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使用SQL语句按照一定时间间隔填充时间的方法

作者：衡水世耀科技有限公司

本文总结了在不同数据库中按固定时间间隔补齐时间轴的SQL做法,涵盖了PostgreSQL、MySQL、SQLServer、SQLite、BigQuery和Snowflake等常见数据库,每种数据库都有其特定的方法,感兴趣的朋友跟随小编一起看看吧

我们在工作当中经常会遇到填充时间轴的问题，我整理了一份通用“按固定时间间隔补齐时间轴”的SQL做法合集，覆盖常见数据库（PostgreSQL、MySQL、SQL Server、SQLite、BigQuery、Snowflake）。你可以选用与你环境匹配的版本。思路都一样：

  1. 先生成一条连续的时间序列（按分钟/小时/天等间隔）；
  2. 用这条时间序列和你的数据LEFT JOIN，对缺失点补 0 或空值；
  3. 再做需要的聚合（如每5分钟求和/计数/均值）。

1) PostgreSQL / Amazon Redshift（推荐，最简洁）

PostgreSQL 原生有 generate_series，写法非常优雅。

示例：每5分钟补齐一次，统计每5分钟事件数

WITH ts AS (
  SELECT generate_series(
           timestamp '2025-10-01 00:00:00',
           timestamp '2025-10-02 00:00:00',
           interval '5 minute'
         ) AS bucket
),
events AS (
  -- 你的原始数据表，假设字段：event_time (timestamp)
  SELECT date_trunc('minute', event_time) AS minute_ts
  FROM public.event_log
  WHERE event_time >= '2025-10-01 00:00:00'
    AND event_time <  '2025-10-02 00:00:00'
)
SELECT
  ts.bucket,
  COALESCE(cnt.c, 0) AS event_count
FROM ts
LEFT JOIN (
  SELECT date_trunc('minute', minute_ts) - (extract(minute FROM minute_ts)::int % 5) * interval '1 minute'
           AS bucket_5m,
         count(*) AS c
  FROM events
  GROUP BY 1
) cnt
ON ts.bucket = cnt.bucket_5m
ORDER BY ts.bucket;

如果你的数据已经是整分，就可以把上面的“对5分钟分箱”的表达式简化成 date_trunc('minute', event_time) 再对 interval '5 minute' 进行对齐。

每天/每小时序列只需把 interval '5 minute' 换成 interval '1 hour' 或 interval '1 day'，同时聚合逻辑改为 date_trunc('hour'/'day', ...)。

2) MySQL 8.0+（使用递归CTE）

MySQL 没有内置的 generate_series，我们用递归CTE造序列。

示例：每15分钟补齐一次

WITH RECURSIVE ts AS (
  SELECT TIMESTAMP('2025-10-01 00:00:00') AS bucket
  UNION ALL
  SELECT bucket + INTERVAL 15 MINUTE
  FROM ts
  WHERE bucket < '2025-10-02 00:00:00'
),
events AS (
  SELECT event_time
  FROM event_log
  WHERE event_time >= '2025-10-01 00:00:00'
    AND event_time <  '2025-10-02 00:00:00'
),
agg AS (
  SELECT
    -- 把 event_time 对齐到 15分钟的时间桶
    FROM_UNIXTIME(FLOOR(UNIX_TIMESTAMP(event_time) / (15*60)) * (15*60)) AS bucket_15m,
    COUNT(*) AS c
  FROM events
  GROUP BY 1
)
SELECT
  ts.bucket,
  COALESCE(agg.c, 0) AS event_count
FROM ts
LEFT JOIN agg
  ON ts.bucket = agg.bucket_15m
ORDER BY ts.bucket
OPTION MAX_RECURSION_DEPTH = 100000; -- 如有需要可调整

性能提示：长时间跨度建议用“辅助数字表/日历表/时间维表”替代递归；或者先生成按天的序列再在应用层扩展。

3) SQL Server（两种做法：递归CTE 或 Tally/Numbers 表）

3.1 递归CTE

WITH ts AS (
  SELECT CAST('2025-10-01T00:00:00' AS datetime2) AS bucket
  UNION ALL
  SELECT DATEADD(minute, 10, bucket)
  FROM ts
  WHERE bucket < CAST('2025-10-02T00:00:00' AS datetime2)
),
agg AS (
  SELECT DATEADD(minute,
                 DATEDIFF(minute, 0, event_time) / 10 * 10, 0) AS bucket_10m,
         COUNT(*) AS c
  FROM dbo.EventLog
  WHERE event_time >= '2025-10-01T00:00:00'
    AND event_time <  '2025-10-02T00:00:00'
  GROUP BY DATEADD(minute, DATEDIFF(minute, 0, event_time) / 10 * 10, 0)
)
SELECT ts.bucket,
       ISNULL(agg.c, 0) AS event_count
FROM ts
LEFT JOIN agg
  ON ts.bucket = agg.bucket_10m
ORDER BY ts.bucket
OPTION (MAXRECURSION 0);

3.2 Numbers/Tally 表（更高效，推荐生产）

先准备一个连续整数表（可持久化）。随后：

DECLARE @start datetime2 = '2025-10-01T00:00:00';
DECLARE @end   datetime2 = '2025-10-02T00:00:00';

WITH ts AS (
  SELECT DATEADD(minute, n*5, @start) AS bucket
  FROM dbo.Numbers
  WHERE DATEADD(minute, n*5, @start) <= @end
)
-- 其余与上面 LEFT JOIN 聚合同理

4) SQLite（递归CTE）

WITH RECURSIVE ts(bucket) AS (
  SELECT DATETIME('2025-10-01 00:00:00')
  UNION ALL
  SELECT DATETIME(bucket, '+5 minutes')
  FROM ts
  WHERE bucket < '2025-10-02 00:00:00'
),
agg AS (
  SELECT
    DATETIME(STRFTIME('%s', event_time) / (5*60) * (5*60), 'unixepoch') AS bucket_5m,
    COUNT(*) AS c
  FROM event_log
  WHERE event_time >= '2025-10-01 00:00:00'
    AND event_time <  '2025-10-02 00:00:00'
  GROUP BY 1
)
SELECT ts.bucket, IFNULL(agg.c, 0) AS event_count
FROM ts
LEFT JOIN agg ON ts.bucket = agg.bucket_5m
ORDER BY ts.bucket;

5) BigQuery（原生数组函数，非常方便）

WITH ts AS (
  SELECT
    ts AS bucket
  FROM UNNEST(
    GENERATE_TIMESTAMP_ARRAY(
      TIMESTAMP('2025-10-01 00:00:00+00'),
      TIMESTAMP('2025-10-02 00:00:00+00'),
      INTERVAL 15 MINUTE
    )
  ) AS ts
),
agg AS (
  SELECT
    TIMESTAMP_TRUNC(event_time, MINUTE) - 
      INTERVAL MOD(EXTRACT(MINUTE FROM event_time), 15) MINUTE AS bucket_15m,
    COUNT(*) AS c
  FROM `project.dataset.event_log`
  WHERE event_time >= TIMESTAMP('2025-10-01 00:00:00+00')
    AND event_time <  TIMESTAMP('2025-10-02 00:00:00+00')
  GROUP BY 1
)
SELECT ts.bucket, IFNULL(agg.c, 0) AS event_count
FROM ts
LEFT JOIN agg
  ON ts.bucket = agg.bucket_15m
ORDER BY ts.bucket;

6) Snowflake（使用 GENERATOR）

WITH params AS (
  SELECT
    TO_TIMESTAMP('2025-10-01 00:00:00') AS start_ts,
    TO_TIMESTAMP('2025-10-02 00:00:00') AS end_ts,
    5 AS step_min
),
ts AS (
  SELECT
    DATEADD(minute, seq4()*step_min, start_ts) AS bucket
  FROM params,
       TABLE(GENERATOR(ROWCOUNT => 100000000)) -- 上限要能覆盖区间长度
  QUALIFY bucket <= (SELECT end_ts FROM params)
),
agg AS (
  SELECT
    DATE_TRUNC('minute', event_time) - 
      (DATE_PART(minute, event_time) % 5) * INTERVAL '1 minute' AS bucket_5m,
    COUNT(*) AS c
  FROM EVENT_LOG
  WHERE event_time >= (SELECT start_ts FROM params)
    AND event_time <  (SELECT end_ts   FROM params)
  GROUP BY 1
)
SELECT ts.bucket, COALESCE(agg.c, 0) AS event_count
FROM ts
LEFT JOIN agg ON ts.bucket = agg.bucket_5m
ORDER BY ts.bucket;

ROWCOUNT 要覆盖足够的时间点：大致 = (总分钟数 / step_min) + 1。

通用“参数化模版”

把这段思想搬到任何库都成立：

  1. 定义参数：start_ts、end_ts、step（分钟/小时/天）。
  2. 生成连续时间（递归CTE、内置序列函数、Numbers表、数组生成等）。
  3. 对齐/分箱：把事实表时间戳落到 step 对齐的“时间桶”。
  4. LEFT JOIN + COALESCE：保证缺失点返回 0。
  5. ORDER BY 时间桶。

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