191 lines
7.3 KiB
Python
Executable file
191 lines
7.3 KiB
Python
Executable file
# event_analyzing_sample.py: general event handler in python
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# SPDX-License-Identifier: GPL-2.0
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#
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# Current perf report is already very powerful with the annotation integrated,
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# and this script is not trying to be as powerful as perf report, but
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# providing end user/developer a flexible way to analyze the events other
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# than trace points.
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#
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# The 2 database related functions in this script just show how to gather
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# the basic information, and users can modify and write their own functions
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# according to their specific requirement.
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#
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# The first function "show_general_events" just does a basic grouping for all
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# generic events with the help of sqlite, and the 2nd one "show_pebs_ll" is
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# for a x86 HW PMU event: PEBS with load latency data.
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#
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import os
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import sys
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import math
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import struct
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import sqlite3
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sys.path.append(os.environ['PERF_EXEC_PATH'] + \
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'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
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from perf_trace_context import *
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from EventClass import *
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#
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# If the perf.data has a big number of samples, then the insert operation
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# will be very time consuming (about 10+ minutes for 10000 samples) if the
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# .db database is on disk. Move the .db file to RAM based FS to speedup
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# the handling, which will cut the time down to several seconds.
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#
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con = sqlite3.connect("/dev/shm/perf.db")
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con.isolation_level = None
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def trace_begin():
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print "In trace_begin:\n"
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#
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# Will create several tables at the start, pebs_ll is for PEBS data with
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# load latency info, while gen_events is for general event.
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#
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con.execute("""
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create table if not exists gen_events (
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name text,
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symbol text,
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comm text,
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dso text
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);""")
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con.execute("""
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create table if not exists pebs_ll (
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name text,
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symbol text,
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comm text,
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dso text,
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flags integer,
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ip integer,
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status integer,
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dse integer,
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dla integer,
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lat integer
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);""")
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#
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# Create and insert event object to a database so that user could
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# do more analysis with simple database commands.
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#
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def process_event(param_dict):
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event_attr = param_dict["attr"]
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sample = param_dict["sample"]
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raw_buf = param_dict["raw_buf"]
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comm = param_dict["comm"]
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name = param_dict["ev_name"]
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# Symbol and dso info are not always resolved
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if (param_dict.has_key("dso")):
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dso = param_dict["dso"]
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else:
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dso = "Unknown_dso"
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if (param_dict.has_key("symbol")):
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symbol = param_dict["symbol"]
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else:
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symbol = "Unknown_symbol"
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# Create the event object and insert it to the right table in database
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event = create_event(name, comm, dso, symbol, raw_buf)
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insert_db(event)
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def insert_db(event):
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if event.ev_type == EVTYPE_GENERIC:
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con.execute("insert into gen_events values(?, ?, ?, ?)",
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(event.name, event.symbol, event.comm, event.dso))
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elif event.ev_type == EVTYPE_PEBS_LL:
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event.ip &= 0x7fffffffffffffff
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event.dla &= 0x7fffffffffffffff
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con.execute("insert into pebs_ll values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
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(event.name, event.symbol, event.comm, event.dso, event.flags,
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event.ip, event.status, event.dse, event.dla, event.lat))
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def trace_end():
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print "In trace_end:\n"
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# We show the basic info for the 2 type of event classes
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show_general_events()
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show_pebs_ll()
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con.close()
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#
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# As the event number may be very big, so we can't use linear way
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# to show the histogram in real number, but use a log2 algorithm.
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#
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def num2sym(num):
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# Each number will have at least one '#'
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snum = '#' * (int)(math.log(num, 2) + 1)
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return snum
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def show_general_events():
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# Check the total record number in the table
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count = con.execute("select count(*) from gen_events")
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for t in count:
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print "There is %d records in gen_events table" % t[0]
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if t[0] == 0:
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return
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print "Statistics about the general events grouped by thread/symbol/dso: \n"
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# Group by thread
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commq = con.execute("select comm, count(comm) from gen_events group by comm order by -count(comm)")
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print "\n%16s %8s %16s\n%s" % ("comm", "number", "histogram", "="*42)
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for row in commq:
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print "%16s %8d %s" % (row[0], row[1], num2sym(row[1]))
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# Group by symbol
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print "\n%32s %8s %16s\n%s" % ("symbol", "number", "histogram", "="*58)
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symbolq = con.execute("select symbol, count(symbol) from gen_events group by symbol order by -count(symbol)")
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for row in symbolq:
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print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
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# Group by dso
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print "\n%40s %8s %16s\n%s" % ("dso", "number", "histogram", "="*74)
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dsoq = con.execute("select dso, count(dso) from gen_events group by dso order by -count(dso)")
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for row in dsoq:
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print "%40s %8d %s" % (row[0], row[1], num2sym(row[1]))
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#
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# This function just shows the basic info, and we could do more with the
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# data in the tables, like checking the function parameters when some
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# big latency events happen.
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#
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def show_pebs_ll():
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count = con.execute("select count(*) from pebs_ll")
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for t in count:
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print "There is %d records in pebs_ll table" % t[0]
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if t[0] == 0:
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return
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print "Statistics about the PEBS Load Latency events grouped by thread/symbol/dse/latency: \n"
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# Group by thread
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commq = con.execute("select comm, count(comm) from pebs_ll group by comm order by -count(comm)")
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print "\n%16s %8s %16s\n%s" % ("comm", "number", "histogram", "="*42)
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for row in commq:
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print "%16s %8d %s" % (row[0], row[1], num2sym(row[1]))
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# Group by symbol
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print "\n%32s %8s %16s\n%s" % ("symbol", "number", "histogram", "="*58)
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symbolq = con.execute("select symbol, count(symbol) from pebs_ll group by symbol order by -count(symbol)")
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for row in symbolq:
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print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
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# Group by dse
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dseq = con.execute("select dse, count(dse) from pebs_ll group by dse order by -count(dse)")
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print "\n%32s %8s %16s\n%s" % ("dse", "number", "histogram", "="*58)
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for row in dseq:
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print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
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# Group by latency
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latq = con.execute("select lat, count(lat) from pebs_ll group by lat order by lat")
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print "\n%32s %8s %16s\n%s" % ("latency", "number", "histogram", "="*58)
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for row in latq:
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print "%32s %8d %s" % (row[0], row[1], num2sym(row[1]))
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def trace_unhandled(event_name, context, event_fields_dict):
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print ' '.join(['%s=%s'%(k,str(v))for k,v in sorted(event_fields_dict.items())])
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