tools/perf/scripts/python/event_analyzing_sample.py - kernel/bruno - Git at Google

 # 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())])
	# 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())])