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Fix pylint warnings on event_analyzing_sample.py #12

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Fix pylint warnings on event_analyzing_sample.py #12

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311 changes: 166 additions & 145 deletions tools/perf/scripts/python/event_analyzing_sample.py
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Original file line number Diff line number Diff line change
@@ -1,58 +1,55 @@
# event_analyzing_sample.py: general event handler in python
# SPDX-License-Identifier: GPL-2.0
#
# 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.
#
'''
event_analyzing_sample.py: general event handler in python
SPDX-License-Identifier: GPL-2.0

from __future__ import print_function
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.

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.
'''

from __future__ import print_function
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 *
from perf_trace_context 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.
#

sys.path.append(os.environ['PERF_EXEC_PATH'] +
'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
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("""
def trace_begin():
'''
Will create several tables at the start, pebs_ll is for PEBS data with
load latency info, while gen_events is for general event.'''
print("In trace_begin:\n")
con.execute("""
create table if not exists gen_events (
name text,
symbol text,
comm text,
dso text
);""")
con.execute("""
con.execute("""
create table if not exists pebs_ll (
name text,
symbol text,
Expand All @@ -66,127 +63,151 @@ def trace_begin():
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 ("dso" in param_dict):
dso = param_dict["dso"]
else:
dso = "Unknown_dso"

if ("symbol" in param_dict):
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)
'''
Create and insert event object to a database so that user could
do more analysis with simple database commands.
'''
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 "dso" in param_dict:
dso = param_dict["dso"]
else:
dso = "Unknown_dso"

if "symbol" in param_dict:
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))
'''
Insert into generic and pebs event values
'''
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()
'''
We show the basic info for the 2 type of event classes
'''
print("In trace_end:\n")
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
'''
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.
Each number will have at least one '#'
'''
snum = '#' * (int)(math.log(num, 2) + 1)
return snum


def show_general_events():
'''
basic grouping for all
generic events with the help of sqlite
'''
# Check the total record number in the table
count = con.execute("select count(*) from gen_events")
for cnt in count:
print("There is %d records in gen_events table" % cnt[0])
if cnt[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])))


# 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():
'''
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.
'''
count = con.execute("select count(*) from pebs_ll")
for cnt in count:
print("There is %d records in pebs_ll table" % cnt[0])
if cnt[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])))

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())]))
'''
print unhandled trace
'''
print(' '.join(['%s=%s' % (k, str(v))
for k, v in sorted(event_fields_dict.items())]))