0.文章系列連結
- SLS機器學習介紹（01）：時序統計模組化
- SLS機器學習介紹（02）：時序聚類模組化
- SLS機器學習介紹（03）：時序異常檢測模組化
- SLS機器學習介紹（04）：規則模式挖掘
- SLS機器學習介紹（05）：時間序列預測
1.手中的錘子都有啥？
圍繞日志，挖掘其中更大價值，一直是我們團隊所關注。在原有日志實時查詢基礎上，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
- 提供多種時序資料的異常檢測和預測函數，來做更智能的檢查和預測
- 資料分析的結果可視化
- 強大的告警設定和通知，通過調用webhook進行關聯行動
SLS機器學習最佳實戰：日志聚類+異常告警
今天我們重點介紹下，日志隻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.平台實驗
2.1 實驗資料
一份Sys Log的原始資料，，并且開啟了日志聚類服務，具體的狀态截圖如下：
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，但是對于每個最細粒度的pattern并不會改變，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找到對應的原始日志條目。
2.2 生成子模式的時序資訊
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對應的原始日志，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，這個模式的日志分布不是很均衡，其中還有一些是沒有的，如果直接按照時間視窗統計數量，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并不是連續的。是以，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 告警該如何設定
- 将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，并設定兜底政策
__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 ) 具體的告警設定如下：
3.硬廣時間
3.1 日志進階
這裡是日志服務的各種功能的示範
日志服務整體介紹，各種Demo 更多日志進階内容可以參考：
日志服務學習路徑。
3.2 聯系我們
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