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(I'm so sorry!) Linux kernel mirror (for testing) git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git
kernel os linux
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123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193# 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.#
from __future__ import print_function
import osimport sysimport mathimport structimport 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 ("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))
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())]))