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76 lignes
2.2 KiB
76 lignes
2.2 KiB
-module(performance_monitor).
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-behaviour(gen_server).
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-export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
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-export([start_monitoring/1, stop_monitoring/1, get_metrics/1, generate_report/1]).
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-record(state, {
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monitors = #{}, % 监控对象集合
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metrics = #{}, % 性能指标数据
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alerts = [], % 告警信息
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start_time = undefined
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}).
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-record(monitor_data, {
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type, % 监控类型
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metrics = [], % 指标列表
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threshold = #{}, % 阈值设置
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callback % 回调函数
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}).
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%% API
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start_link() ->
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gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).
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start_monitoring(Target) ->
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gen_server:call(?MODULE, {start_monitoring, Target}).
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stop_monitoring(Target) ->
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gen_server:call(?MODULE, {stop_monitoring, Target}).
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get_metrics(Target) ->
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gen_server:call(?MODULE, {get_metrics, Target}).
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generate_report(Target) ->
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gen_server:call(?MODULE, {generate_report, Target}).
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%% 内部函数
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collect_metrics(Target) ->
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% 收集各种性能指标
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#{
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cpu_usage => get_cpu_usage(Target),
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memory_usage => get_memory_usage(Target),
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response_time => get_response_time(Target),
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throughput => get_throughput(Target)
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}.
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analyze_performance(Metrics) ->
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% 分析性能数据
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#{
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avg_response_time => calculate_average(maps:get(response_time, Metrics)),
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peak_memory => get_peak_value(maps:get(memory_usage, Metrics)),
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bottlenecks => identify_bottlenecks(Metrics)
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}.
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generate_alerts(Metrics, Thresholds) ->
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% 生成性能告警
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lists:filtermap(
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fun({Metric, Value}) ->
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case check_threshold(Metric, Value, Thresholds) of
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{true, Alert} -> {true, Alert};
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false -> false
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end
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end,
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maps:to_list(Metrics)
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).
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create_report(Target, Metrics) ->
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% 生成性能报告
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#{
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target => Target,
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timestamp => os:timestamp(),
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metrics => Metrics,
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analysis => analyze_performance(Metrics),
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recommendations => generate_recommendations(Metrics)
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}.
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