# 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.# 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 (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())])