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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | # event_analyzing_sample.py: general event handler in python
#
# Current perf report is already very powerful with the annotation integrated,
# and this script is not trying to be as powerful as perf report, but
# providing end user/developer a flexible way to analyze the events other
# than trace points.
#
# The 2 database related functions in this script just show how to gather
# the basic information, and users can modify and write their own functions
# according to their specific requirement.
#
# The first function "show_general_events" just does a basic grouping for all
# generic events with the help of sqlite, and the 2nd one "show_pebs_ll" is
# for a x86 HW PMU event: PEBS with load latency data.
#
import os
import sys
import math
import struct
import sqlite3
sys.path.append(os.environ['PERF_EXEC_PATH'] + \
'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
from perf_trace_context import *
from EventClass import *
#
# If the perf.data has a big number of samples, then the insert operation
# will be very time consuming (about 10+ minutes for 10000 samples) if the
# .db database is on disk. Move the .db file to RAM based FS to speedup
# the handling, which will cut the time down to several seconds.
#
con = sqlite3.connect("/dev/shm/perf.db")
con.isolation_level = None
def trace_begin():
print "In trace_begin:\n"
#
# Will create several tables at the start, pebs_ll is for PEBS data with
# load latency info, while gen_events is for general event.
#
con.execute("""
create table if not exists gen_events (
name text,
symbol text,
comm text,
dso text
);""")
con.execute("""
create table if not exists pebs_ll (
name text,
symbol text,
comm text,
dso text,
flags integer,
ip integer,
status integer,
dse integer,
dla integer,
lat integer
);""")
#
# Create and insert event object to a database so that user could
# do more analysis with simple database commands.
#
def process_event(param_dict):
event_attr = param_dict["attr"]
sample = param_dict["sample"]
raw_buf = param_dict["raw_buf"]
comm = param_dict["comm"]
name = param_dict["ev_name"]
# Symbol and dso info are not always resolved
if (param_dict.has_key("dso")):
dso = param_dict["dso"]
else:
dso = "Unknown_dso"
if (param_dict.has_key("symbol")):
symbol = param_dict["symbol"]
else:
symbol = "Unknown_symbol"
# Create the event object and insert it to the right table in database
event = create_event(name, comm, dso, symbol, raw_buf)
insert_db(event)
def insert_db(event):
if event.ev_type == EVTYPE_GENERIC:
con.execute("insert into gen_events values(?, ?, ?, ?)",
(event.name, event.symbol, event.comm, event.dso))
elif event.ev_type == EVTYPE_PEBS_LL:
event.ip &= 0x7fffffffffffffff
event.dla &= 0x7fffffffffffffff
con.execute("insert into pebs_ll values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
(event.name, event.symbol, event.comm, event.dso, event.flags,
event.ip, event.status, event.dse, event.dla, event.lat))
def trace_end():
print "In trace_end:\n"
# We show the basic info for the 2 type of event classes
show_general_events()
show_pebs_ll()
con.close()
#
# As the event number may be very big, so we can't use linear way
# to show the histogram in real number, but use a log2 algorithm.
#
def num2sym(num):
# Each number will have at least one '#'
snum = '#' * (int)(math.log(num, 2) + 1)
return snum
def show_general_events():
# Check the total record number in the table
count = con.execute("select count(*) from gen_events")
for t in count:
print "There is %d records in gen_events table" % t[0]
if t[0] == 0:
return
print "Statistics about the general events grouped by thread/symbol/dso: \n"
# Group by thread
commq = con.execute("select comm, count(comm) from gen_events group by comm order by -count(comm)")
print "\n%16s %8s %16s\n%s" % ("comm", "number", "histogram", "="*42)
for row in commq:
print "%16s %8d %s" % (row[0], row[1], num2sym(row[1]))
# Group by symbol
print "\n%32s %8s %16s\n%s" % ("symbol", "number", "histogram", "="*58)
symbolq = con.execute("select symbol, count(symbol) from gen_events group by symbol order by -count(symbol)")
for row in symbolq:
print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
# Group by dso
print "\n%40s %8s %16s\n%s" % ("dso", "number", "histogram", "="*74)
dsoq = con.execute("select dso, count(dso) from gen_events group by dso order by -count(dso)")
for row in dsoq:
print "%40s %8d %s" % (row[0], row[1], num2sym(row[1]))
#
# This function just shows the basic info, and we could do more with the
# data in the tables, like checking the function parameters when some
# big latency events happen.
#
def show_pebs_ll():
count = con.execute("select count(*) from pebs_ll")
for t in count:
print "There is %d records in pebs_ll table" % t[0]
if t[0] == 0:
return
print "Statistics about the PEBS Load Latency events grouped by thread/symbol/dse/latency: \n"
# Group by thread
commq = con.execute("select comm, count(comm) from pebs_ll group by comm order by -count(comm)")
print "\n%16s %8s %16s\n%s" % ("comm", "number", "histogram", "="*42)
for row in commq:
print "%16s %8d %s" % (row[0], row[1], num2sym(row[1]))
# Group by symbol
print "\n%32s %8s %16s\n%s" % ("symbol", "number", "histogram", "="*58)
symbolq = con.execute("select symbol, count(symbol) from pebs_ll group by symbol order by -count(symbol)")
for row in symbolq:
print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
# Group by dse
dseq = con.execute("select dse, count(dse) from pebs_ll group by dse order by -count(dse)")
print "\n%32s %8s %16s\n%s" % ("dse", "number", "histogram", "="*58)
for row in dseq:
print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
# Group by latency
latq = con.execute("select lat, count(lat) from pebs_ll group by lat order by lat")
print "\n%32s %8s %16s\n%s" % ("latency", "number", "histogram", "="*58)
for row in latq:
print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
def trace_unhandled(event_name, context, event_fields_dict):
print ' '.join(['%s=%s'%(k,str(v))for k,v in sorted(event_fields_dict.items())])
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