了解计算成本¶

仓库的计算¶

查询：过去 m 天 Snowflake 的每小时平均支出（跨所有仓库）

此查询按小时显示 Credit 总使用量，以帮助了解过去 m 天的使用量趋势（峰值、谷值）。这有助于确定一天中的使用量高峰时间。

SELECT start_time,
  warehouse_name,
  credits_used_compute
FROM snowflake.account_usage.warehouse_metering_history
WHERE start_time >= DATEADD(day, -m, CURRENT_TIMESTAMP())
  AND warehouse_id > 0  -- Skip pseudo-VWs such as "CLOUD_SERVICES_ONLY"
ORDER BY 1 DESC, 2;

-- by hour
SELECT DATE_PART('HOUR', start_time) AS start_hour,
  warehouse_name,
  AVG(credits_used_compute) AS credits_used_compute_avg
FROM snowflake.account_usage.warehouse_metering_history
WHERE start_time >= DATEADD(day, -m, CURRENT_TIMESTAMP())
  AND warehouse_id > 0  -- Skip pseudo-VWs such as "CLOUD_SERVICES_ONLY"
GROUP BY 1, 2
ORDER BY 1, 2;

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查询：特定时间段的 Credit 使用量（按仓库）

此查询显示特定时间段内每个仓库的 Credit 总使用量。这有助于确定使用的 Credit 比其他仓库多的仓库，以及使用的 Credit 比预期多的特定仓库。

-- Credits used (all time = past year)
SELECT warehouse_name,
  SUM(credits_used_compute) AS credits_used_compute_sum
FROM snowflake.account_usage.warehouse_metering_history
GROUP BY 1
ORDER BY 2 DESC;

-- Credits used (past N days/weeks/months)
SELECT warehouse_name,
  SUM(credits_used_compute) AS credits_used_compute_sum
FROM snowflake.account_usage.warehouse_metering_history
WHERE start_time >= DATEADD(day, -m, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 2 DESC;

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查询：m 天内仓库的平均使用量

此查询返回按周和仓库分组的 Credit 每日平均使用量。它可用于识别过去一年各周的仓库 Credit 使用量的异常情况。

WITH cte_date_wh AS (
  SELECT TO_DATE(start_time) AS start_date,
    warehouse_name,
    SUM(credits_used) AS credits_used_date_wh
  FROM snowflake.account_usage.warehouse_metering_history
  GROUP BY start_date, warehouse_name
)

SELECT start_date,
  warehouse_name,
  credits_used_date_wh,
  AVG(credits_used_date_wh) OVER (PARTITION BY warehouse_name ORDER BY start_date ROWS m PRECEDING) AS credits_used_m_day_avg,
  100.0*((credits_used_date_wh / credits_used_m_day_avg) - 1) AS pct_over_to_m_day_average
FROM cte_date_wh
  QUALIFY credits_used_date_wh > 100  -- Minimum N=100 credits
    AND pct_over_to_m_day_average >= 0.5  -- Minimum 50% increase over past m day average
ORDER BY pct_over_to_m_day_average DESC;

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云服务的计算¶

查询：计费云服务

仅当 云服务的使用量 超过虚拟仓库每日使用量的 10% 时，才会收取使用费用。此查询返回特定日期实际计费的云服务使用量，按最高计费金额排序。

SELECT
    usage_date,
    credits_used_cloud_services,
    credits_adjustment_cloud_services,
    credits_used_cloud_services + credits_adjustment_cloud_services AS billed_cloud_services
FROM snowflake.account_usage.metering_daily_history
WHERE usage_date >= DATEADD(month,-1,CURRENT_TIMESTAMP())
    AND credits_used_cloud_services > 0
ORDER BY 4 DESC;

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查询：云服务总成本（按查询类型）

此查询按特定查询类型返回用于云服务的总 Credit。

SELECT query_type,
  SUM(credits_used_cloud_services) AS cs_credits,
  COUNT(1) num_queries
FROM snowflake.account_usage.query_history
WHERE true
  AND start_time >= TIMESTAMPADD(day, -1, CURRENT_TIMESTAMP)
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;

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查询：给定类型查询的云服务成本

此查询按特定类型的所有查询返回用于云服务的总 Credit。如果您希望关注另一种类型的查询，请替换 'COPY'；如果您希望了解更长或更短的时间段，请替换 day。

SELECT *
FROM snowflake.account_usage.query_history
WHERE true
  AND start_time >= TIMESTAMPADD(day, -1, CURRENT_TIMESTAMP)
  AND query_type = 'COPY'
ORDER BY credits_used_cloud_services DESC
LIMIT 10;

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查询：云服务使用率高的仓库

此查询显示没有使用足够长的仓库时间来覆盖计算的云服务部分的仓库。此查询通过隔离云服务使用率高（超过 10% 的总 Credit）的仓库，提供了进一步调查的起点。调查候选对象包括与克隆、S3 中的列表文件、合作伙伴工具、设置会话参数等相关的问题。

SELECT
  warehouse_name,
  SUM(credits_used) AS credits_used,
  SUM(credits_used_cloud_services) AS credits_used_cloud_services,
  SUM(credits_used_cloud_services)/SUM(credits_used) AS percent_cloud_services
FROM snowflake.account_usage.warehouse_metering_history
WHERE TO_DATE(start_time) >= DATEADD(month,-1,CURRENT_TIMESTAMP())
    AND credits_used_cloud_services > 0
GROUP BY 1
ORDER BY 4 DESC;

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查询：按查询时间部分排序的云服务使用量

此查询返回最后一分钟运行的所有查询，并按总查询执行时间的各个部分（例如，编译时间与排队时间）对这些查询进行排序。

SELECT *
FROM snowflake.account_usage.query_history
WHERE true
  AND start_time >= TIMESTAMPADD(minute, -60, CURRENT_TIMESTAMP)
ORDER BY compilation_time DESC,
  execution_time DESC,
  list_external_files_time DESC,
  queued_overload_time DESC,
  credits_used_cloud_services DESC
LIMIT 10;

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自动聚类的计算¶

查询：自动聚类成本历史记录（按天、按对象）

此查询提供含有自动聚类的表的列表，以及过去 30 天通过服务使用的 Credit（按天细分）。Credit 使用中的任何违规行为或持续高使用量都是需要进行进一步调查的标志。

SELECT TO_DATE(start_time) AS date,
  database_name,
  schema_name,
  table_name,
  SUM(credits_used) AS credits_used
FROM snowflake.account_usage.automatic_clustering_history
WHERE start_time >= DATEADD(month,-1,CURRENT_TIMESTAMP())
GROUP BY 1,2,3,4
ORDER BY 5 DESC;

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查询：自动聚类历史记录和 m 天平均值

此查询显示去年自动聚类使用的 Credit 每日平均值（按周分组）。它可以帮助识别一年中每日平均值的异常情况，以便您可以调查使用量的峰值或意外变化。

WITH credits_by_day AS (
  SELECT TO_DATE(start_time) AS date,
    SUM(credits_used) AS credits_used
  FROM snowflake.account_usage.automatic_clustering_history
  WHERE start_time >= DATEADD(year,-1,CURRENT_TIMESTAMP())
  GROUP BY 1
  ORDER BY 2 DESC
)

SELECT DATE_TRUNC('week',date),
      AVG(credits_used) AS avg_daily_credits
FROM credits_by_day
GROUP BY 1
ORDER BY 1;

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搜索优化的计算¶

查询：搜索优化成本历史记录（按天、按对象）

此查询提供含有搜索优化的表的完整列表，以及过去 30 天通过服务使用的 Credit（按天细分）。Credit 使用中的任何违规行为或持续高使用量都是需要进行进一步调查的标志。

SELECT TO_DATE(start_time) AS date,
  database_name,
  schema_name,
  table_name,
  SUM(credits_used) AS credits_used
FROM snowflake.account_usage.search_optimization_history
WHERE start_time >= DATEADD(month,-1,CURRENT_TIMESTAMP())
GROUP BY 1,2,3,4
ORDER BY 5 DESC;

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查询：搜索优化历史记录和 m 天平均值

此查询显示去年搜索优化使用的 Credit 每日平均值（按周分组）。它可以帮助识别一年中每日平均值的异常情况，以便您可以调查使用量的峰值或意外变化。

WITH credits_by_day AS (
  SELECT TO_DATE(start_time) AS date,
    SUM(credits_used) AS credits_used
  FROM snowflake.account_usage.search_optimization_history
  WHERE start_time >= DATEADD(year,-1,CURRENT_TIMESTAMP())
  GROUP BY 1
  ORDER BY 2 DESC
)

SELECT DATE_TRUNC('week', date),
  AVG(credits_used) as avg_daily_credits
FROM credits_by_day
GROUP BY 1
ORDER BY 1;

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物化视图的计算¶

查询：物化视图成本历史记录（按天、按对象）

此查询提供物化视图的完整列表，以及过去 30 天通过服务使用的 Credit（按天细分）。Credit 使用中的任何违规行为或持续高使用量都是需要进行进一步调查的标志。

SELECT TO_DATE(start_time) AS date,
  database_name,
  schema_name,
  table_name,
  SUM(credits_used) AS credits_used
FROM snowflake.account_usage.materialized_view_refresh_history
WHERE start_time >= DATEADD(month,-1,CURRENT_TIMESTAMP())
GROUP BY 1,2,3,4
ORDER BY 5 DESC;

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查询：物化视图历史记录和 m 天平均值

此查询显示去年物化视图使用的 Credit 每日平均值（按周分组）。它可以帮助识别一年中每日平均值的异常情况，以便您可以调查使用量的峰值或意外变化。

WITH credits_by_day AS (
  SELECT TO_DATE(start_time) AS date,
    SUM(credits_used) AS credits_used
  FROM snowflake.account_usage.materialized_view_refresh_history
  WHERE start_time >= DATEADD(year,-1,CURRENT_TIMESTAMP())
  GROUP BY 1
  ORDER BY 2 DESC
)

SELECT DATE_TRUNC('week',date),
  AVG(credits_used) AS avg_daily_credits
FROM credits_by_day
GROUP BY 1
ORDER BY 1;

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Query Acceleration Service 的计算¶

查询：Query Acceleration Service 成本（按仓库）

此查询返回您的账户中每个仓库月初至今用于 Query Acceleration Service 的 Credit 总数：

SELECT warehouse_name,
       SUM(credits_used) AS total_credits_used
  FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_ACCELERATION_HISTORY
  WHERE start_time >= DATE_TRUNC(month, CURRENT_DATE)
  GROUP BY 1
  ORDER BY 2 DESC;

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Snowpipe 和 Snowpipe Streaming 的计算¶

查询：数据引入的累计使用量（Snowpipe 和“复制”）

此查询返回 Snowflake 中每个表的所有负载的每日摘要（经汇总），并显示平均文件大小、总行数、总用量和引入方法（复制或 Snowpipe）。如果文件因太小或太大而无法实现更好的引入，则可能需要进一步的调查/优化。通过将用量映射到 Credit 使用量，可以确定哪些表在加载 1 TB 时使用的 Credit 更多。

SELECT TO_DATE(last_load_time) AS load_date,
  status,
  table_catalog_name AS database_name,
  table_schema_name AS schema_name,
  table_name,
  CASE
    WHEN pipe_name IS NULL THEN 'COPY'
    ELSE 'SNOWPIPE'
  END AS ingest_method,
  SUM(row_count) AS row_count,
  SUM(row_parsed) AS rows_parsed,
  AVG(file_size) AS avg_file_size_bytes,
  SUM(file_size) AS total_file_size_bytes,
  SUM(file_size)/POWER(1024,1) AS total_file_size_kb,
  SUM(file_size)/POWER(1024,2) AS total_file_size_mb,
  SUM(file_size)/POWER(1024,3) AS total_file_size_gb,
  SUM(file_size)/POWER(1024,4) AS total_file_size_tb
FROM snowflake.account_usage.copy_history
GROUP BY 1,2,3,4,5,6
ORDER BY 3,4,5,1,2;

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查询：Snowpipe 成本历史记录（按天、按对象）

此查询提供管道的完整列表，以及过去 30 天通过服务使用的 Credit（按天细分）。Credit 使用中的任何违规行为或持续高使用量都是需要进行进一步调查的标志。

SELECT TO_DATE(start_time) AS date,
  pipe_name,
  SUM(credits_used) AS credits_used
FROM snowflake.account_usage.pipe_usage_history
WHERE start_time >= DATEADD(month,-1,CURRENT_TIMESTAMP())
GROUP BY 1,2
ORDER BY 3 DESC;

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查询：Snowpipe 历史记录和 m 天平均值

此查询显示了 Snowpipe 去年按周分组的平均每日使用 Credit。它可以帮助识别一年中每日平均值的异常情况，以便您可以调查使用量的峰值或意外变化。

WITH credits_by_day AS (
  SELECT TO_DATE(start_time) AS date,
    SUM(credits_used) AS credits_used
  FROM snowflake.account_usage.pipe_usage_history
  WHERE start_time >= DATEADD(year,-1,CURRENT_TIMESTAMP())
  GROUP BY 1
  ORDER BY 2 DESC
)

SELECT DATE_TRUNC('week',date),
  AVG(credits_used) AS avg_daily_credits
FROM credits_by_day
GROUP BY 1
ORDER BY 1;

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查询：Snowpipe Streaming 总成本

此查询列出 Snowpipe Streaming 的当前 Credit 使用量（包括了 Snowpipe Streaming 计算和客户端成本）。

SELECT start_time,
  end_time,
  SUM(credits_used) AS total_credits,
  name,
  IFF(CONTAINS(name,':'),'streaming client cost', 'streaming compute cost') AS streaming_cost_type
FROM SNOWFLAKE.ACCOUNT_USAGE.METERING_HISTORY
WHERE service_type ='SNOWPIPE_STREAMING'
GROUP BY ALL;

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无服务器警报的计算¶

查询：无服务器警报总成本

此查询列出所有无服务器警报的当前 Credit 使用量：

SELECT
    start_time,
    end_time,
    alert_id,
    alert_name,
    credits_used,
    schema_id,
    schema_name,
    database_id,
    database_name
  FROM SNOWFLAKE.ACCOUNT_USAGE.serverless_alert_history
  ORDER BY start_time, alert_id;

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Serverless Task 的计算¶

查询：Serverless Task 总成本

此查询列出所有 Serverless Task 的当前 Credit 使用量：

SELECT start_time,
  end_time,
  task_id,
  task_name,
  credits_used,
  schema_id,
  schema_name,
  database_id,
  database_name
FROM snowflake.account_usage.serverless_task_history
ORDER BY start_time, task_id;

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复制的计算¶

查询：账户复制成本

此查询列出复制组或故障转移组当前月份用于账户复制的 Credit：

SELECT start_time, 
  end_time, 
  replication_group_name, 
  credits_used, 
  bytes_transferred
FROM snowflake.account_usage.replication_group_usage_history
WHERE start_time >= DATE_TRUNC('month', CURRENT_DATE());

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查询：数据库复制成本历史记录（按天、按对象）

此查询提供复制数据库的完整列表，以及过去 30 天通过复制服务使用的 Credit（按天细分）。Credit 使用中的任何违规行为或持续高使用量都是需要进行进一步调查的标志。

SELECT TO_DATE(start_time) AS date,
  database_name,
  SUM(credits_used) AS credits_used
FROM snowflake.account_usage.database_replication_usage_history
WHERE start_time >= DATEADD(month,-1,CURRENT_TIMESTAMP())
GROUP BY 1,2
ORDER BY 3 DESC;

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查询：数据库复制历史记录和 m 天平均值

此查询显示去年复制服务使用的 Credit 每日平均值（按周分组）。它可以帮助识别每日平均值的任何异常情况，以便您可以调查使用量的任何峰值或变化。

WITH credits_by_day AS (
  SELECT TO_DATE(start_time) AS date,
    SUM(credits_used) AS credits_used
  FROM snowflake.account_usage.database_replication_usage_history
  WHERE start_time >= DATEADD(year,-1,CURRENT_TIMESTAMP())
  GROUP BY 1
  ORDER BY 2 DESC
)

SELECT DATE_TRUNC('week',date),
  AVG(credits_used) AS avg_daily_credits
FROM credits_by_day
GROUP BY 1
ORDER BY 1;

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合作伙伴工具的计算¶

查询：Credit 使用量（按合作伙伴工具）

此查询确定 Snowflake 的哪些合作伙伴工具/解决方案（例如 BI、ETL 等）使用了最多 Credit。这可以帮助确定使用的 Credit 比预期多的合作伙伴解决方案，从而借以开展进一步调查。

-- This Is Approximate Credit Consumption By Client Application
WITH
  client_hour_execution_cte AS (
    SELECT
      CASE
        WHEN client_application_id LIKE 'Go %' THEN 'Go'
        WHEN client_application_id LIKE 'Snowflake UI %' THEN 'Snowflake UI'
        WHEN client_application_id LIKE 'Snowflake CLI %' THEN 'Snowflake CLI'
        WHEN client_application_id LIKE 'SnowSQL %' THEN 'SnowSQL'
        WHEN client_application_id LIKE 'JDBC %' THEN 'JDBC'
        WHEN client_application_id LIKE 'PythonConnector %' THEN 'Python'
        WHEN client_application_id LIKE 'ODBC %' THEN 'ODBC'
        ELSE 'NOT YET MAPPED: ' || CLIENT_APPLICATION_ID
      END AS client_application_name,
      warehouse_name,
      DATE_TRUNC('hour',start_time) AS start_time_hour,
      SUM(execution_time)  AS client_hour_execution_time
    FROM snowflake.account_usage.query_history qh
      JOIN snowflake.account_usage.sessions se
        ON se.session_id = qh.session_id
    WHERE warehouse_name IS NOT NULL
      AND execution_time > 0
      AND start_time > DATEADD(month,-1,CURRENT_TIMESTAMP())
    GROUP BY 1,2,3
  ),
  hour_execution_cte AS (
    SELECT start_time_hour,
      warehouse_name,
      SUM(client_hour_execution_time) AS hour_execution_time
    FROM client_hour_execution_cte
    GROUP BY 1,2
  ),
  approximate_credits AS (
    SELECT A.client_application_name,
      C.warehouse_name,
      (A.client_hour_execution_time/B.hour_execution_time)*C.credits_used AS approximate_credits_used
    FROM client_hour_execution_cte A
      JOIN hour_execution_cte B
        ON A.start_time_hour = B.start_time_hour and B.warehouse_name = A.warehouse_name
      JOIN snowflake.account_usage.warehouse_metering_history C
        ON C.warehouse_name = A.warehouse_name AND C.start_time = A.start_time_hour
  )

SELECT client_application_name,
  warehouse_name,
  SUM(approximate_credits_used) AS approximate_credits_used
FROM approximate_credits
GROUP BY 1,2
ORDER BY 3 DESC;

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混合表的计算¶

查询：特定时间段内混合表的 Credit 消耗

此查询显示了特定时间段内每个混合表的总计 Credit 消耗。这有助于识别比其他表消耗更多 Credit 的混合表，以及相比预期消耗更多 Credit 的特定混合表。

-- Credits used (all time = past year)

SELECT object_type,
  SUM(credits_used) AS total_credits
FROM SNOWFLAKE.ACCOUNT_USAGE.HYBRID_TABLE_USAGE_HISTORY
GROUP BY 1;

-- Credits used (past N days/weeks/months)

SELECT object_type,
  SUM(credits_used) AS total_credits
FROM SNOWFLAKE.ACCOUNT_USAGE.HYBRID_TABLE_USAGE_HISTORY
WHERE start_time >= DATEADD(day, -5, CURRENT_TIMESTAMP()) 
GROUP BY 1;

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Cortex Analyst 的计算¶

查询：Cortex Analyst 的 Credit 使用量。

此查询显示的 Cortex Analyst Credit 使用量。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_ANALYST_USAGE_HISTORY;

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Cortex 微调的计算¶

查询：Cortex 微调的 Credit 使用量

此查询显示每次 Cortex 微调的训练 Credit 使用量，以一小时为增量汇总。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FINE_TUNING_USAGE_HISTORY;

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Cortex 函数的计算¶

查询：Cortex 函数的 Credit 使用量

此查询显示每次 Cortex 函数调用的 Credit 使用量，基于函数和模型以一小时为增量汇总。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_USAGE_HISTORY;

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查询：通过 mistral-large 模型调用的 Cortex 函数的 Credit 使用量。

此查询显示每次使用 mistral-large 模型调用的 Cortex 函数的 Credit 使用量，基于函数和模型以一小时为增量汇总。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_USAGE_HISTORY
  WHERE model_name = 'mistral-large';

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查询：Cortex 函数查询的 Credit 使用量

此查询显示每次 Cortex 函数查询的 Credit 使用量，基于函数和模型以一小时为增量汇总。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY
  WHERE query_id = 'query-id';

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Compute for Cortex REST API¶

Query: Credit consumption by Cortex REST API.

This query shows the credit consumption for Cortex REST API calls, including the number of tokens processed and the model used for each request.

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_REST_API_USAGE_HISTORY;

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Cortex Search 的计算¶

查询：Cortex Search 的每日 Credit 使用量。

此查询显示每个 Cortex Search Service 的 Credit 使用量，每日汇总，包括服务和嵌入文本令牌使用量。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_SEARCH_DAILY_USAGE_HISTORY;

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查询：Cortex Search 服务的 Credit 使用量。

此查询显示每项 Cortex Search 服务的服务 Credit 使用量，以一小时为增量汇总。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_SEARCH_SERVING_USAGE_HISTORY;

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Document AI 的计算¶

查询：Document AI 的 Credit 使用量。

此查询显示的 Document AI Credit 使用量。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.DOCUMENT_AI_USAGE_HISTORY;

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查询：每次 Document AI 查询的 Credit 使用量。

此查询从 CREDITS_USED 大于 0.072 的 CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY 视图中检索记录。

SELECT *
  FROM SNOWFLAKE.ACCOUNT_USAGE.CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY
  WHERE CREDITS_USED > 0.072

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Snowflake Notebooks 的计算¶

查询：笔记本每小时的 Credit 使用量

此查询检索特定笔记本的运行时历史记录，包括 Credit 使用量和执行时间戳。使用这些数据来了解笔记本的运行频率和时长，并确定每小时的 Credit 使用模式或峰值。

SELECT * FROM snowflake.account_usage.notebooks_container_runtime_history
WHERE notebook_name = '<example_nb_name>';

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查询：运行特定笔记本的成本

此查询显示特定笔记本的总 Credit 使用量。使用它来估算笔记本的成本并确定高成本笔记本。

SELECT
  notebook_name,
  SUM(credits) AS total_credits
FROM snowflake.account_usage.notebooks_container_runtime_history
WHERE notebook_name = '<example_nb_name>'
GROUP BY notebook_name;

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查询：每个笔记本的总计算池成本

此查询显示在特定计算池上运行的每个笔记本的总 Credit 使用量。使用它来细分笔记本的计算使用情况，这有助于确定哪些笔记本消耗的计算池的总成本最高。

SELECT
  notebook_name,
  SUM(credits) AS total_credits
FROM snowflake.account_usage.notebooks_container_runtime_history
WHERE compute_pool_name = '<example_cp_name>'
GROUP BY notebook_name;

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查询：识别运行特定笔记本的用户

此查询会返回执行特定笔记本的用户的列表。使用它来了解使用模式，或识别共享笔记本的协作者和使用者。

SELECT
  DISTINCT user_name
FROM snowflake.account_usage.notebooks_container_runtime_history
WHERE notebook_name = '<example_nb_name>';

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