nfs: disintegrate UAPI for nfs

This is to complete part of the Userspace API (UAPI) disintegration for which
the preparatory patches were pulled recently.  After these patches, userspace
headers will be segregated into:

        include/uapi/linux/.../foo.h

for the userspace interface stuff, and:

        include/linux/.../foo.h

for the strictly kernel internal stuff.

Signed-off-by: J. Bruce Fields <bfields@redhat.com>

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())])