I have to find the next higher sequence-number depending on certain where-conditions:
TABLE:
+-------+------------------+---------------------------+
| Seq | Start_Time | Queue |
+-------+------------------+---------------------------+
| 34962 | 28.07.2020 17:06 | PQ_NEW PRICE REQUEST GMDM |
| 35393 | 29.07.2020 11:03 | |
| 35394 | 29.07.2020 11:03 | |
| 42886 | 04.09.2020 14:16 | PQ_NEW PRICE REQUEST GMDM |
| 42887 | 04.09.2020 14:16 | PQ_NEW PRICE REQUEST GMDM |
| 42888 | 04.09.2020 14:16 | |
| 42889 | 04.09.2020 14:16 | |
| 42890 | 04.09.2020 14:17 | PQ_COST SWEDEN |
| 42891 | 04.09.2020 14:17 | PQ_COST SWEDEN |
| 42892 | 04.09.2020 14:17 | |
| 42893 | 04.09.2020 14:17 | |
| 42894 | 04.09.2020 14:17 | PQ_NEW PRICE REQUEST GMDM |
| 42895 | 04.09.2020 14:17 | PQ_NEW PRICE REQUEST GMDM |
+-------+------------------+---------------------------+
Example select:
SELECT
start_time
FROM table
WHERE
queue <> 'PQ_NEW PRICE REQUEST GMDM'
AND seq **IS NEXT HIGHER SEQ-VALUE COMPARED TO** (SELECT seq
FROM table
WHERE
queue = 'PQ_NEW PRICE REQUEST GMDM'
AND seq = MIN(seq))
Expected result from table for NEXT HIGHER SEQ-VALUE COMPARED TO:
42890
This would be the next higher number where the condition is met, based on the minimum-sequence number and the condition in the sub-select (34962).
How can I find exactly the next higher sequence-number under certain where-conditions?
Is there even an Oracle-SQL-command? By the way: order by is not an option for the scenario I need it.
SELECT *,
(
select min(seq) from table t2
where t2.seq > t.seq and queue = 'PQ_NEW PRICE REQUEST GMDM'
) as next_seq
FROM table t
WHERE queue <> 'PQ_NEW PRICE REQUEST GMDM';
Try ranking function after calculating the time differences.
Related
So... I am currently using Oracle 11.1g and I need to create a query that uses the ID and CusCODE from Table_with_value and checks Table_with_status using the ID to find active CO_status but on different CusCODE.
This is what I have so far - obviously does not work as it should unless CusCODE and ID are provided manually:
SELECT wm_concat(CoID) as active_CO_Status_for_same_ID_but_different_CusCODE
FROM Table_with_status
WHERE
CoID IN (SELECT CoID FROM Table_with_status WHERE ID = Table_with_value.ID AND CusCODE != Table_with_value.CusCODE)) AND Co_status = 'active';
Table_with_value:
|CoID | CusCODE | ID | Value |
|--------|---------|----------|----|
|354223 | 1.432 | 0784296L | 99 |
|321232 | 4.212321.22 | 0432296L | 32 |
|938421 | 3.213 | 0021321L | 93 |
Table_with_status:
|CoID | CusCODE | ID | Co_status|
|--------|--------------|----------|--------|
|354223 | 1.432 | 0784296L | active|
|354232 | 1.432 | 0784296L | inactive |
|666698 | 1.47621 | 0784296L | active |
|666700 | 1.5217 | 0784296L | active |
|938421 | 3.213 | 0021321L | active |
|938422 | 3.213 | 0021321L | active |
|938423 | 3.213 | 0021321L | active |
|321232 | 4.212321.22 | 0432296L | active |
|321232 | 4.212321.22 | 0432296L | active |
|321232 | 1.689 | 0432296L | inactive |
Expected output:
|CoID | active_CO_Status_for_same_ID_but_different_CusCODE | ID | Value |
|--------|---------|----------|----|
|354223 | 666698,666700 | 1.432 | 0784296L | 99 |
|321232 | N/A | 4.212321.22 | 0432296L | 32 |
|938421 | N/A | 3.213 | 0021321L | 93 |
Any idea on how this can be implemented ideally without any PL/SQL for loops, but it should be fine as well since the output dataset is expected < 300 IDs.
I apologize in advance for the cryptic nature in which I structured the question :) Let me know if something is not clear.
From your description and expected output, it looks like you need a left outer join, something like:
SELECT v.CoID,
wm_concat(s.CoID) as other_active_CusCODE -- active_CO_Status_for_same_ID_but_different_CusCODE
v.CusCODE,
v.ID,
v.value
FROM Table_with_value v
LEFT JOIN Table_with_status s
ON s.ID = v.ID
AND s.CusCODE != v.CusCODE
AND s.Co_status = 'active'
GROUP BY v.CoID, v.CusCODE, v.ID, v.value;
SQL Fiddle using listagg() instead of the never-supported and now-removed wm_concat(); with a couple of different approaches if the logic isn't quite what I interpreted. With your sample data they all get:
COID OTHER_ACTIVE_CUSCODE CUSCODE ID VALUE
------ -------------------- ----------- -------- -----
321232 (null) 4.212321.22 0432296L 32
354223 666698,666700 1.432 0784296L 99
938421 (null) 3.213 0021321L 93
Your code looks like it should work, assuming you are referring to the correct tables:
SELECT wm_concat(s.CoID) as active_CO_Status_for_same_ID_but_different_CusCODE
FROM Table_with_status s
WHERE s.CoID IN (SELECT v.CoID
FROM Table_with_value v
WHERE v.ID = s.ID AND
v.CusCODE <> s.CusCODE
) AND
s.Co_status = 'active';
I have a table containing geological resource information.
| Property | Zone | Area | Category | Tonnage | Au_gt | Au_oz |
|----------|------|-------------|-----------|---------|-------|-------|
| Ket | Eel | Open Pit | Measured | 43400 | 5.52 | 7700 |
| Ket | Eel | Open Pit | Inferred | 51400 | 5.88 | 9700 |
| Ket | Eel | Open Pit | Indicated | 357300 | 6.41 | 73600 |
| Ket | Eel | Underground | Measured | 3300 | 7.16 | 800 |
| Ket | Eel | Underground | Inferred | 14700 | 6.16 | 2900 |
| Ket | Eel | Underground | Indicated | 168100 | 8.85 | 47800 |
I would like to summarize the data so that it can be read more easily by our clients.
| Property | Zone | Category | Open_Pit_Tonnage | Open_Pit_Au_gt | Open_Pit_Au_oz | Underground_tonnage | Underground_au_gt | Underground_au_oz | Combined_tonnage | Combined_au_gt | Combined_au_oz |
|----------|------|-----------|------------------|----------------|----------------|---------------------|-------------------|-------------------|------------------|----------------|----------------|
| Ket | Eel | Measured | 43,400 | 5.52 | 7,700 | 3,300 | 7.16 | 800 | 46,700 | 5.64 | 8,500 |
| Ket | Eel | Indicated | 357,300 | 6.41 | 73,600 | 168,100 | 8.85 | 47,800 | 525,400 | 7.19 | 121,400 |
| Ket | Eel | Inferred | 51,400 | 5.88 | 9,700 | 14,700 | 6.16 | 2,900 | 66,100 | 5.94 | 12,600 |
I'm fairly new to pivot tables. How could I write a query to translate and summarize the data?
Thanks!
If your Oracle version is 11.1 or higher (which it should be if you are a relatively new user!) then you can use the PIVOT operator, as shown below.
Note that the result of the PIVOT operation can be given an alias (I used p) - this makes it easier to write the SELECT clause.
I assumed the name of your table is geological_data - replace it with your actual table name.
select p.*
, open_pit_tonnage + underground_tonnage as combined_tonnage
, open_pit_au_gt + underground_au_gt as combined_au_gt
, open_pit_au_oz + underground_au_oz as combined_au_oz
from geological_data
pivot (sum(tonnage) as tonnage, sum(au_gt) as au_gt, sum(au_oz) as au_oz
for area in ('Open Pit' as open_pit, 'Underground' as underground)) p
;
Conditional aggregation is a simple method:
select Property, Zone, Category,
max(case when area = 'Open Pit' then tonnage end) as open_pit_tonnage,
max(case when area = 'Open Pit' then Au_gt end) as open_pit_Au_gt,
max(case when area = 'Open Pit' then Au_oz end) as open_pit_Au_ox,
max(case when area = 'Underground' then tonnage end) as Underground_tonnage,
max(case when area = 'Underground' then Au_gt end) as Underground_Au_gt,
max(case when area = 'Underground' then Au_oz end) as Underground_Au_ox
from t
group by Property, Zone, Category
SQL Server PIVOT operator is used to convert rows to columns.
Goal is to turn the category names from the first column of the output into multiple columns and count the number of products for each category
This query reference can be taken into account for you above table:
SELECT * FROM
(
SELECT
category_name,
product_id,
model_year
FROM
production.products p
INNER JOIN production.categories c
ON c.category_id = p.category_id
) t
PIVOT(
COUNT(product_id)
FOR category_name IN (
[Children Bicycles],
[Comfort Bicycles],
[Cruisers Bicycles],
[Cyclocross Bicycles],
[Electric Bikes],
[Mountain Bikes],
[Road Bikes])
) AS pivot_table;
Consider the following sample table("Customer") with these records
=========
Customer
=========
-----------------------------------------------------------------------------------------------
| customer-id | att-a | att-b | att-c | att-d | att-e | att-f | att-g | att-h | att-i | att-j |
--------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+
| customer-1 | att-a-7 | att-b-3 | att-c-10 | att-d-10 | att-e-15 | att-f-11 | att-g-2 | att-h-7 | att-i-5 | att-j-14 |
--------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+
| customer-2 | att-a-9 | att-b-7 | att-c-12 | att-d-4 | att-e-10 | att-f-4 | att-g-13 | att-h-4 | att-i-1 | att-j-13 |
--------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+
| customer-3 | att-a-10 | att-b-6 | att-c-1 | att-d-1 | att-e-13 | att-f-12 | att-g-9 | att-h-6 | att-i-7 | tt-j-4 |
--------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+
| customer-19 | att-a-7 | att-b-9 | att-c-13 | att-d-5 | att-e-8 | att-f-5 | att-g-12 | att-h-14 | att-i-13 | att-j-15 |
--------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+-------+
I have these records and many more records dumped into SQL database and wanted to find top 10 similar customer based on the attribute value. For example customer-1 and customer-19 have atleast one column value matching .i.e "att-a-7" so the output should give me 2 customer-id's or top similar customer that are customer-1 and customer-19.
P.S - there can be one or more columns similar across rows.
I'm using windowing technique to find top 10 similar customer and im not sure if I'm correct.
following is my approach I used in my query :
row_number() over (partition by att-a, att-b,..,att-j order by customer-id) as customers
is this correct. ?
I have a table with following stock data where we have couple of columns like date, ticker, open and close(stock prices).
To query this data, I want to know which stock has given the highest margin on particular date. So if I have 516 different stocks, my query should return 516 rows of ticker, date, open, close and a new column Margin(which will be max(close-open)).
| deep_stocks.date_ | deep_stocks.ticker | deep_stocks.open | deep_stocks.close |
+--------------------+---------------------+-------------------+--------------------+--+
| 20100721 | A | 27.68 | 27.58 |
| 20100722 | A | 27.95 | 28.72 |
| 20100723 | A | 28.56 | 29.3 |
| 20100726 | A | 29.22 | 29.64 |
| 20100727 | A | 29.73 | 28.87 |
| 20100728 | A | 28.79 | 28.78 |
| 20100729 | A | 28.97 | 28.15 |
| 20100730 | A | 27.78 | 27.93 |
| 20100802 | A | 28.35 | 28.82 |
| 20100803 | A | 28.7 | 27.84 |
I have written a query where my approach was:
Step 1 - Get the difference between Close and Open prices (Inner/Sub query)
Step 2 - Get the maximum of margin for every stock (used group by with max function)
Step 3 - Join the results with Main Table and get the data.
I'll put my query in solution or comments can someone please correct it as it is taking more time. Also I would like to know can we have any other alternative approach.
As already told about my approach please find below query:
SELECT ds.ticker, ds.date_, ds.close, ds.open, ds.Margin FROM
(SELECT ticker, date_, close, open, case(close-open)>0 when true then round(close-open,2) else 0 end as Margin FROM DataStocks) ds
JOIN
(SELECT dsIn.ticker, max(dsIn.Margin) mxMargin FROM
(select ticker, case(close-open)>0 when true then round(close-open,2) else 0 end as Margin FROM DataStocks ) dsIn group by dsIn.ticker) dsEx
ON ds.ticker=dsEx.ticker AND ds.Margin=dsEx.mxMargin ORDER BY ds.Margin;
Do we have any other alternatives for this query or can it be possible to optimize it.
I have an MS Access database for rainfall data of several climate stations.
For each day of each station, I want to calculate the rainfall in the previous day (if recorded), and the sum of the rainfall at the previous 3 and 7 days.
Due to the huge amount of data and the limitations of Access, I made a query that takes station by station; Then I applied an auxillary query to find dates first, For each station, The following SQL statement is applied (and named RainFallStudy query):
SELECT
[173].ID, [173].AirportCode, [173].RFmm,
DateSerial([rYear], [rMonth], [rDay]) AS DateSer,
[DateSer]-1 AS DM1,
[DateSer]-2 AS DM2,
[DateSer]-3 AS DM3,
[DateSer]-4 AS DM4,
[DateSer]-5 AS DM5,
[DateSer]-6 AS DM6,
[DateSer]-7 AS DM7
FROM
[173]
WHERE
((([173].AirportCode) = 786660));
I used DM1, DM2, etc as the date serial of the day-1, day-2, etc.
Then I used another query that uses RainFallStudy query with left joints as shown in the figure:
The SQL statement is
SELECT
RainFallStudy.ID, RainFallStudy.AirportCode,
RainFallStudy.RFmm AS RF0, RainFallStudy.DateSer,
RainFallStudy.DM1, RainFallStudy_1.RFmm AS RF1,
RainFallStudy_2.RFmm AS RF2, RainFallStudy_3.RFmm AS RF3,
RainFallStudy_4.RFmm AS RF4, RainFallStudy_5.RFmm AS RF5,
RainFallStudy_6.RFmm AS RF6, RainFallStudy_7.RFmm AS RF7,
Nz([rf1], 0) + Nz([rf2], 0) + Nz([rf3], 0) + Nz([rf4], 0) + Nz([rf5], 0) + Nz([rf6], 0) + Nz([rf7], 0) AS RF_W
FROM
((((((RainFallStudy
LEFT JOIN
RainFallStudy AS RainFallStudy_1 ON RainFallStudy.DM1 = RainFallStudy_1.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_2 ON RainFallStudy.DM2 = RainFallStudy_2.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_3 ON RainFallStudy.DM3 = RainFallStudy_3.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_4 ON RainFallStudy.DM4 = RainFallStudy_4.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_5 ON RainFallStudy.DM5 = RainFallStudy_5.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_6 ON RainFallStudy.DM6 = RainFallStudy_6.DateSer)
LEFT JOIN
RainFallStudy AS RainFallStudy_7 ON RainFallStudy.DM7 = RainFallStudy_7.RFmm;
Now I suffer from the slow performance of this query, as the records of each station range from 1,000 to 750,000 records! Is there any better way to find what I need in a faster SQL statement? The second question, can I make a standalone SQL statement for that (one query without the auxiliary query) as I will use it in python, which requires one SQL statement (as Iof my knowledge).
Thanks in advance.
Update
As requested by #Andre, Here are some sample data of table [173] in HTML
<table><tbody><tr><th>ID</th><th>AirportCode</th><th>rYear</th><th>rMonth</th><th>rDay</th><th>RFmm</th></tr><tr><td>11216</td><td>409040</td><td>2012</td><td>1</td><td>23</td><td>0.51</td></tr><tr><td>11217</td><td>409040</td><td>2012</td><td>1</td><td>24</td><td>0</td></tr><tr><td>11218</td><td>409040</td><td>2012</td><td>1</td><td>25</td><td>0</td></tr><tr><td>11219</td><td>409040</td><td>2012</td><td>1</td><td>26</td><td>2.03</td></tr><tr><td>11220</td><td>409040</td><td>2012</td><td>1</td><td>27</td><td>0</td></tr><tr><td>11221</td><td>409040</td><td>2012</td><td>1</td><td>28</td><td>0</td></tr><tr><td>11222</td><td>409040</td><td>2012</td><td>1</td><td>29</td><td>0</td></tr><tr><td>11223</td><td>409040</td><td>2012</td><td>1</td><td>30</td><td>0</td></tr><tr><td>11224</td><td>409040</td><td>2012</td><td>1</td><td>31</td><td>0.25</td></tr><tr><td>11225</td><td>409040</td><td>2012</td><td>2</td><td>1</td><td>0</td></tr><tr><td>11226</td><td>409040</td><td>2012</td><td>2</td><td>2</td><td>0</td></tr><tr><td>11227</td><td>409040</td><td>2012</td><td>2</td><td>3</td><td>4.32</td></tr><tr><td>11228</td><td>409040</td><td>2012</td><td>2</td><td>4</td><td>13.21</td></tr><tr><td>11229</td><td>409040</td><td>2012</td><td>2</td><td>5</td><td>1.02</td></tr><tr><td>11230</td><td>409040</td><td>2012</td><td>2</td><td>6</td><td>0</td></tr><tr><td>11231</td><td>409040</td><td>2012</td><td>2</td><td>7</td><td>0</td></tr><tr><td>11232</td><td>409040</td><td>2012</td><td>2</td><td>8</td><td>0</td></tr><tr><td>11233</td><td>409040</td><td>2012</td><td>2</td><td>9</td><td>0</td></tr><tr><td>11234</td><td>409040</td><td>2012</td><td>2</td><td>10</td><td>5.08</td></tr><tr><td>11235</td><td>409040</td><td>2012</td><td>2</td><td>11</td><td>0</td></tr><tr><td>11236</td><td>409040</td><td>2012</td><td>2</td><td>12</td><td>12.95</td></tr><tr><td>11237</td><td>409040</td><td>2012</td><td>2</td><td>13</td><td>5.59</td></tr><tr><td>11238</td><td>409040</td><td>2012</td><td>2</td><td>14</td><td>0.25</td></tr><tr><td>11239</td><td>409040</td><td>2012</td><td>2</td><td>15</td><td>0</td></tr><tr><td>11240</td><td>409040</td><td>2012</td><td>2</td><td>16</td><td>0</td></tr><tr><td>11241</td><td>409040</td><td>2012</td><td>2</td><td>17</td><td>0</td></tr><tr><td>11242</td><td>409040</td><td>2012</td><td>2</td><td>18</td><td>0</td></tr><tr><td>11243</td><td>409040</td><td>2012</td><td>2</td><td>19</td><td>0</td></tr><tr><td>11244</td><td>409040</td><td>2012</td><td>2</td><td>20</td><td>14.48</td></tr><tr><td>11245</td><td>409040</td><td>2012</td><td>2</td><td>21</td><td>9.65</td></tr><tr><td>11246</td><td>409040</td><td>2012</td><td>2</td><td>22</td><td>3.05</td></tr><tr><td>11247</td><td>409040</td><td>2012</td><td>2</td><td>23</td><td>0</td></tr><tr><td>11248</td><td>409040</td><td>2012</td><td>2</td><td>24</td><td>0</td></tr><tr><td>11249</td><td>409040</td><td>2012</td><td>2</td><td>25</td><td>0</td></tr><tr><td>11250</td><td>409040</td><td>2012</td><td>2</td><td>26</td><td>0</td></tr><tr><td>11251</td><td>409040</td><td>2012</td><td>2</td><td>27</td><td>0</td></tr><tr><td>11252</td><td>409040</td><td>2012</td><td>2</td><td>28</td><td>7.37</td></tr><tr><td>11253</td><td>409040</td><td>2012</td><td>2</td><td>29</td><td>0</td></tr></tbody></table>
And here is sample output (HTML)
<table><tbody><tr><th>ID</th><th>AirportCode</th><th>DateSer</th><th>ThisDay</th><th>Yesterday</th><th>Prev3days</th><th>PrevWeek</th></tr><tr><td>11216</td><td>409040</td><td>23-01-2012</td><td>0.51</td><td>0</td><td>0</td><td>0</td></tr><tr><td>11217</td><td>409040</td><td>24-01-2012</td><td>0</td><td>0.51</td><td>0.51</td><td>0.51</td></tr><tr><td>11218</td><td>409040</td><td>25-01-2012</td><td>0</td><td>0</td><td>0.51</td><td>0.51</td></tr><tr><td>11219</td><td>409040</td><td>26-01-2012</td><td>2.03</td><td>0</td><td>0.51</td><td>0.51</td></tr><tr><td>11220</td><td>409040</td><td>27-01-2012</td><td>0</td><td>2.03</td><td>2.03</td><td>2.54</td></tr><tr><td>11221</td><td>409040</td><td>28-01-2012</td><td>0</td><td>0</td><td>2.03</td><td>2.54</td></tr><tr><td>11222</td><td>409040</td><td>29-01-2012</td><td>0</td><td>0</td><td>2.03</td><td>2.54</td></tr><tr><td>11223</td><td>409040</td><td>30-01-2012</td><td>0</td><td>0</td><td>0</td><td>2.54</td></tr><tr><td>11224</td><td>409040</td><td>31-01-2012</td><td>0.25</td><td>0</td><td>0</td><td>2.03</td></tr><tr><td>11225</td><td>409040</td><td>01-02-2012</td><td>0</td><td>0.25</td><td>0.25</td><td>2.28</td></tr><tr><td>11226</td><td>409040</td><td>02-02-2012</td><td>0</td><td>0</td><td>0.25</td><td>2.28</td></tr><tr><td>11227</td><td>409040</td><td>03-02-2012</td><td>4.32</td><td>0</td><td>0.25</td><td>0.25</td></tr><tr><td>11228</td><td>409040</td><td>04-02-2012</td><td>13.21</td><td>4.32</td><td>4.32</td><td>4.57</td></tr><tr><td>11229</td><td>409040</td><td>05-02-2012</td><td>1.02</td><td>13.21</td><td>17.53</td><td>17.78</td></tr><tr><td>11230</td><td>409040</td><td>06-02-2012</td><td>0</td><td>1.02</td><td>18.55</td><td>18.8</td></tr><tr><td>11231</td><td>409040</td><td>07-02-2012</td><td>0</td><td>0</td><td>14.23</td><td>18.8</td></tr><tr><td>11232</td><td>409040</td><td>08-02-2012</td><td>0</td><td>0</td><td>1.02</td><td>18.55</td></tr><tr><td>11233</td><td>409040</td><td>09-02-2012</td><td>0</td><td>0</td><td>0</td><td>18.55</td></tr><tr><td>11234</td><td>409040</td><td>10-02-2012</td><td>5.08</td><td>0</td><td>0</td><td>18.55</td></tr><tr><td>11235</td><td>409040</td><td>11-02-2012</td><td>0</td><td>5.08</td><td>5.08</td><td>19.31</td></tr><tr><td>11236</td><td>409040</td><td>12-02-2012</td><td>12.95</td><td>0</td><td>5.08</td><td>6.1</td></tr><tr><td>11237</td><td>409040</td><td>13-02-2012</td><td>5.59</td><td>12.95</td><td>18.03</td><td>18.03</td></tr><tr><td>11238</td><td>409040</td><td>14-02-2012</td><td>0.25</td><td>5.59</td><td>18.54</td><td>23.62</td></tr><tr><td>11239</td><td>409040</td><td>15-02-2012</td><td>0</td><td>0.25</td><td>18.79</td><td>23.87</td></tr><tr><td>11240</td><td>409040</td><td>16-02-2012</td><td>0</td><td>0</td><td>5.84</td><td>23.87</td></tr><tr><td>11241</td><td>409040</td><td>17-02-2012</td><td>0</td><td>0</td><td>0.25</td><td>23.87</td></tr><tr><td>11242</td><td>409040</td><td>18-02-2012</td><td>0</td><td>0</td><td>0</td><td>18.79</td></tr><tr><td>11243</td><td>409040</td><td>19-02-2012</td><td>0</td><td>0</td><td>0</td><td>18.79</td></tr><tr><td>11244</td><td>409040</td><td>20-02-2012</td><td>14.48</td><td>0</td><td>0</td><td>5.84</td></tr><tr><td>11245</td><td>409040</td><td>21-02-2012</td><td>9.65</td><td>14.48</td><td>14.48</td><td>14.73</td></tr><tr><td>11246</td><td>409040</td><td>22-02-2012</td><td>3.05</td><td>9.65</td><td>24.13</td><td>24.13</td></tr><tr><td>11247</td><td>409040</td><td>23-02-2012</td><td>0</td><td>3.05</td><td>27.18</td><td>27.18</td></tr><tr><td>11248</td><td>409040</td><td>24-02-2012</td><td>0</td><td>0</td><td>12.7</td><td>27.18</td></tr><tr><td>11249</td><td>409040</td><td>25-02-2012</td><td>0</td><td>0</td><td>3.05</td><td>27.18</td></tr><tr><td>11250</td><td>409040</td><td>26-02-2012</td><td>0</td><td>0</td><td>0</td><td>27.18</td></tr><tr><td>11251</td><td>409040</td><td>27-02-2012</td><td>0</td><td>0</td><td>0</td><td>27.18</td></tr><tr><td>11252</td><td>409040</td><td>28-02-2012</td><td>7.37</td><td>0</td><td>0</td><td>12.7</td></tr><tr><td>11253</td><td>409040</td><td>29-02-2012</td><td>0</td><td>7.37</td><td>7.37</td><td>10.42</td></tr></tbody></table>
I created an additional column rDate (DateTime) and filled it with this query:
UPDATE Rainfall SET Rainfall.rDate = DateSerial([rYear],[rMonth],[rDay]);
Then your desired result can be achieved with several subqueries, using SUM() for the last two columns:
SELECT r.ID, r.AirportCode, r.rDate, r.RFmm,
(SELECT RFmm FROM Rainfall r1 WHERE r1.AirportCode = r.AirportCode AND r1.rDate = r.rDate-1) AS Yesterday,
(SELECT SUM(RFmm) FROM Rainfall r3 WHERE r3.AirportCode = r.AirportCode AND r3.rDate BETWEEN r.rDate-3 AND r.rDate-1) AS Prev3days,
(SELECT SUM(RFmm) FROM Rainfall r7 WHERE r7.AirportCode = r.AirportCode AND r7.rDate BETWEEN r.rDate-7 AND r.rDate-1) AS PrevWeek
FROM Rainfall r
Make sure AirportCode and rDate are indexed for larger numbers of records.
Result:
+-------+-------------+------------+-------+-----------+-----------+----------+
| ID | AirportCode | rDate | RFmm | Yesterday | Prev3days | PrevWeek |
+-------+-------------+------------+-------+-----------+-----------+----------+
| 11216 | 409040 | 23.01.2012 | 0,51 | | | |
| 11217 | 409040 | 24.01.2012 | 0 | 0,51 | 0,51 | 0,51 |
| 11218 | 409040 | 25.01.2012 | 0 | 0 | 0,51 | 0,51 |
| 11219 | 409040 | 26.01.2012 | 2,03 | 0 | 0,51 | 0,51 |
| 11220 | 409040 | 27.01.2012 | 0 | 2,03 | 2,03 | 2,54 |
| 11221 | 409040 | 28.01.2012 | 0 | 0 | 2,03 | 2,54 |
| 11222 | 409040 | 29.01.2012 | 0 | 0 | 2,03 | 2,54 |
| 11223 | 409040 | 30.01.2012 | 0 | 0 | 0 | 2,54 |
| 11224 | 409040 | 31.01.2012 | 0,25 | 0 | 0 | 2,03 |
| 11225 | 409040 | 01.02.2012 | 0 | 0,25 | 0,25 | 2,28 |
| 11226 | 409040 | 02.02.2012 | 0 | 0 | 0,25 | 2,28 |
| 11227 | 409040 | 03.02.2012 | 4,32 | 0 | 0,25 | 0,25 |
| 11228 | 409040 | 04.02.2012 | 13,21 | 4,32 | 4,32 | 4,57 |
| 11229 | 409040 | 05.02.2012 | 1,02 | 13,21 | 17,53 | 17,78 |
+-------+-------------+------------+-------+-----------+-----------+----------+
Use Nz() to avoid NULL values in the first row.
It appears that you store the day in separate fields (rYear, rMonth, rDay). So, in order to get the date you use the DateSerial function. This means that in order to use the date for a join or where clause, Access must calculate the date for the entire table. You need to store the date in a separate field and index it to avoid the calculation.