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Editing: dcstat-bpfcc
#! /usr/bin/python3 # @lint-avoid-python-3-compatibility-imports # # dcstat Directory entry cache (dcache) stats. # For Linux, uses BCC, eBPF. # # USAGE: dcstat [interval [count]] # # This uses kernel dynamic tracing of kernel functions, lookup_fast() and # d_lookup(), which will need to be modified to match kernel changes. See # code comments. # # Copyright 2016 Netflix, Inc. # Licensed under the Apache License, Version 2.0 (the "License") # # 09-Feb-2016 Brendan Gregg Created this. from __future__ import print_function from bcc import BPF from ctypes import c_int from time import sleep, strftime from sys import argv def usage(): print("USAGE: %s [interval [count]]" % argv[0]) exit() # arguments interval = 1 count = -1 if len(argv) > 1: try: interval = int(argv[1]) if interval == 0: raise if len(argv) > 2: count = int(argv[2]) except: # also catches -h, --help usage() # define BPF program bpf_text = """ #include <uapi/linux/ptrace.h> enum stats { S_REFS = 1, S_SLOW, S_MISS, S_MAXSTAT }; BPF_ARRAY(stats, u64, S_MAXSTAT); /* * How this is instrumented, and how to interpret the statistics, is very much * tied to the current kernel implementation (this was written on Linux 4.4). * This will need maintenance to keep working as the implementation changes. To * aid future adventurers, this is is what the current code does, and why. * * First problem: the current implementation takes a path and then does a * lookup of each component. So how do we count a reference? Once for the path * lookup, or once for every component lookup? I've chosen the latter * since it seems to map more closely to actual dcache lookups (via * __d_lookup_rcu()). It's counted via calls to lookup_fast(). * * The implementation tries different, progressively slower, approaches to * lookup a file. At what point do we call it a dcache miss? I've chosen when * a d_lookup() (which is called during lookup_slow()) returns zero. * * I've also included a "SLOW" statistic to show how often the fast lookup * failed. Whether this exists or is interesting is an implementation detail, * and the "SLOW" statistic may be removed in future versions. */ void count_fast(struct pt_regs *ctx) { int key = S_REFS; stats.atomic_increment(key); } void count_lookup(struct pt_regs *ctx) { int key = S_SLOW; stats.atomic_increment(key); if (PT_REGS_RC(ctx) == 0) { key = S_MISS; stats.atomic_increment(key); } } """ # load BPF program b = BPF(text=bpf_text) b.attach_kprobe(event_re=r'^lookup_fast$|^lookup_fast.constprop.*.\d$', fn_name="count_fast") b.attach_kretprobe(event="d_lookup", fn_name="count_lookup") # stat column labels and indexes stats = { "REFS": 1, "SLOW": 2, "MISS": 3 } # header print("%-8s " % "TIME", end="") for stype, idx in sorted(stats.items(), key=lambda k_v: (k_v[1], k_v[0])): print(" %8s" % (stype + "/s"), end="") print(" %8s" % "HIT%") # output i = 0 while (1): if count > 0: i += 1 if i > count: exit() try: sleep(interval) except KeyboardInterrupt: exit() print("%-8s: " % strftime("%H:%M:%S"), end="") # print each statistic as a column for stype, idx in sorted(stats.items(), key=lambda k_v: (k_v[1], k_v[0])): try: val = b["stats"][c_int(idx)].value / interval print(" %8d" % val, end="") except: print(" %8d" % 0, end="") # print hit ratio percentage try: ref = b["stats"][c_int(stats["REFS"])].value miss = b["stats"][c_int(stats["MISS"])].value hit = ref - miss pct = float(100) * hit / ref print(" %8.2f" % pct) except: print(" %7s%%" % "-") b["stats"].clear()
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