Clickhouse moving average
Input:
Clickhouse
Table A
business_dttm (datetime)
amount (float)
I need to calculate moving sum for 15 minutes (or for last 3 records) on each business_dttm
For example
amount business_dttm moving sum
0.3 2018-11-19 13:00:00
0.3 2018-11-19 13:05:00
0.4 2018-11-19 13:10:00 1
0.5 2018-11-19 13:15:00 1.2
0.6 2018-11-19 13:15:00 1.5
0.7 2018-11-19 13:20:00 1.8
0.8 2018-11-19 13:25:00 2.1
0.9 2018-11-19 13:25:00 2.4
0.5 2018-11-19 13:30:00 2.2
Unfortunately we haven't window functions and join without equal conditions in Clickhouse
How can i do it without cross join and where condition?
moving-average clickhouse
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
add a comment |
Input:
Clickhouse
Table A
business_dttm (datetime)
amount (float)
I need to calculate moving sum for 15 minutes (or for last 3 records) on each business_dttm
For example
amount business_dttm moving sum
0.3 2018-11-19 13:00:00
0.3 2018-11-19 13:05:00
0.4 2018-11-19 13:10:00 1
0.5 2018-11-19 13:15:00 1.2
0.6 2018-11-19 13:15:00 1.5
0.7 2018-11-19 13:20:00 1.8
0.8 2018-11-19 13:25:00 2.1
0.9 2018-11-19 13:25:00 2.4
0.5 2018-11-19 13:30:00 2.2
Unfortunately we haven't window functions and join without equal conditions in Clickhouse
How can i do it without cross join and where condition?
moving-average clickhouse
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
add a comment |
Input:
Clickhouse
Table A
business_dttm (datetime)
amount (float)
I need to calculate moving sum for 15 minutes (or for last 3 records) on each business_dttm
For example
amount business_dttm moving sum
0.3 2018-11-19 13:00:00
0.3 2018-11-19 13:05:00
0.4 2018-11-19 13:10:00 1
0.5 2018-11-19 13:15:00 1.2
0.6 2018-11-19 13:15:00 1.5
0.7 2018-11-19 13:20:00 1.8
0.8 2018-11-19 13:25:00 2.1
0.9 2018-11-19 13:25:00 2.4
0.5 2018-11-19 13:30:00 2.2
Unfortunately we haven't window functions and join without equal conditions in Clickhouse
How can i do it without cross join and where condition?
moving-average clickhouse
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
Input:
Clickhouse
Table A
business_dttm (datetime)
amount (float)
I need to calculate moving sum for 15 minutes (or for last 3 records) on each business_dttm
For example
amount business_dttm moving sum
0.3 2018-11-19 13:00:00
0.3 2018-11-19 13:05:00
0.4 2018-11-19 13:10:00 1
0.5 2018-11-19 13:15:00 1.2
0.6 2018-11-19 13:15:00 1.5
0.7 2018-11-19 13:20:00 1.8
0.8 2018-11-19 13:25:00 2.1
0.9 2018-11-19 13:25:00 2.4
0.5 2018-11-19 13:30:00 2.2
Unfortunately we haven't window functions and join without equal conditions in Clickhouse
How can i do it without cross join and where condition?
moving-average clickhouse
moving-average clickhouse
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
asked Nov 21 at 16:29
Vsevolod Lukovsky
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
If the window size is countably small, you can do something like this
SELECT
sum(window.2) AS amount,
max(dttm) AS business_dttm,
sum(amt) AS moving_sum
FROM
(
SELECT
arrayJoin([(rowNumberInAllBlocks(), amount), (rowNumberInAllBlocks() + 1, 0), (rowNumberInAllBlocks() + 2, 0)]) AS window,
amount AS amt,
business_dttm AS dttm
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
)
GROUP BY window.1
HAVING count() = 3
ORDER BY window.1;
The first two rows are ignored as ClickHouse doesn't collapse aggregates into null. You can prepend them later.
Update:
It's still possible to compute moving sum for arbitrary window sizes. Tune the window_size as you want (3 for this example).
-- Note, rowNumberInAllBlocks is incorrect if declared inside with block due to being stateful
WITH
(
SELECT arrayCumSum(groupArray(amount))
FROM
(
SELECT
amount
FROM A
ORDER BY business_dttm
)
) AS arr,
3 AS window_size
SELECT
amount,
business_dttm,
if(rowNumberInAllBlocks() + 1 < window_size, NULL, arr[rowNumberInAllBlocks() + 1] - arr[rowNumberInAllBlocks() + 1 - window_size]) AS moving_sum
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
Or this variant
SELECT
amount,
business_dttm,
moving_sum
FROM
(
WITH 3 AS window_size
SELECT
groupArray(amount) AS amount_arr,
groupArray(business_dttm) AS business_dttm_arr,
arrayCumSum(amount_arr) AS amount_cum_arr,
arrayMap(i -> if(i < window_size, NULL, amount_cum_arr[i] - amount_cum_arr[(i - window_size)]), arrayEnumerate(amount_cum_arr)) AS moving_sum_arr
FROM
(
SELECT *
FROM A
ORDER BY business_dttm ASC
)
)
ARRAY JOIN
amount_arr AS amount,
business_dttm_arr AS business_dttm,
moving_sum_arr AS moving_sum
Fair warning, both approaches are far from optimal, but it exhibits the unique power of ClickHouse beyond SQL.
answered Nov 22 at 9:33
Amos
1,36921028
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
add a comment |
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1 Answer
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1 Answer
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oldest
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oldest
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oldest
votes
If the window size is countably small, you can do something like this
SELECT
sum(window.2) AS amount,
max(dttm) AS business_dttm,
sum(amt) AS moving_sum
FROM
(
SELECT
arrayJoin([(rowNumberInAllBlocks(), amount), (rowNumberInAllBlocks() + 1, 0), (rowNumberInAllBlocks() + 2, 0)]) AS window,
amount AS amt,
business_dttm AS dttm
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
)
GROUP BY window.1
HAVING count() = 3
ORDER BY window.1;
The first two rows are ignored as ClickHouse doesn't collapse aggregates into null. You can prepend them later.
Update:
It's still possible to compute moving sum for arbitrary window sizes. Tune the window_size as you want (3 for this example).
-- Note, rowNumberInAllBlocks is incorrect if declared inside with block due to being stateful
WITH
(
SELECT arrayCumSum(groupArray(amount))
FROM
(
SELECT
amount
FROM A
ORDER BY business_dttm
)
) AS arr,
3 AS window_size
SELECT
amount,
business_dttm,
if(rowNumberInAllBlocks() + 1 < window_size, NULL, arr[rowNumberInAllBlocks() + 1] - arr[rowNumberInAllBlocks() + 1 - window_size]) AS moving_sum
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
Or this variant
SELECT
amount,
business_dttm,
moving_sum
FROM
(
WITH 3 AS window_size
SELECT
groupArray(amount) AS amount_arr,
groupArray(business_dttm) AS business_dttm_arr,
arrayCumSum(amount_arr) AS amount_cum_arr,
arrayMap(i -> if(i < window_size, NULL, amount_cum_arr[i] - amount_cum_arr[(i - window_size)]), arrayEnumerate(amount_cum_arr)) AS moving_sum_arr
FROM
(
SELECT *
FROM A
ORDER BY business_dttm ASC
)
)
ARRAY JOIN
amount_arr AS amount,
business_dttm_arr AS business_dttm,
moving_sum_arr AS moving_sum
Fair warning, both approaches are far from optimal, but it exhibits the unique power of ClickHouse beyond SQL.
answered Nov 22 at 9:33
Amos
1,36921028
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
add a comment |
If the window size is countably small, you can do something like this
SELECT
sum(window.2) AS amount,
max(dttm) AS business_dttm,
sum(amt) AS moving_sum
FROM
(
SELECT
arrayJoin([(rowNumberInAllBlocks(), amount), (rowNumberInAllBlocks() + 1, 0), (rowNumberInAllBlocks() + 2, 0)]) AS window,
amount AS amt,
business_dttm AS dttm
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
)
GROUP BY window.1
HAVING count() = 3
ORDER BY window.1;
The first two rows are ignored as ClickHouse doesn't collapse aggregates into null. You can prepend them later.
Update:
It's still possible to compute moving sum for arbitrary window sizes. Tune the window_size as you want (3 for this example).
-- Note, rowNumberInAllBlocks is incorrect if declared inside with block due to being stateful
WITH
(
SELECT arrayCumSum(groupArray(amount))
FROM
(
SELECT
amount
FROM A
ORDER BY business_dttm
)
) AS arr,
3 AS window_size
SELECT
amount,
business_dttm,
if(rowNumberInAllBlocks() + 1 < window_size, NULL, arr[rowNumberInAllBlocks() + 1] - arr[rowNumberInAllBlocks() + 1 - window_size]) AS moving_sum
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
Or this variant
SELECT
amount,
business_dttm,
moving_sum
FROM
(
WITH 3 AS window_size
SELECT
groupArray(amount) AS amount_arr,
groupArray(business_dttm) AS business_dttm_arr,
arrayCumSum(amount_arr) AS amount_cum_arr,
arrayMap(i -> if(i < window_size, NULL, amount_cum_arr[i] - amount_cum_arr[(i - window_size)]), arrayEnumerate(amount_cum_arr)) AS moving_sum_arr
FROM
(
SELECT *
FROM A
ORDER BY business_dttm ASC
)
)
ARRAY JOIN
amount_arr AS amount,
business_dttm_arr AS business_dttm,
moving_sum_arr AS moving_sum
Fair warning, both approaches are far from optimal, but it exhibits the unique power of ClickHouse beyond SQL.
answered Nov 22 at 9:33
Amos
1,36921028
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
add a comment |
If the window size is countably small, you can do something like this
SELECT
sum(window.2) AS amount,
max(dttm) AS business_dttm,
sum(amt) AS moving_sum
FROM
(
SELECT
arrayJoin([(rowNumberInAllBlocks(), amount), (rowNumberInAllBlocks() + 1, 0), (rowNumberInAllBlocks() + 2, 0)]) AS window,
amount AS amt,
business_dttm AS dttm
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
)
GROUP BY window.1
HAVING count() = 3
ORDER BY window.1;
The first two rows are ignored as ClickHouse doesn't collapse aggregates into null. You can prepend them later.
Update:
It's still possible to compute moving sum for arbitrary window sizes. Tune the window_size as you want (3 for this example).
-- Note, rowNumberInAllBlocks is incorrect if declared inside with block due to being stateful
WITH
(
SELECT arrayCumSum(groupArray(amount))
FROM
(
SELECT
amount
FROM A
ORDER BY business_dttm
)
) AS arr,
3 AS window_size
SELECT
amount,
business_dttm,
if(rowNumberInAllBlocks() + 1 < window_size, NULL, arr[rowNumberInAllBlocks() + 1] - arr[rowNumberInAllBlocks() + 1 - window_size]) AS moving_sum
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
Or this variant
SELECT
amount,
business_dttm,
moving_sum
FROM
(
WITH 3 AS window_size
SELECT
groupArray(amount) AS amount_arr,
groupArray(business_dttm) AS business_dttm_arr,
arrayCumSum(amount_arr) AS amount_cum_arr,
arrayMap(i -> if(i < window_size, NULL, amount_cum_arr[i] - amount_cum_arr[(i - window_size)]), arrayEnumerate(amount_cum_arr)) AS moving_sum_arr
FROM
(
SELECT *
FROM A
ORDER BY business_dttm ASC
)
)
ARRAY JOIN
amount_arr AS amount,
business_dttm_arr AS business_dttm,
moving_sum_arr AS moving_sum
Fair warning, both approaches are far from optimal, but it exhibits the unique power of ClickHouse beyond SQL.
answered Nov 22 at 9:33
Amos
1,36921028
If the window size is countably small, you can do something like this
SELECT
sum(window.2) AS amount,
max(dttm) AS business_dttm,
sum(amt) AS moving_sum
FROM
(
SELECT
arrayJoin([(rowNumberInAllBlocks(), amount), (rowNumberInAllBlocks() + 1, 0), (rowNumberInAllBlocks() + 2, 0)]) AS window,
amount AS amt,
business_dttm AS dttm
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
)
GROUP BY window.1
HAVING count() = 3
ORDER BY window.1;
The first two rows are ignored as ClickHouse doesn't collapse aggregates into null. You can prepend them later.
Update:
It's still possible to compute moving sum for arbitrary window sizes. Tune the window_size as you want (3 for this example).
-- Note, rowNumberInAllBlocks is incorrect if declared inside with block due to being stateful
WITH
(
SELECT arrayCumSum(groupArray(amount))
FROM
(
SELECT
amount
FROM A
ORDER BY business_dttm
)
) AS arr,
3 AS window_size
SELECT
amount,
business_dttm,
if(rowNumberInAllBlocks() + 1 < window_size, NULL, arr[rowNumberInAllBlocks() + 1] - arr[rowNumberInAllBlocks() + 1 - window_size]) AS moving_sum
FROM
(
SELECT
amount,
business_dttm
FROM A
ORDER BY business_dttm
)
Or this variant
SELECT
amount,
business_dttm,
moving_sum
FROM
(
WITH 3 AS window_size
SELECT
groupArray(amount) AS amount_arr,
groupArray(business_dttm) AS business_dttm_arr,
arrayCumSum(amount_arr) AS amount_cum_arr,
arrayMap(i -> if(i < window_size, NULL, amount_cum_arr[i] - amount_cum_arr[(i - window_size)]), arrayEnumerate(amount_cum_arr)) AS moving_sum_arr
FROM
(
SELECT *
FROM A
ORDER BY business_dttm ASC
)
)
ARRAY JOIN
amount_arr AS amount,
business_dttm_arr AS business_dttm,
moving_sum_arr AS moving_sum
Fair warning, both approaches are far from optimal, but it exhibits the unique power of ClickHouse beyond SQL.
answered Nov 22 at 9:33
Amos
1,36921028
edited Nov 22 at 18:46
answered Nov 22 at 9:33
Amos
1,36921028
answered Nov 22 at 9:33
Amos
1,36921028
answered Nov 22 at 9:33
Amos
1,36921028
1,36921028
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
add a comment |
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Unfortunately, window size ~ 10000 rows
– Vsevolod Lukovsky
Nov 22 at 12:37
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Thanks for answer, but 1 moment. I was talking about Moving Sum, Not Cumulative Sum. Is it real to do it for Moving Sum?
– Vsevolod Lukovsky
Nov 23 at 21:49
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
Sorry! Just have tried, that's working :)
– Vsevolod Lukovsky
Nov 23 at 22:07
1
1
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
@VsevolodLukovsky please accept the answer if it solves your problem
– Amos
Nov 24 at 4:09
add a comment |
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