# 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 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 ("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())]))