类似top命令,查看erlang进程占用cpu、内存较高的进程
参数:
node atom erlang node
port integer The used port
accumulate boolean If true execution time is accumulated
lines integer Number of displayed processes
interval integer Display update interval in secs
sort runtime | reductions | memory | msg_q
output graphical | text
tracing on | off
setcookie string
使用举例:
1. 找出cpu占用最高的进程,图形界面输出,每10秒更新一次
> spawn(fun() -> etop:start([{interval,10}, {sort, runtime}]) end). > etop:stop().
2. 找出内存占用较高进程, 输出进程的数量为20,文本形式输出
> spawn(fun() -> etop:start([{output, text}, {lines, 20}, {sort, memory}]) end). > etop:stop().
3. 查看远程节点etop:
> erl -name abc@192.168.17.102 -hidden -s etop -output text -sort memory -lines 20 -node 'test@192.168.17.102' -setcookie mycookie123
或者:
> erl -name abc@192.168.17.102 -hidden > etop:start([{node,'test@192.168.17.102'}, {setcookie, "mycookie123"}, {output, text}, {lines, 20}, {sort, memory}])
> erl -name abc@192.168.17.102 -setcookie mycookie123 > rpc:call('test@192.168.17.102', etop, start, [{output, text}, {lines, 20}, {sort, memory}]).
假设我们使用etop查到了cpu占用时间较多的进程id,那么可以使用eprof进行进一步的分析.
基本用法:
> eprof:start(). > eprof:profile([pid(x,x,x)]). > eprof:stop_profiling(). > eprof:analyze(). > eprof:stop().
或:
> eprof:start_profiling([regNames], {gen, call, 4}). > eprof:stop_profiling(). > eprof:analyze(). > eprof:stop().
regNames可以填写进程的注册名, {gen, call, 4}表示只记录gen:call/4这个函数
analyze结果示例:
****** Process <0.60.0> -- 100.00 % of profiled time *** FUNCTION CALLS % TIME [uS / CALLS] -------- ----- --- ---- [----------] gen:call/4 2 0.00 0 [ 0.00] gen:do_call/4 2 0.22 1 [ 0.50] gen_server:call/2 2 0.44 2 [ 1.00] dbutil:i_connect/1 2 0.66 3 [ 1.50] gen:call/3 2 0.66 3 [ 1.50] resource_pool:get/1 2 0.66 3 [ 1.50] mvar:modify/2 2 0.66 3 [ 1.50] gen_server:decode_msg/8 4 0.88 4 [ 1.00] erlang:monitor/2 2 0.88 4 [ 2.00] erlang:demonitor/2 2 1.33 6 [ 3.00] gen_server:handle_msg/5 4 1.55 7 [ 1.75] myserver:handle_call/3 4 1.77 8 [ 2.00] gen_server:loop/6 4 1.99 9 [ 2.25] erlang:send/3 2 3.76 17 [ 8.50] gen_server:reply/2 4 84.51 382 [ 95.50]
fprof类似eprof,但是会把详细信息存储到文件中,方便数据统计分析。
只看某一函数的简单调用方法:
1> fprof:apply(Module, fun, Args). 2> fprof:profile(). 3> fprof:analyse().
实际上在执行的时候,fprof:apply/3前后会自动添加trace([start, ...]) 和 trace(stop).
完整的写法是:
> fprof:trace([start, {file, "./fprof.trace"}, {procs, PidSpec}]). %% 或者可以trace多个Pid,[PidSpec] > fprof:trace(stop). > fprof:profile({file, "./fprof.trace"}). > fprof:analyse([{dest, "fprof.analysis"}, append, {sort,own}]). %% 详细参数见: http://www.erlang.org/doc/man/fprof.html#analyse-2
结果示例:
1> fprof:apply(lists, reverse, ["abcdef"]). "fedcba" 2> fprof:profile(). Reading trace data... End of trace! ok 3> fprof:analyse(). Processing data... Creating output... %% Analysis results: { analysis_options, [{callers, true}, {sort, acc}, {totals, false}, {details, true}]}. % CNT ACC OWN [{ totals, 3, 0.027, 0.027}]. %%% % CNT ACC OWN [{ "<0.33.0>", 3,undefined, 0.027}]. %% {[{undefined, 0, 0.027, 0.019}], { {fprof,apply_start_stop,4}, 0, 0.027, 0.019}, % [{{lists,reverse,1}, 1, 0.008, 0.005}, {suspend, 1, 0.000, 0.000}]}. {[{{fprof,apply_start_stop,4}, 1, 0.008, 0.005}], { {lists,reverse,1}, 1, 0.008, 0.005}, % [{{lists,reverse,2}, 1, 0.003, 0.003}]}. {[{{lists,reverse,1}, 1, 0.003, 0.003}], { {lists,reverse,2}, 1, 0.003, 0.003}, % [ ]}. {[ ], { undefined, 0, 0.000, 0.000}, % [{{fprof,apply_start_stop,4}, 0, 0.027, 0.019}]}. {[{{fprof,apply_start_stop,4}, 1, 0.000, 0.000}], { suspend, 1, 0.000, 0.000}, % [ ]}. Done! ok
原文:http://blog.csdn.net/huang1196/article/details/38660325