Account Usage¶

示例：用户登录指标¶

用户登录尝试失败之间的平均秒数（月初至今）：

select user_name,
       count(*) as failed_logins,
       avg(seconds_between_login_attempts) as average_seconds_between_login_attempts
from (
      select user_name,
             timediff(seconds, event_timestamp, lead(event_timestamp)
                 over(partition by user_name order by event_timestamp)) as seconds_between_login_attempts
      from login_history
      where event_timestamp > date_trunc(month, current_date)
      and is_success = 'NO'
     )
group by 1
order by 3;

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用户登录失败次数（月初至今）：

select user_name,
       sum(iff(is_success = 'NO', 1, 0)) as failed_logins,
       count(*) as logins,
       sum(iff(is_success = 'NO', 1, 0)) / nullif(count(*), 0) as login_failure_rate
from login_history
where event_timestamp > date_trunc(month, current_date)
group by 1
order by 4 desc;

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用户和连接客户端登录失败次数（月初至今）：

select reported_client_type,
       user_name,
       sum(iff(is_success = 'NO', 1, 0)) as failed_logins,
       count(*) as logins,
       sum(iff(is_success = 'NO', 1, 0)) / nullif(count(*), 0) as login_failure_rate
from login_history
where event_timestamp > date_trunc(month, current_date)
group by 1,2
order by 5 desc;

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示例：仓库性能¶

此查询计算虚拟仓库的性能指标，例如一天中以 15 分钟为间隔的吞吐量和延迟。

在下面的代码示例中，您可以将 CURRENT_WAREHOUSE() 替换为仓库名称，以计算该仓库的指标。此外，更改 WITH 子句中的 time_from 和 time_to 日期来指定时间段。

WITH
params AS (
SELECT
    CURRENT_WAREHOUSE() AS warehouse_name,
    '2021-11-01' AS time_from,
    '2021-11-02' AS time_to
),

jobs AS (
SELECT
    query_id,
    time_slice(start_time::timestamp_ntz, 15, 'minute','start') as interval_start,
    qh.warehouse_name,
    database_name,
    query_type,
    total_elapsed_time,
    compilation_time AS compilation_and_scheduling_time,
    (queued_provisioning_time + queued_repair_time + queued_overload_time) AS queued_time,
    transaction_blocked_time,
    execution_time
FROM snowflake.account_usage.query_history qh, params
WHERE
    qh.warehouse_name = params.warehouse_name
AND start_time >= params.time_from
AND start_time <= params.time_to
AND execution_status = 'SUCCESS'
AND query_type IN ('SELECT','UPDATE','INSERT','MERGE','DELETE')
),

interval_stats AS (
SELECT
    query_type,
    interval_start,
    COUNT(DISTINCT query_id) AS numjobs,
    MEDIAN(total_elapsed_time)/1000 AS p50_total_duration,
    (percentile_cont(0.95) within group (order by total_elapsed_time))/1000 AS p95_total_duration,
    SUM(total_elapsed_time)/1000 AS sum_total_duration,
    SUM(compilation_and_scheduling_time)/1000 AS sum_compilation_and_scheduling_time,
    SUM(queued_time)/1000 AS sum_queued_time,
    SUM(transaction_blocked_time)/1000 AS sum_transaction_blocked_time,
    SUM(execution_time)/1000 AS sum_execution_time,
    ROUND(sum_compilation_and_scheduling_time/sum_total_duration,2) AS compilation_and_scheduling_ratio,
    ROUND(sum_queued_time/sum_total_duration,2) AS queued_ratio,
    ROUND(sum_transaction_blocked_time/sum_total_duration,2) AS blocked_ratio,
    ROUND(sum_execution_time/sum_total_duration,2) AS execution_ratio,
    ROUND(sum_total_duration/numjobs,2) AS total_duration_perjob,
    ROUND(sum_compilation_and_scheduling_time/numjobs,2) AS compilation_and_scheduling_perjob,
    ROUND(sum_queued_time/numjobs,2) AS queued_perjob,
    ROUND(sum_transaction_blocked_time/numjobs,2) AS blocked_perjob,
    ROUND(sum_execution_time/numjobs,2) AS execution_perjob
FROM jobs
GROUP BY 1,2
ORDER BY 1,2
)
SELECT * FROM interval_stats;

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备注

分别分析不同的语句类型（例如，独立于 INSERT、DELETE 或其他语句分析 SELECT 语句）。

• NUMJOBS 值表示该时间间隔的吞吐量。

• P50_TOTAL_DURATION （中位数）和 P95_TOTAL_DURATION （峰值）这两个值代表延迟。

• SUM_TOTAL_DURATION 是不同作业阶段（COMPILATION_AND_SCHEDULING、QUEUED、BLOCKED、EXECUTION）的 SUM_<job_stage>_TIME 之和。

• 分析在负载 (NUMJOBS) 增加时的 <job_stage>_RATIO。观察比率变化或与平均值的偏差。

• 如果 QUEUED_RATIO 较高，则可能代表仓库容量不足。添加更多群集或增加仓库大小。

示例：仓库 Credit 使用量¶

账户中每个仓库使用的 credit（月初至今）：

select warehouse_name,
       sum(credits_used) as total_credits_used
from warehouse_metering_history
where start_time >= date_trunc(month, current_date)
group by 1
order by 2 desc;

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账户中每个仓库在一段时间内使用的 credit（月初至今）：

select start_time::date as usage_date,
       warehouse_name,
       sum(credits_used) as total_credits_used
from warehouse_metering_history
where start_time >= date_trunc(month, current_date)
group by 1,2
order by 2,1;

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示例：数据存储的使用情况¶

在一段时间内，账户中存储的可计费数据 TB 数：

select date_trunc(month, usage_date) as usage_month
  , avg(storage_bytes + stage_bytes + failsafe_bytes) / power(1024, 4) as billable_tb
from storage_usage
group by 1
order by 1;

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示例：用户查询总数和执行时间¶

账户中执行的作业总数（月初至今）：

select count(*) as number_of_jobs
from query_history
where start_time >= date_trunc(month, current_date);

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账户中每个仓库执行的作业总数（月初至今）：

select warehouse_name,
       count(*) as number_of_jobs
from query_history
where start_time >= date_trunc(month, current_date)
group by 1
order by 2 desc;

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用户的平均查询执行时间（月初至今）：

select user_name,
       avg(execution_time) as average_execution_time
from query_history
where start_time >= date_trunc(month, current_date)
group by 1
order by 2 desc;

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按查询类型和仓库大小划分的平均查询执行时间（月初至今）：

select query_type,
       warehouse_size,
       avg(execution_time) as average_execution_time
from query_history
where start_time >= date_trunc(month, current_date)
group by 1,2
order by 3 desc;

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示例：获取每个登录事件的查询计数¶

联接来自 LOGIN_HISTORY、QUERY_HISTORY 和 SESSIONS 的列，以获取每个用户登录事件的查询计数。

备注

SESSIONS 视图记录了从 2020 年 7 月 20-21 日起的信息，因此对于这三个视图中的每一个，查询结果均仅包含从该日期起的重叠信息。

select l.user_name,
       l.event_timestamp as login_time,
       l.client_ip,
       l.reported_client_type,
       l.first_authentication_factor,
       l.second_authentication_factor,
       count(q.query_id)
from snowflake.account_usage.login_history l
join snowflake.account_usage.sessions s on l.event_id = s.login_event_id
join snowflake.account_usage.query_history q on q.session_id = s.session_id
group by 1,2,3,4,5,6
order by l.user_name
;

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