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Update arc_summary and arcstat outputs
Recent ARC commits added new statistic counters, such as iohits, uncached state, etc. Represent those. Also some of previously reported numbers were confusing or even made no sense. Cleanup and restructure existing reports. Reviewed-by: Ryan Moeller <ryan@iXsystems.com> Reviewed-by: Brian Behlendorf <behlendorf1@llnl.gov> Signed-off-by: Alexander Motin <mav@FreeBSD.org> Sponsored by: iXsystems, Inc. Issue #14115 Issue #14123 Issue #14243 Closes #14320
This commit is contained in:
Alexander Motin
+120
-63
@@ -558,8 +558,12 @@ def section_arc(kstats_dict):
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arc_target_size = arc_stats['c']
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arc_max = arc_stats['c_max']
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arc_min = arc_stats['c_min']
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anon_size = arc_stats['anon_size']
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mfu_size = arc_stats['mfu_size']
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mru_size = arc_stats['mru_size']
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mfug_size = arc_stats['mfu_ghost_size']
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mrug_size = arc_stats['mru_ghost_size']
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unc_size = arc_stats['uncached_size']
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meta_limit = arc_stats['arc_meta_limit']
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meta_size = arc_stats['arc_meta_used']
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dnode_limit = arc_stats['arc_dnode_limit']
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@@ -574,11 +578,17 @@ def section_arc(kstats_dict):
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f_perc(arc_min, arc_max), f_bytes(arc_min))
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prt_i2('Max size (high water):',
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target_size_ratio, f_bytes(arc_max))
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caches_size = int(mfu_size)+int(mru_size)
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caches_size = int(anon_size)+int(mfu_size)+int(mru_size)+int(unc_size)
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prt_i2('Anonymouns data size:',
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f_perc(anon_size, caches_size), f_bytes(anon_size))
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prt_i2('Most Frequently Used (MFU) cache size:',
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f_perc(mfu_size, caches_size), f_bytes(mfu_size))
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prt_i2('Most Recently Used (MRU) cache size:',
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f_perc(mru_size, caches_size), f_bytes(mru_size))
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prt_i1('Most Frequently Used (MFU) ghost size:', f_bytes(mfug_size))
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prt_i1('Most Recently Used (MRU) ghost size:', f_bytes(mrug_size))
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prt_i2('Uncached data size:',
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f_perc(unc_size, caches_size), f_bytes(unc_size))
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prt_i2('Metadata cache size (hard limit):',
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f_perc(meta_limit, arc_max), f_bytes(meta_limit))
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prt_i2('Metadata cache size (current):',
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@@ -626,78 +636,119 @@ def section_archits(kstats_dict):
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"""
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arc_stats = isolate_section('arcstats', kstats_dict)
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all_accesses = int(arc_stats['hits'])+int(arc_stats['misses'])
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actual_hits = int(arc_stats['mfu_hits'])+int(arc_stats['mru_hits'])
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prt_1('ARC total accesses (hits + misses):', f_hits(all_accesses))
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ta_todo = (('Cache hit ratio:', arc_stats['hits']),
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('Cache miss ratio:', arc_stats['misses']),
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('Actual hit ratio (MFU + MRU hits):', actual_hits))
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all_accesses = int(arc_stats['hits'])+int(arc_stats['iohits'])+\
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int(arc_stats['misses'])
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prt_1('ARC total accesses:', f_hits(all_accesses))
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ta_todo = (('Total hits:', arc_stats['hits']),
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('Total I/O hits:', arc_stats['iohits']),
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('Total misses:', arc_stats['misses']))
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for title, value in ta_todo:
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prt_i2(title, f_perc(value, all_accesses), f_hits(value))
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print()
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dd_total = int(arc_stats['demand_data_hits']) +\
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int(arc_stats['demand_data_iohits']) +\
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int(arc_stats['demand_data_misses'])
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prt_i2('Data demand efficiency:',
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f_perc(arc_stats['demand_data_hits'], dd_total),
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f_hits(dd_total))
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dp_total = int(arc_stats['prefetch_data_hits']) +\
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int(arc_stats['prefetch_data_misses'])
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prt_i2('Data prefetch efficiency:',
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f_perc(arc_stats['prefetch_data_hits'], dp_total),
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f_hits(dp_total))
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known_hits = int(arc_stats['mfu_hits']) +\
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int(arc_stats['mru_hits']) +\
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int(arc_stats['mfu_ghost_hits']) +\
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int(arc_stats['mru_ghost_hits'])
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anon_hits = int(arc_stats['hits'])-known_hits
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prt_2('ARC demand data accesses:', f_perc(dd_total, all_accesses),
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f_hits(dd_total))
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dd_todo = (('Demand data hits:', arc_stats['demand_data_hits']),
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('Demand data I/O hits:', arc_stats['demand_data_iohits']),
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('Demand data misses:', arc_stats['demand_data_misses']))
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for title, value in dd_todo:
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prt_i2(title, f_perc(value, dd_total), f_hits(value))
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print()
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print('Cache hits by cache type:')
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dm_total = int(arc_stats['demand_metadata_hits']) +\
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int(arc_stats['demand_metadata_iohits']) +\
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int(arc_stats['demand_metadata_misses'])
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prt_2('ARC demand metadata accesses:', f_perc(dm_total, all_accesses),
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f_hits(dm_total))
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dm_todo = (('Demand metadata hits:', arc_stats['demand_metadata_hits']),
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('Demand metadata I/O hits:',
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arc_stats['demand_metadata_iohits']),
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('Demand metadata misses:', arc_stats['demand_metadata_misses']))
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for title, value in dm_todo:
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prt_i2(title, f_perc(value, dm_total), f_hits(value))
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print()
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pd_total = int(arc_stats['prefetch_data_hits']) +\
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int(arc_stats['prefetch_data_iohits']) +\
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int(arc_stats['prefetch_data_misses'])
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prt_2('ARC prefetch metadata accesses:', f_perc(pd_total, all_accesses),
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f_hits(pd_total))
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pd_todo = (('Prefetch data hits:', arc_stats['prefetch_data_hits']),
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('Prefetch data I/O hits:', arc_stats['prefetch_data_iohits']),
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('Prefetch data misses:', arc_stats['prefetch_data_misses']))
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for title, value in pd_todo:
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prt_i2(title, f_perc(value, pd_total), f_hits(value))
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print()
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pm_total = int(arc_stats['prefetch_metadata_hits']) +\
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int(arc_stats['prefetch_metadata_iohits']) +\
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int(arc_stats['prefetch_metadata_misses'])
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prt_2('ARC prefetch metadata accesses:', f_perc(pm_total, all_accesses),
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f_hits(pm_total))
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pm_todo = (('Prefetch metadata hits:',
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arc_stats['prefetch_metadata_hits']),
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('Prefetch metadata I/O hits:',
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arc_stats['prefetch_metadata_iohits']),
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('Prefetch metadata misses:',
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arc_stats['prefetch_metadata_misses']))
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for title, value in pm_todo:
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prt_i2(title, f_perc(value, pm_total), f_hits(value))
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print()
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all_prefetches = int(arc_stats['predictive_prefetch'])+\
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int(arc_stats['prescient_prefetch'])
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prt_2('ARC predictive prefetches:',
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f_perc(arc_stats['predictive_prefetch'], all_prefetches),
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f_hits(arc_stats['predictive_prefetch']))
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prt_i2('Demand hits after predictive:',
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f_perc(arc_stats['demand_hit_predictive_prefetch'],
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arc_stats['predictive_prefetch']),
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f_hits(arc_stats['demand_hit_predictive_prefetch']))
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prt_i2('Demand I/O hits after predictive:',
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f_perc(arc_stats['demand_iohit_predictive_prefetch'],
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arc_stats['predictive_prefetch']),
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f_hits(arc_stats['demand_iohit_predictive_prefetch']))
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never = int(arc_stats['predictive_prefetch']) -\
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int(arc_stats['demand_hit_predictive_prefetch']) -\
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int(arc_stats['demand_iohit_predictive_prefetch'])
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prt_i2('Never demanded after predictive:',
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f_perc(never, arc_stats['predictive_prefetch']),
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f_hits(never))
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print()
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prt_2('ARC prescient prefetches:',
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f_perc(arc_stats['prescient_prefetch'], all_prefetches),
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f_hits(arc_stats['prescient_prefetch']))
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prt_i2('Demand hits after prescient:',
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f_perc(arc_stats['demand_hit_prescient_prefetch'],
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arc_stats['prescient_prefetch']),
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f_hits(arc_stats['demand_hit_prescient_prefetch']))
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prt_i2('Demand I/O hits after prescient:',
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f_perc(arc_stats['demand_iohit_prescient_prefetch'],
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arc_stats['prescient_prefetch']),
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f_hits(arc_stats['demand_iohit_prescient_prefetch']))
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never = int(arc_stats['prescient_prefetch'])-\
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int(arc_stats['demand_hit_prescient_prefetch'])-\
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int(arc_stats['demand_iohit_prescient_prefetch'])
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prt_i2('Never demanded after prescient:',
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f_perc(never, arc_stats['prescient_prefetch']),
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f_hits(never))
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print()
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print('ARC states hits of all accesses:')
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cl_todo = (('Most frequently used (MFU):', arc_stats['mfu_hits']),
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('Most recently used (MRU):', arc_stats['mru_hits']),
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('Most frequently used (MFU) ghost:',
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arc_stats['mfu_ghost_hits']),
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('Most recently used (MRU) ghost:',
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arc_stats['mru_ghost_hits']))
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arc_stats['mru_ghost_hits']),
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('Uncached:', arc_stats['uncached_hits']))
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for title, value in cl_todo:
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prt_i2(title, f_perc(value, arc_stats['hits']), f_hits(value))
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# For some reason, anon_hits can turn negative, which is weird. Until we
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# have figured out why this happens, we just hide the problem, following
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# the behavior of the original arc_summary.
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if anon_hits >= 0:
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prt_i2('Anonymously used:',
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f_perc(anon_hits, arc_stats['hits']), f_hits(anon_hits))
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print()
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print('Cache hits by data type:')
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dt_todo = (('Demand data:', arc_stats['demand_data_hits']),
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('Prefetch data:', arc_stats['prefetch_data_hits']),
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('Demand metadata:', arc_stats['demand_metadata_hits']),
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('Prefetch metadata:',
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arc_stats['prefetch_metadata_hits']))
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for title, value in dt_todo:
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prt_i2(title, f_perc(value, arc_stats['hits']), f_hits(value))
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print()
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print('Cache misses by data type:')
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dm_todo = (('Demand data:', arc_stats['demand_data_misses']),
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('Prefetch data:',
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arc_stats['prefetch_data_misses']),
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('Demand metadata:', arc_stats['demand_metadata_misses']),
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('Prefetch metadata:',
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arc_stats['prefetch_metadata_misses']))
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for title, value in dm_todo:
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prt_i2(title, f_perc(value, arc_stats['misses']), f_hits(value))
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prt_i2(title, f_perc(value, all_accesses), f_hits(value))
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print()
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@@ -708,11 +759,17 @@ def section_dmu(kstats_dict):
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zfetch_access_total = int(zfetch_stats['hits'])+int(zfetch_stats['misses'])
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prt_1('DMU prefetch efficiency:', f_hits(zfetch_access_total))
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prt_i2('Hit ratio:', f_perc(zfetch_stats['hits'], zfetch_access_total),
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prt_1('DMU predictive prefetcher calls:', f_hits(zfetch_access_total))
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prt_i2('Stream hits:',
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f_perc(zfetch_stats['hits'], zfetch_access_total),
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f_hits(zfetch_stats['hits']))
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prt_i2('Miss ratio:', f_perc(zfetch_stats['misses'], zfetch_access_total),
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prt_i2('Stream misses:',
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f_perc(zfetch_stats['misses'], zfetch_access_total),
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f_hits(zfetch_stats['misses']))
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prt_i2('Streams limit reached:',
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f_perc(zfetch_stats['max_streams'], zfetch_stats['misses']),
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f_hits(zfetch_stats['max_streams']))
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prt_i1('Prefetches issued', f_hits(zfetch_stats['io_issued']))
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print()
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