diff options
| author | Johannes Berg <johannes.berg@intel.com> | 2012-10-15 17:20:54 +0200 | 
|---|---|---|
| committer | Johannes Berg <johannes.berg@intel.com> | 2012-10-15 17:20:54 +0200 | 
| commit | df9b42963f2d010ae3163a894ce22cf6b27cd344 (patch) | |
| tree | f42f826d9bb975766c1a79986c39e64c9a900908 /tools/perf/scripts/python/event_analyzing_sample.py | |
| parent | 33766368f6532313571534f9112b1796d6651bbe (diff) | |
| parent | c3e7724b6bc2f25e46c38dbe68f09d71fafeafb8 (diff) | |
Merge remote-tracking branch 'wireless/master' into mac80211
Diffstat (limited to 'tools/perf/scripts/python/event_analyzing_sample.py')
| -rw-r--r-- | tools/perf/scripts/python/event_analyzing_sample.py | 189 | 
1 files changed, 189 insertions, 0 deletions
| diff --git a/tools/perf/scripts/python/event_analyzing_sample.py b/tools/perf/scripts/python/event_analyzing_sample.py new file mode 100644 index 000000000000..163c39fa12d9 --- /dev/null +++ b/tools/perf/scripts/python/event_analyzing_sample.py @@ -0,0 +1,189 @@ +# 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())]) | 
