CnosDB 是一个专注于时序数据处理的数据库，旨在解决时序数据存储与分析问题，为用户提供高效的时序数据管理与查询便利。为了实现这一目标，CnosDB 实现了一系列专用函数，快来和CC一起来看看吧！
CnosDB：深入了解时序数据处理函数
stats_agg
用途
stats_agg 函数适用于需要对时序数据进行统计分析的场景，例如计算相关系数和协方差。
并且还可以分别计算每个维度的常见统计数据，例如平均值和标准差。
stats_agg 提供了与sum，count，corr，covar_pop，stddev 和 stddev_pop 等聚合函数相同的功能，
适用于一条SQL中，包含多个分析函数的场景。
函数用法
stats_agg(y, x)参数类型：
-
y: double 类型
-
x: double 类型
返回结果：
{
n: bigint, -- count
sx: double, -- sum(x)- sum(x)
sx2: double, -- sum((x-sx/n)^2) (sum of squares)
sx3: double, -- sum((x-sx/n)^3)
sx4: double, -- sum((x-sx/n)^4)
sy: double, -- sum(y)
sy2: double, -- sum((y-sy/n)^2) (sum of squares)
sy3: double, -- sum((y-sy/n)^3)
sy4: double, -- sum((y-sy/n)^4)
sxy: double, -- sum((x-sx/n)*(y-sy/n)) (sum of products)
}示例
想象一下，你有一张表记录了某个测试项目的执行情况，其中 x 表示时间，y 表示对应的测试数据。你想要了解这些测试数据的分布规律，而 stats_agg 就是你的得力助手。
create table if not exists test_stats(x bigint, y bigint);
insert into test_stats(time, x, y) values
(1, 1, 1),
(2, 1, 2),
(3, 1, 3),
(4, 1, 4),
(5, 1, 5),
(6, 2, 1),
(7, 2, 2),
(8, 2, 3),
(9, 2, 4),
(10, 2, 5);select stats_agg(y, x) from test_stats;在这个例子中，stats_agg 会告诉你这些测试数据的数量（n）、时间的总和（sx）、时间的平方和（sx2），以及测试数据的总和（sy）、测试数据的平方和（sy2）等等。
+------------------------------------------------------------------------------------------------------------------------------------------------------------+
| stats_agg(test_stats.y,test_stats.x) |
+------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {n: 10, sx: 15.0, sx2: 2.5, sx3: -2.7755575615628914e-16, sx4: 0.6249999999999999, sy: 30.0, sy2: 20.0, sy3: -1.7763568394002505e-15, sy4: 68.0, sxy: 0.0} |
+------------------------------------------------------------------------------------------------------------------------------------------------------------+注意事项
输入参数都不为 NULL 时才纳入聚合
gauge_agg
用途
适用于监控系统、物联网应用和金融领域，提供时间段内的数据快照。
函数用法
gauge_agg(time, value)参数类型：
-
time: Timestamp 类型
-
value: DOUBLE 类型
返回结果：
{
first: { ts: Timestamp, value: Double },
second: { ts: Timestamp, value: Double },
penultimate: { ts: Timestamp, value: Double },
last: { ts: Timestamp, value: Double },
num_elements: Bigint Unsingned
}示例
这个 SQL 查询使用了 gauge_agg 函数，目的是对空气质量数据按月进行聚合，提供每个月内的压力数据快照。
SELECT gauge_agg(time, pressure) FROM air GROUP BY date_trunc('month', time);这条查询的目的是从名为 "air" 的数据表中选取时间 (time) 和压力 (pressure) 列的数据，并按照每月的时间戳 (date_trunc('month', time)) 进行分组。最后，对每个月的数据应用 gauge_agg 函数。
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| gauge_agg(air.time,air.pressure) |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {first: {ts: 2023-03-01T00:00:00, val: 54.0}, second: {ts: 2023-03-01T00:00:00, val: 59.0}, penultimate: {ts: 2023-03-14T16:00:00, val: 55.0}, last: {ts: 2023-03-14T16:00:00, val: 80.0}, num_elements: 13122} |
| {first: {ts: 2023-01-14T16:00:00, val: 63.0}, second: {ts: 2023-01-14T16:00:00, val: 68.0}, penultimate: {ts: 2023-01-31T23:57:00, val: 77.0}, last: {ts: 2023-01-31T23:57:00, val: 54.0}, num_elements: 16640} |
| {first: {ts: 2023-02-01T00:00:00, val: 54.0}, second: {ts: 2023-02-01T00:00:00, val: 60.0}, penultimate: {ts: 2023-02-28T23:57:00, val: 74.0}, last: {ts: 2023-02-28T23:57:00, val: 59.0}, num_elements: 26880} |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+上面的数据，每行结果表示一个月的数据聚合结果。
first 到 last 分别表示每个月内的第一、第二、倒数第二和最后一条数据的时间戳和压力数值。
num_elements 表示该月的数据点数量。
compact_state_agg/state_agg
用途
用于跟踪状态的持续时间，适用于设备健康监控、事件统计等。
函数用法
compact_state_agg(time, state)
state_agg(time, state)参数类型：
-
time: Timestamp 类型
-
state: 任意类型
返回结果：
{
state_duration: [
{ state: Any, interval: Duration },
...
],
state_periods: [
{
state: Any,
periods: [
{ start_time: Timestamp, end_time: Timestamp },
...
]
},
...
]
}示例
在一个名为 states 的表中插入了一些包含时间戳和状态信息的数据。然后，通过使用两个不同的 SQL 查询来调用 compact_state_agg 和 state_agg 函数。
CREATE TABLE IF NOT EXISTS states(state STRING);
INSERT INTO states VALUES
('2020-01-01 10:00:00', 'starting'),
('2020-01-01 10:30:00', 'running'),
('2020-01-03 16:00:00', 'error'),
('2020-01-03 18:30:00', 'starting'),
('2020-01-03 19:30:00', 'running'),
('2020-01-05 12:00:00', 'stopping');compact_state_agg 示例
SELECT compact_state_agg(time, state) FROM states;返回的结果是一个 JSON 格式的对象，其中包含了不同状态的总持续时间 (state_duration)。在这个示例中，它显示了每个状态的总持续时间，但没有提供每个状态的具体切换周期。
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| compact_state_agg(states.time,states.state) |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {state_duration: [{state: running, duration: 0 years 0 mons 3 days 22 hours 0 mins 0.000000000 secs}, {state: error, duration: 0 years 0 mons 0 days 2 hours 30 mins 0.000000000 secs}, {state: starting, duration: 0 years 0 mons 0 days 1 hours 30 mins 0.000000000 secs}, {state: stopping, duration: 0 years 0 mons 0 days 0 hours 0 mins 0.000000000 secs}], state_periods: []} |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+state_agg 示例
SELECT state_agg(time, state) FROM states;返回的结果同样是一个 JSON 格式的对象，其中包含了每个状态的总持续时间 (state_duration) 和每个状态的切换周期 (state_periods)。在这个示例中，state_periods 列表显示了每个状态的具体切换周期，包括开始时间和结束时间。
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| state_agg(states.time,states.state) |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {state_duration: [{state: starting, duration: 0 years 0 mons 0 days 1 hours 30 mins 0.000000000 secs}, {state: running, duration: 0 years 0 mons 3 days 22 hours 0 mins 0.000000000 secs}, {state: stopping, duration: 0 years 0 mons 0 days 0 hours 0 mins 0.000000000 secs}, {state: error, duration: 0 years 0 mons 0 days 2 hours 30 mins 0.000000000 secs}], state_periods: [{state: starting, periods: [{start_time: 2020-01-01T10:00:00, end_time: 2020-01-01T10:30:00}, {start_time: 2020-01-03T18:30:00, end_time: 2020-01-03T19:30:00}]}, {state: error, periods: [{start_time: 2020-01-03T16:00:00, end_time: 2020-01-03T18:30:00}]}, {state: running, periods: [{start_time: 2020-01-01T10:30:00, end_time: 2020-01-03T16:00:00}, {start_time: 2020-01-03T19:30:00, end_time: 2020-01-05T12:00:00}]}]} |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+注意事项
-
state_agg 会记录每个状态的每个持续时间段，当状态基数较大或持续时间段较分散的情况下，会使用较大内存，谨慎使用。
-
compact_state_agg 返回的数据中不包含 state_periods字段。会将每个状态的所有持续时间段汇总成总时间，占用内存相对较少，两个函数可以根据具体场景来抉择使用。
所以，这两个函数的区别在于 compact_state_agg 更关注总体持续时间，而 state_agg 提供了更详细的状态切换周期信息。
candlestick_agg
用途
适用于金融领域，生成开盘价、收盘价、最高价和最低价的数据。
函数用法
candlestick_agg(time, price, volume)参数类型：
-
time: Timestamp 类型
-
price: Double 类型
-
volume: Double 类型
返回类型：
{
open: { ts: Timestamp, val: Double },
close: { ts: Timestamp, val: Double },
high: { ts: Timestamp, val: Double },
low: { ts: Timestamp, val: Double },
volume: { vol: Double, vwap: Double },
}示例
那么现在创建一个示例数据集。
CREATE TABLE IF NOT EXISTS tick(price bigint ,volume bigint);
INSERT tick(time, price, volume)
VALUES
('1999-12-31 00:00:00.000', 111, 444),
('1999-12-31 00:00:00.005', 222, 444),
('1999-12-31 00:00:00.010', 333, 222),
('1999-12-31 00:00:10.015', 444, 111),
('1999-12-31 00:00:10.020', 222, 555),
('1999-12-31 00:10:00.025', 333, 555),
('1999-12-31 00:10:00.030', 444, 333),
('1999-12-31 01:00:00.035', 555, 222);下列语句查询了关于时间序列数据的蜡烛图形式的统计信息，包括开盘价、收盘价、最低价。
SELECT candlestick_agg(time, price, volume) FROM tick;返回结果：
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| candlestick_agg(tick.time,tick.price,tick.volume) |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {open: {ts: 1999-12-31T00:00:00, val: 111.0}, close: {ts: 1999-12-31T01:00:00.035, val: 555.0}, low: {ts: 1999-12-31T00:00:00, val: 111.0}, high: {ts: 1999-12-31T01:00:00.035, val: 555.0}, volume: {vol: 2886.0, vwap: 850149.0}} |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+asap_smooth
用途
ASAP_SMOOTH 是一种降采样函数，适用于可视化和数据探索、预测和模型训练等场景。
函数用法
asap_smooth(time, value, resolution order by time)参数类型：
-
time: Timestamp 类型
-
value: Double 类型
-
resolution: Bigint 类型
返回结果：
{
time: [ Timestamp, ... ],
value: [ Double, ... ],
resolution: Int Unsigned,
}示例
使用 asap_smooth 函数，对空气质量数据按月进行聚合，并在压力 (pressure) 数据上应用 ASAP (As Soon As Possible) 平滑，以得到平滑的时间序列数据。
SELECT asap_smooth(time, pressure, 10) FROM air GROUP BY date_trunc('month', time);返回结果：
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| asap_smooth(air.time,air.pressure,Int64(10)) |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {time: [2023-01-14T16:00:00, 2023-01-16T14:13:00, 2023-01-18T12:26:00, 2023-01-20T10:39:00, 2023-01-22T08:52:00, 2023-01-24T07:05:00, 2023-01-26T05:18:00, 2023-01-28T03:31:00, 2023-01-30T01:44:00, 2023-01-31T23:57:00], value: [64.79507211538461, 65.31009615384616, 65.25841346153847, 64.8485576923077, 65.09495192307692, 65.02524038461539, 64.8389423076923, 65.2421875, 65.02103365384616, 65.1141826923077], resolution: 10} |
| {time: [2023-02-01T00:00:00, 2023-02-04T02:39:40, 2023-02-07T05:19:20, 2023-02-10T07:59:00, 2023-02-13T10:38:40, 2023-02-16T13:18:20, 2023-02-19T15:58:00, 2023-02-22T18:37:40, 2023-02-25T21:17:20, 2023-02-28T23:57:00], value: [65.20982142857143, 64.90625, 64.94828869047619, 64.97916666666667, 64.88504464285714, 64.8203125, 64.64434523809524, 64.88802083333333, 65.0, 64.76004464285714], resolution: 10} |
| {time: [2023-03-01T00:00:00, 2023-03-02T12:26:40, 2023-03-04T00:53:20, 2023-03-05T13:20:00, 2023-03-07T01:46:40, 2023-03-08T14:13:20, 2023-03-10T02:40:00, 2023-03-11T15:06:40, 2023-03-13T03:33:20, 2023-03-14T16:00:00], value: [65.29115853658537, 64.58307926829268, 64.7530487804878, 64.76753048780488, 65.14405487804878, 65.4298780487805, 65.1920731707317, 65.10365853658537, 64.86356707317073, 64.83841463414635], resolution: 10} |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+注意事项
-
此函数对时间顺序敏感，在使用时需要添加 `order by time`。
-
返回值中的 time 和 value 为数组类型。
-
此函数在执行时会一次性处理所有数据，所以尽可能避免读入大量数据导致过多的内存开销。
CnosDB，这可不是普通的数据库，是你数据冒险的好伙伴，助你在数据的海洋中航行无忧！
CnosDB简介
CnosDB是一款高性能、高易用性的开源分布式时序数据库，现已正式发布及全部开源。
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