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日志服務通過異常檢測功能，自動識別服務系統中的異常狀態及其根源。該功能可自動識別指標的變化是否偏離正常模式，并結合指標當前模式與機器學習技術來進行異常檢測。本文主要介紹異常檢測功能（多變量模式識別函數）。

多變量模式識別函數列表

函數名稱	語法	說明	返回值類型
summarize函數	summarize(array(array(double)) data_samples) summarize(array(array(double)) data_samples, array(double) weights)	通過對給定樣本及樣本權重（可選）進行統計學習，識別并返回多變量的統計模式，輸出結果為統計模式。統計模式涵蓋多種統計量與聯合統計量，例如平均值、標準差、協方差矩陣等。	varchar
merge_summary函數	merge_summary(varchar summary1, varchar summary2) merge_summary(varchar summary1, double weight1, varchar summary2, double weight2)	將不同階段分別用summarize函數學習得到的模式進行合并，包括同一數據集在不同時段學習出的模式，或者來自兩個獨立數據集各自學習出的模式。	varchar
normalize_vector函數	normalize_vector(varchar summary, array(double) x_vector)	使用summarize函數獲得的多變量模式summary，對新樣本向量`x_vector`進行歸一化處理，確保其每個分量都被映射至[0, 1]的區間。	array(double)
standardize_vector函數	standardize_vector(varchar summary, array(double) x_vector)	使用summarize函數獲得的多變量模式summary，對新樣本向量`x_vector`進行標準化處理，將向量分量標準化為均值0和標準差1的某個值。	array(double)
mah_distance函數	mah_distance(varchar summary, array(double) x_vector)	使用summarize函數獲得的多變量模式summary，對新樣本`x_vector計`算其馬氏距離。馬氏距離能夠有效處理不同變量間的尺度差異問題，通過標準化給定樣本向量`x_vector`到向量重心的距離來進行衡量。當該距離值為1時，表示樣本向量與重心之間的距離等于所有向量到重心的平均距離。	double
standard_distance函數	standard_distance(varchar summary, double metric_value, int element_index)	使用summarize函數獲得的多變量模式`summary`，計算新樣本`metric_value`的標準化距離。標準化距離和馬氏距離類似，馬氏距離是計算由多個指標構成的一個向量到重心的標準化距離，而標準化距離是單個指標到重心的距離，其中`element_index`是該指標在向量中的索引，`metric_value`是要標準化的指標值。	double
anomaly_level函數	anomaly_level(varchar summary, array(double) x_vector) anomaly_level(varchar summary, array(double) x_vector, int element_index)	使用summarize函數獲得的多變量模式summary，對新樣本`x_vector`計算其馬氏距離，接著，通過對結果進行向下取整處理，得到1、2、3、4等，來分別表示不同級別的異常概率，具體為：0.1（一級異常）、0.01（二級異常）、0.001（三級異常）、0.0001（四級異常）等。異常等級增加表明概率減小，樣本點的可疑性增加。用戶通常會對異常檢測結果設置閾值過濾，例如僅保留四級異常及以上的異常。若指定了`element_index`，則僅計算向量指定索引分量的異常概率；若未指定，則計算所有分量的異常概率。	array(double)

summarize函數

通過對給定樣本及樣本權重（可選）進行統計學習，識別并返回多變量的統計模式，輸出結果為統計模式。統計模式涵蓋多種統計量與聯合統計量，例如平均值、標準差、協方差矩陣等。

varchar summarize(array(array(double)) data_samples)

或

varchar summarize(array(array(double)) data_samples, array(double) weights)

參數	說明
`data_samples`	一個二維數組，可以看成一個二維表格。每列表示一個變量，每行表示一次觀測樣本。
`weights`	可選參數。表示樣本的權重，表示為一個與`data_samples`第一維度長度相同的一維數組。若未指定，則默認所有樣本權重相等。

使用示例

查詢和分析語句

* | with data_table as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features union all
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
)
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table
    group by entity_group

返回結果

entity_group

statistical_summary

{
  "sampleCount": 8,
  "vectorSize": 4,
  "means": [
    11.5,
    12.5,
    9.25,
    0.0
  ],
  "stdDevs": [
    6.87386354243376,
    6.87386354243376,
    7.361215932167728,
    0.0
  ],
  "variances": [
    47.25,
    47.25,
    54.1875,
    0.0
  ],
  "mins": [
    1.0,
    2.0,
    1.0,
    0.0
  ],
  "maxs": [
    22.0,
    23.0,
    21.0,
    0.0
  ],
  "covariance": [
    [
      47.25,
      47.25,
      19.125,
      0.0
    ],
    [
      47.25,
      47.25,
      19.125,
      0.0
    ],
    [
      19.125,
      19.125,
      54.1875,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      0.0
    ]
  ],
  "correlations": [
    [
      1.0,
      1.0,
      0.37796447300922725,
      0.0
    ],
    [
      1.0,
      1.0,
      0.37796447300922725,
      0.0
    ],
    [
      0.37796447300922725,
      0.37796447300922725,
      1.0,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      1.0
    ]
  ],
  "sums": [
    92.0,
    100.0,
    74.0,
    0.0
  ],
  "weightSum": 8.0,
  "sumProducts": [
    [
      1436.0,
      1528.0,
      1004.0,
      0.0
    ],
    [
      1528.0,
      1628.0,
      1078.0,
      0.0
    ],
    [
      1004.0,
      1078.0,
      1118.0,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      0.0
    ]
  ],
  "isSummarized": true
}

返回參數說明：

參數	說明
`sampleCount`	樣本數量。
`vectorSize`	向量長度。
`means`	所有向量各分量的平均值。
`stdDevs`	所有向量各分量的標準差。
`variances`	所有向量各分量的方差。
`mins`	所有向量各分量的最小值。
`maxs`	所有向量各分量的最大值。
`covariance`	所有向量各分量間的協方差矩陣。
`correlations`	所有向量各分量間的相關系數矩陣。
`sums`	所有向量各分量的和。
`weightSum`	所有樣本權重的和。
`sumProducts`	合并統計模式時候用到的中間結果。
`isSummarized`	模式統計計算是否正常返回。 true：正常返回。 false：非正常返回。

merge_summary函數

將不同階段分別用summarize函數學習得到的模式進行合并，包括同一數據集在不同時段學習出的模式，或者來自兩個獨立數據集各自學習出的模式。

varchar merge_summary(varchar summary1, varchar summary2)

或

varchar merge_summary(varchar summary1, double weight1, varchar summary2, double weight2)

參數	說明
`summary1`	summarize函數學習過程得到的模式。
`weight1`	summary1模式對應的整體權重。
`summary2`	summarize函數學習過程得到的模式。
`weight2`	summary2模式對應的整體權重。

使用示例

查詢和分析語句

* | with data_table_01 as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features
),

summaries_01 as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table_01
    group by entity_group
),

data_table_02 as
(
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
),

summaries_02 as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table_02
    group by entity_group
)
select s1.entity_group,
    merge_summary(s1.statistical_summary, s2.statistical_summary) as statistical_summary
from summaries_01 as s1
    join summaries_02 as s2
    on s1.entity_group = s2.entity_group

返回結果

statistical_summary為整合后的模式。

entity_group

statistical_summary

{
  "sampleCount": 8,
  "vectorSize": 4,
  "means": [
    11.5,
    12.5,
    9.25,
    0.0
  ],
  "stdDevs": [
    6.87386354243376,
    6.87386354243376,
    7.361215932167728,
    0.0
  ],
  "variances": [
    47.25,
    47.25,
    54.1875,
    0.0
  ],
  "mins": [
    1.0,
    2.0,
    1.0,
    0.0
  ],
  "maxs": [
    22.0,
    23.0,
    21.0,
    0.0
  ],
  "covariance": [
    [
      47.25,
      47.25,
      19.125,
      0.0
    ],
    [
      47.25,
      47.25,
      19.125,
      0.0
    ],
    [
      19.125,
      19.125,
      54.1875,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      0.0
    ]
  ],
  "correlations": [
    [
      1.0,
      1.0,
      0.37796447300922725,
      0.0
    ],
    [
      1.0,
      1.0,
      0.37796447300922725,
      0.0
    ],
    [
      0.37796447300922725,
      0.37796447300922725,
      1.0,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      1.0
    ]
  ],
  "sums": [
    92.0,
    100.0,
    74.0,
    0.0
  ],
  "weightSum": 8.0,
  "sumProducts": [
    [
      1436.0,
      1528.0,
      1004.0,
      0.0
    ],
    [
      1528.0,
      1628.0,
      1078.0,
      0.0
    ],
    [
      1004.0,
      1078.0,
      1118.0,
      0.0
    ],
    [
      0.0,
      0.0,
      0.0,
      0.0
    ]
  ],
  "isSummarized": true
}

返回參數說明：

參數	說明
`sampleCount`	樣本數量。
`vectorSize`	向量長度。
`means`	所有向量各分量的平均值。
`stdDevs`	所有向量各分量的標準差。
`variances`	所有向量各分量的方差。
`mins`	所有向量各分量的最小值。
`maxs`	所有向量各分量的最大值。
`covariance`	所有向量各分量間的協方差矩陣。
`correlations`	所有向量各分量間的相關系數矩陣。
`sums`	所有向量各分量的和。
`weightSum`	所有樣本權重的和。
`sumProducts`	合并統計模式時候用到的中間結果。
`isSummarized`	模式統計計算是否正常返回。 true：正常返回。 false：非正常返回。

normalize_vector函數

使用summarize函數獲得的多變量模式summary，對新樣本向量x_vector進行歸一化處理，確保其每個分量都被映射至[0, 1]的區間。

array(double) normalize_vector(varchar summary, array(double) x_vector)

參數	說明
`summary`	summarize函數學習過程得到的模式。
`x_vector`	一組新樣本數據。

使用示例

查詢和分析語句

* | with data_table as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features union all
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
),

summaries as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table
    group by entity_group
)

select t1.entity_id,
    t1.entity_group,
    normalize_vector(t2.statistical_summary, t1.features) as normalized_features
from data_table as t1
    join summaries as t2
    on t1.entity_group = t2.entity_group

返回結果

normalized_features為輸入樣本向量x_vector歸一化處理后的結果。

entity_id	entity_group	normalized_features
2	A	[0.14285714285714286,0.14285714285714286,0.25,0.5]
4	A	[0.42857142857142857,0.42857142857142857,0.0,0.5]
3	A	[0.2857142857142857,0.2857142857142857,0.4,0.5]
...	...	...

standardize_vector函數

使用summarize函數獲得的多變量模式summary，對新樣本向量x_vector進行標準化處理，將向量分量標準化為均值0和標準差1的某個值。

array(double) standardize_vector(varchar summary, array(double) x_vector)

參數	說明
`summary`	summarize函數學習過程得到的模式。
`x_vector`	一組新樣本數據。

使用示例

查詢和分析語句

* | with data_table as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features union all
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
),

summaries as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table
    group by entity_group
)

select t1.entity_id,
    t1.entity_group,
    standardize_vector(t2.statistical_summary, t1.features) as standardized_features
from data_table as t1
    join summaries as t2
    on t1.entity_group = t2.entity_group

返回結果

standardized_features為輸入樣本向量x_vector標準化處理后的結果。

entity_id	entity_group	standardized_features
2	A	[-1.0910894511799619,-1.0910894511799619,-0.4415031470273609,0.0]
4	A	[-0.21821789023599237,-0.21821789023599237,-1.1207387578386854,0.0]
3	A	[-0.6546536707079771,-0.6546536707079771,-0.03396178054056622,0.0]
...	...	...

mah_distance函數

使用summarize函數獲得的多變量模式summary，對新樣本x_vector計算其馬氏距離。馬氏距離能夠有效處理不同變量間的尺度差異問題，通過標準化樣本向量x_vector到向量重心的距離來進行衡量。當該距離值為1時，表示樣本向量與重心之間的距離等于所有向量到重心平均距離。

double mah_distance(varchar summary, array(double) x_vector)

參數	說明
`summary`	summarize函數學習過程得到的模式。
`x_vector`	一組新樣本數據。

使用示例

查詢和分析語句

* | with data_table as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features union all
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
),

summaries as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table
    group by entity_group
)

select t1.entity_id,
    t1.entity_group,
    mah_distance(t2.statistical_summary, t1.features) as std_distance
from data_table as t1
    join summaries as t2
    on t1.entity_group = t2.entity_group

返回結果
std_distance為輸入樣本向量x_vector的馬氏距離。
entity_id
entity_group
std_distance
8
A
2.386927730244857
7
A
1.6809080087793125
1
A
1.5554594371997328
...
...
...

standard_distance函數

使用summarize函數獲得的多變量模式summary，計算新樣本metric_value的標準化距離。使用summarize函數獲得的多變量模式summary，計算新樣本metric_value的標準化距離。標準化距離和馬氏距離類似，馬氏距離是計算由多個指標構成的一個向量到重心的標準化距離，而標準化距離是單個指標到重心的距離，其中element_index是該指標在向量中的索引（分量的索引是從0開始的，而非從1開始），metric_value是要標準化的指標值。

double standard_distance(varchar summary, double metric_value, int element_index)

參數	說明
`summary`	summarize函數學習過程得到的模式。
`metric_value`	新樣本數據。
`element_index`	`summary`數組指定分量的索引。分量的索引是從0開始的，而非從1開始。

使用示例

查詢和分析語句

* | with data_table as
(
    select 1 as entity_id, 'A' as entity_group, cast(array[1, 2, 3, 0] as array(double)) as features union all
    select 2 as entity_id, 'A' as entity_group, cast(array[4, 5, 6, 0] as array(double)) as features union all
    select 3 as entity_id, 'A' as entity_group, cast(array[7, 8, 9, 0] as array(double)) as features union all
    select 4 as entity_id, 'A' as entity_group, cast(array[10, 11, 1, 0] as array(double)) as features union all
    select 5 as entity_id, 'A' as entity_group, cast(array[13, 14, 15, 0] as array(double)) as features union all
    select 6 as entity_id, 'A' as entity_group, cast(array[16, 17, 18, 0] as array(double)) as features union all
    select 7 as entity_id, 'A' as entity_group, cast(array[19, 20, 21, 0] as array(double)) as features union all
    select 8 as entity_id, 'A' as entity_group, cast(array[22, 23, 1, 0] as array(double)) as features
),

summaries as
(
    select entity_group,
        summarize(array_agg(features)) as statistical_summary
    from data_table
    group by entity_group
)

select t1.entity_id,
    t1.entity_group,
    standard_distance(t2.statistical_summary, 30, 1) as std_distance
from data_table as t1
    join summaries as t2
    on t1.entity_group = t2.entity_group

返回結果
std_distance為輸入樣本metric_value指定索引的標準化距離。
entity_id
entity_group
std_distance
8
A
2.386927730244857
7
A
1.6809080087793125
1
A
1.5554594371997328
...
...
...

anomaly_level函數

使用summarize函數獲得的多變量模式summary，對新樣本x_vector計算其馬氏距離，接著，通過對結果進行向下取整處理，得到1、2、3、4等結果，來分別表示不同級別的異常概率，異常概率的級別具體為：0.1（一級異常）、0.01（二級異常）、0.001（三級異常）、0.0001（四級異常）等。異常等級增加表明概率減小，樣本點的可疑性增加。用戶通常會對異常檢測結果設置閾值過濾，例如僅保留四級異常及以上的異常。

若指定了element_index，則僅計算向量指定索引分量的異常概率；若未指定，則計算所有分量的異常概率。

double anomaly_level(varchar summary, array(double) x_vector)

或

double anomaly_level(varchar summary, array(double) x_vector, int element_index)

參數	說明
`summary`	summarize函數學習過程得到的模式。
`x_vector`	一組新樣本數據。
`element_index`	可選參數。`x_vector`數組指定索引的元素。

使用示例

查詢和分析語句

* |
with dummy as
(
    select sequence(1, 1000) as seq_data,
        count(*) as record_count from log
),

sample_data as
(
    select 'G1' as group_id,
        s.seq_num,
         -- 產生1000個二維的隨機向量數據點，向量中心在(100, 5000)這個位置，兩個分量的標準差分別為20和500
        inverse_normal_cdf(100, 20, random()) as x1,
        inverse_normal_cdf(5000, 500, rand()) as x2
    from dummy,
        unnest(seq_data) as s(seq_num)
),

data_summary as
(
    select group_id,
        summarize(array_agg(array[x1, x2])) as metric_summary
    from sample_data
    group by group_id
),

new_data as
(
    select 'G1' as group_id, 1001 as object_id, 100.0 as x1, 5000.0 as x2
    union all select 'G1' as group_id, 1002 as object_id, 118.0 as x1, 5450.0 as x2
    union all select 'G1' as group_id, 1003 as object_id, 138.0 as x1, 5950.0 as x2
    union all select 'G1' as group_id, 1004 as object_id, 158.0 as x1, 6450.0 as x2
    union all select 'G1' as group_id, 1005 as object_id, 178.0 as x1, 6950.0 as x2
    union all select 'G1' as group_id, 1006 as object_id, 198.0 as x1, 7450.0 as x2
    union all select 'G1' as group_id, 1007 as object_id, 318.0 as x1, 10000.0 as x2
)

select n.group_id,
    json_extract(s.metric_summary, '$.means') as metric_vector_mean,
    json_extract(s.metric_summary, '$.covariance') as metric_covariance,
    n.object_id,
    n.x1,
    n.x2,
    anomaly_level(s.metric_summary, array[x1, x2]) as anomaly_level
from data_summary as s
    join new_data as n
        on s.group_id = n.group_id
order by n.group_id, n.object_id
limit 100000

返回結果
anomaly_level為輸入樣本向量x_vector的異常概率。
group_id
object_id
anomaly_level
G1
1007
13.0
G1
1006
5.0
G1
1005
4.0
...
...
...

entity_id	entity_group	std_distance
8	A	2.386927730244857
7	A	1.6809080087793125
1	A	1.5554594371997328
...	...	...

group_id	object_id	anomaly_level
G1	1007	13.0
G1	1006	5.0
G1	1005	4.0
...	...	...

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多變量模式識別函數列表

summarize函數

使用示例

merge_summary函數

使用示例

normalize_vector函數

使用示例

standardize_vector函數

使用示例

mah_distance函數

使用示例

standard_distance函數

使用示例

anomaly_level函數

使用示例