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