--- /dev/null
+# 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())])
Code Review / kvmfornfv.git / blobdiff