits(dp_total))
prt_2('ARC prefetch metadata accesses:', f_perc(pd_total, all_accesses),
f_hits(pd_total))
pd_todo = (('Prefetch data hits:', arc_stats['prefetch_data_hits']),
('Prefetch data I/O hits:', arc_stats['prefetch_data_iohits']),
('Prefetch data misses:', arc_stats['prefetch_data_misses']))
for title, value in pd_todo:
prt_i2(title, f_perc(value, pd_total), f_hits(value))
print()

known_hits = int(arc_stats['mfu_hits']) +\
int(arc_stats['mru_hits']) +\
int(arc_stats['mfu_ghost_hits']) +\
int(arc_stats['mru_ghost_hits'])
pm_total = int(arc_stats['prefetch_metadata_hits']) +\
int(arc_stats['prefetch_metadata_iohits']) +\
int(arc_stats['prefetch_metadata_misses'])
prt_2('ARC prefetch metadata accesses:', f_perc(pm_total, all_accesses),
f_hits(pm_total))
pm_todo = (('Prefetch metadata hits:',
arc_stats['prefetch_metadata_hits']),
('Prefetch metadata I/O hits:',
arc_stats['prefetch_metadata_iohits']),
('Prefetch metadata misses:',
arc_stats['prefetch_metadata_misses']))
for title, value in pm_todo:
prt_i2(title, f_perc(value, pm_total), f_hits(value))
print()

anon_hits = int(arc_stats['hits'])-known_hits
all_prefetches = int(arc_stats['predictive_prefetch'])+\
int(arc_stats['prescient_prefetch'])
prt_2('ARC predictive prefetches:',
f_perc(arc_stats['predictive_prefetch'], all_prefetches),
f_hits(arc_stats['predictive_prefetch']))
prt_i2('Demand hits after predictive:',
f_perc(arc_stats['demand_hit_predictive_prefetch'],
arc_stats['predictive_prefetch']),
f_hits(arc_stats['demand_hit_predictive_prefetch']))
prt_i2('Demand I/O hits after predictive:',
f_perc(arc_stats['demand_iohit_predictive_prefetch'],
arc_stats['predictive_prefetch']),
f_hits(arc_stats['demand_iohit_predictive_prefetch']))
never = int(arc_stats['predictive_prefetch']) -\
int(arc_stats['demand_hit_predictive_prefetch']) -\
int(arc_stats['demand_iohit_predictive_prefetch'])
prt_i2('Never demanded after predictive:',
f_perc(never, arc_stats['predictive_prefetch']),
f_hits(never))
print()

prt_2('ARC prescient prefetches:',
f_perc(arc_stats['prescient_prefetch'], all_prefetches),
f_hits(arc_stats['prescient_prefetch']))
prt_i2('Demand hits after prescient:',
f_perc(arc_stats['demand_hit_prescient_prefetch'],
arc_stats['prescient_prefetch']),
f_hits(arc_stats['demand_hit_prescient_prefetch']))
prt_i2('Demand I/O hits after prescient:',
f_perc(arc_stats['demand_iohit_prescient_prefetch'],
arc_stats['prescient_prefetch']),
f_hits(arc_stats['demand_iohit_prescient_prefetch']))
never = int(arc_stats['prescient_prefetch'])-\
int(arc_stats['demand_hit_prescient_prefetch'])-\
int(arc_stats['demand_iohit_prescient_prefetch'])
prt_i2('Never demanded after prescient:',
f_perc(never, arc_stats['prescient_prefetch']),
f_hits(never))
print()
print('Cache hits by cache type:')

print('ARC states hits of all accesses:')
cl_todo = (('Most frequently used (MFU):', arc_stats['mfu_hits']),
('Most recently used (MRU):', arc_stats['mru_hits']),
('Most frequently used (MFU) ghost:',
arc_stats['mfu_ghost_hits']),
('Most recently used (MRU) ghost:',
arc_stats['mru_ghost_hits']))

arc_stats['mru_ghost_hits']),
('Uncached:', arc_stats['uncached_hits']))
for title, value in cl_todo:
prt_i2(title, f_perc(value, arc_stats['hits']), f_hits(value))

# For some reason, anon_hits can turn negative, which is weird. Until we
# have figured out why this happens, we just hide the problem, following
# the behavior of the original arc_summary.
if anon_hits >= 0:
prt_i2('Anonymously used:',
f_perc(anon_hits, arc_stats['hits']), f_hits(anon_hits))

print()
print('Cache hits by data type:')
dt_todo = (('Demand data:', arc_stats['demand_data_hits']),
('Prefetch data:', arc_stats['prefetch_data_hits']),
('Demand metadata:', arc_stats['demand_metadata_hits']),
('Prefetch metadata:',
arc_stats['prefetch_metadata_hits']))

for title, value in dt_todo:
prt_i2(title, f_perc(value, arc_stats['hits']), f_hits(value))

print()
print('Cache misses by data type:')
dm_todo = (('Demand data:', arc_stats['demand_data_misses']),
('Prefetch data:',
arc_stats['prefetch_data_misses']),
('Demand metadata:', arc_stats['demand_metadata_misses']),
('Prefetch metadata:',
arc_stats['prefetch_metadata_misses']))

for title, value in dm_todo:
prt_i2(title, f_perc(value, arc_stats['misses']), f_hits(value))

prt_i2(title, f_perc(value, all_accesses), f_hits(value))
print()


Expand All @@ -708,11 +759,17 @@ def section_dmu(kstats_dict):

zfetch_access_total = int(zfetch_stats['hits'])+int(zfetch_stats['misses'])

prt_1('DMU prefetch efficiency:', f_hits(zfetch_access_total))
prt_i2('Hit ratio:', f_perc(zfetch_stats['hits'], zfetch_access_total),
prt_1('DMU predictive prefetcher calls:', f_hits(zfetch_access_total))
prt_i2('Stream hits:',
f_perc(zfetch_stats['hits'], zfetch_access_total),
f_hits(zfetch_stats['hits']))
prt_i2('Miss ratio:', f_perc(zfetch_stats['misses'], zfetch_access_total),
prt_i2('Stream misses:',
f_perc(zfetch_stats['misses'], zfetch_access_total),
f_hits(zfetch_stats['misses']))
prt_i2('Streams limit reached:',
f_perc(zfetch_stats['max_streams'], zfetch_stats['misses']),
f_hits(zfetch_stats['max_streams']))
prt_i1('Prefetches issued', f_hits(zfetch_stats['io_issued']))
print()


Expand Down
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