SLS機器學習最佳實戰：日誌聚類+異常告警

1.手中的錘子都有啥？

圍繞日誌，挖掘其中更大價值，一直是我們團隊所關注。在原有日誌實時查詢基礎上，今年SLS在DevOps領域完善了如下功能：

• 上下文查詢
• 實時Tail和智能聚類，以提高問題調查效率
• 提供多種時序數據的異常檢測和預測函數，來做更智能的檢查和預測
• 數據分析的結果可視化
• 強大的告警設置和通知，通過調用webhook進行關聯行動

今天我們重點介紹下，日誌只能聚類和異常告警如何配合，更好的進行異常發現和告警

2.平臺實驗

2.1 實驗數據

一份Sys Log的原始數據，，並且開啓了日誌聚類服務，具體的狀態截圖如下：

通過調整下面截圖中紅色框1的大小，可以改變圖中紅色框2的結果，但是對於每個最細粒度的pattern並不會改變，也就是說：子Pattern的結果是穩定且唯一的，我們可以通過子Pattern的Signature找到對應的原始日誌條目。

2.2 生成子模式的時序信息

假設，我們對這個子Pattern要進行監控：

msg:vm-111932.tc su: pam_unix(*:session): session closed for user root
對應的 signature_id : __log_signature__: 1814836459146662485

我們得到了上述pattern對應的原始日誌，可以看下具體的數量在時間軸上的直返圖：

上圖中，我們可以發現，這個模式的日誌分佈不是很均衡，其中還有一些是沒有的，如果直接按照時間窗口統計數量，得到的時序圖如下：

__log_signature__: 1814836459146662485 |  
select 
    date_trunc('minute', __time__) as time, 
    COUNT(*) as num 
from log GROUP BY time order by time ASC limit 10000

上述圖中我們發現時間上並不是連續的。因此，我們需要對這條時序進行補點操作。

__log_signature__: 1814836459146662485 | 
select 
    time_series(time, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, 
    avg(num) as num 
from  ( 
    select 
        __time__ - __time__ % 60 as time, 
        COUNT(*) as num 
    from log GROUP BY time order by time desc ) 
GROUP by time order by time ASC limit 10000

2.3 對時序進行異常檢測

使用時序異常檢測函數： ts_predicate_arma

__log_signature__: 1814836459146662485 | 
select 
    ts_predicate_arma(to_unixtime(time), num, 5, 1, 1, 1, 'avg') 
from  ( 
    select 
        time_series(time, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, 
        avg(num) as num 
    from  ( 
        select 
            __time__ - __time__ % 60 as time, 
            COUNT(*) as num 
        from log GROUP BY time order by time desc ) 
    GROUP by time order by time ASC ) limit 10000

2.4 告警該如何設置

• 將機器學習函數的結果拆解開

__log_signature__: 1814836459146662485 | 
select 
    t1[1] as unixtime, t1[2] as src, t1[3] as pred, t1[4] as up, t1[5] as lower, t1[6] as prob 
from  ( 
    select 
        ts_predicate_arma(to_unixtime(time), num, 5, 1, 1, 1, 'avg') as res 
    from  ( 
        select 
            time_series(time, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, 
            avg(num) as num 
        from  ( 
            select 
                __time__ - __time__ % 60 as time, 
                COUNT(*) as num 
            from log GROUP BY time order by time desc ) 
        GROUP by time order by time ASC )) , unnest(res) as t(t1)

• 針對最近兩分鐘的結果進行告警

__log_signature__: 1814836459146662485 | 
select 
    unixtime, src, pred, up, lower, prob 
from  ( 
    select 
        t1[1] as unixtime, t1[2] as src, t1[3] as pred, t1[4] as up, t1[5] as lower, t1[6] as prob 
    from  ( 
        select 
            ts_predicate_arma(to_unixtime(time), num, 5, 1, 1, 1, 'avg') as res 
        from  ( 
            select 
                time_series(time, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, 
                avg(num) as num 
            from  ( 
                select 
                    __time__ - __time__ % 60 as time, COUNT(*) as num 
                from log GROUP BY time order by time desc ) 
            GROUP by time order by time ASC )) , unnest(res) as t(t1) ) 
    where is_nan(src) = false order by unixtime desc limit 2

• 針對上升點進行告警，並設置兜底策略

__log_signature__: 1814836459146662485 | 
select 
    sum(prob) as sumProb, max(src) as srcMax, max(up) as upMax 
from ( 
    select 
        unixtime, src, pred, up, lower, prob 
    from  ( 
        select 
            t1[1] as unixtime, t1[2] as src, t1[3] as pred, t1[4] as up, t1[5] as lower, t1[6] as prob 
        from  ( 
            select 
                ts_predicate_arma(to_unixtime(time), num, 5, 1, 1, 1, 'avg') as res 
            from  ( 
                select 
                    time_series(time, '1m', '%Y-%m-%d %H:%i:%s', '0') as time, avg(num) as num 
                from  ( 
                    select 
                        __time__ - __time__ % 60 as time, COUNT(*) as num 
                    from log GROUP BY time order by time desc ) 
                GROUP by time order by time ASC )) , unnest(res) as t(t1) ) 
        where is_nan(src) = false order by unixtime desc limit 2 )

具體的告警設置如下：

3.硬廣時間

3.1 日誌進階

這裏是日誌服務的各種功能的演示 日誌服務整體介紹，各種Demo

更多日誌進階內容可以參考：日誌服務學習路徑。

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本文作者：悟冥

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