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'm hoping that im over thinking this. but i need to sum a column where i have no unique link to join on and when i do it double ups columns.
This is my current SQL that works until i add the join on vwBatchInData then it doubles up every record, what is the best way to achieve this?
select b.fldBatchID as 'ID',SUM(bIn.fldBatchDetailsWeight) as 'Batch In', sum(t.fldTransactionNetWeight) as 'Batch Out' , format((sum(t.fldTransactionNetWeight) / sum(bIn.fldBatchDetailsWeight)),'P2' ) as 'Yield'
from [TRANSACTION] t
right join vwBatchInData bIn on bIn.fldBatchID = t.fldBatchID
inner join Batch b on b.fldBatchID = t.fldBatchID
where CAST(b.fldBatchDate as date) = '2020-03-04'
group by b.fldBatchID**
vwBatchInData Table
+------------+---------------+-----------------------+
| fldBatchID | fldKillNumber | fldBatchDetailsWeight |
+------------+---------------+-----------------------+
| 2862 | 601598 | 164.40 |
| 2862 | 601599 | 190.80 |
| 2862 | 601596 | 195.00 |
| 2862 | 601597 | 200.20 |
| 2862 | 601594 | 176.60 |
+------------+---------------+-----------------------+
Transaction Table
+------------+------------------+-------------------------+
| fldBatchID | fldTransactionID | fldTransactionNetWeight |
+------------+------------------+-------------------------+
| 2862 | 10242352 | 16.26 |
| 2862 | 10242353 | 22.82 |
| 2862 | 10242362 | 18.52 |
| 2862 | 10242363 | 21.44 |
| 2862 | 10242364 | 20.32 |
+------------+------------------+-------------------------+
Batch Table
+------------+-------------------------+
| fldBatchID | fldBatchDate |
+------------+-------------------------+
| 2862 | 2020-03-04 00:00:00.000 |
+------------+-------------------------+
Desired output with the above snipets
+------+----------+-----------+---------+
| ID | Batch In | Batch Out | Yield |
+------+----------+-----------+---------+
| 2862 | 927.00 | 90.36 | 10.76 % |
+------+----------+-----------+---------+
I think you just want to aggregate before joining:
select b.fldBatchID as ID,
(bIn.fldBatchDetailsWeight) as batch_in,
(t.fldTransactionNetWeight) as batch_out,
format(t.fldTransactionNetWeight / bIn.fldBatchDetailsWeight, 'P2' ) as Yield
from batch b left join
(select bin.fldBatchID, sum(fldBatchDetailsWeight) as fldBatchDetailsWeight
from vwBatchInData bin
group by bin.fldBatchID
) bin
on bIn.fldBatchID = b.fldBatchID left join
(select t.fldBatchID, sum(fldTransactionNetWeight) as fldTransactionNetWeight
from transactions t
group by t.fldBatchID
) bin
on t.fldBatchID = b.fldBatchID
where CAST(b.fldBatchDate as date) = '2020-03-04';
I have three different tables here:
df_umts_relation table:
|---------------------|------------------|---------------------|------------------|------------------|
| cell_name | n_cell_name | technology | source_ops_num | target_ops_num |
|---------------------|------------------|---------------------|------------------|------------------|
| 121 | 221 | UMTS | 1 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 122 | 222 | GSM | 2 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 123 | 223 | UMTS | 3 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 124 | 224 | GSM | 4 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 125 | 225 | GSM | 5 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 126 | 226 | UMTS | 6 | | |
|---------------------|------------------|---------------------|------------------|------------------|
| 127 | 227 | UMTS | 7 | | |
|---------------------|------------------|---------------------|------------------|------------------|
So now I want to update target_ops_num from the two below tables
df_umts_carrier table as this table contains those thow columns I want to work on them and contains some integer values also:
|---------------------|------------------|
| opsnum_umts | cell_name_umts |
|---------------------|------------------|
as I have another table called df_gsm_carrier:
|---------------------|------------------|
| opsnum_gsm | cellname |
|---------------------|------------------|
So All I need I want to update [MyNewDatabase].[dbo].[df_umts_relation].[target_ops_num] CASE WHEN technologyis UMTS then update from table df_umts_carrier ELSE technology is GSM then update from df_gsm_carrier on n_cell_name = cell_name_umts and on n_cell_name = cellname
So I tried to create a query as the below one works with one condition only and it's update the the rows which is UMTS only:
UPDATE [MyNewDatabase].[dbo].[df_umts_relation]
SET [MyNewDatabase].[dbo].[df_umts_relation].[target_ops_num] = [MyNewDatabase].[dbo].[df_umts_carrier].[opsnum_umts]
FROM [MyNewDatabase].[dbo].[df_umts_relation]
INNER JOIN [MyNewDatabase].[dbo].[df_umts_carrier]
ON [n_cell_name] = [cell_name_umts]
and works fine but doesn't update the rows which contains GSM...
On other way I tried to create a query to handle this but it didn't update the GSM part and take a long of time:
UPDATE [MyNewDatabase].[dbo].[df_umts_relation]
SET [MyNewDatabase].[dbo].[df_umts_relation].[target_ops_num] = (CASE WHEN [MyNewDatabase].[dbo].[df_umts_relation].[technology] = 'UMTS'
THEN [MyNewDatabase].[dbo].[df_umts_carrier].[opsnum_umts] ELSE [MyNewDatabase].[dbo].[df_gsm_carrier].[opsnum_gsm] END)
FROM [MyNewDatabase].[dbo].[df_umts_relation]
LEFT JOIN [MyNewDatabase].[dbo].[df_umts_carrier]
ON [n_cell_name] = [cell_name_umts]
LEFT JOIN [MyNewDatabase].[dbo].[df_gsm_carrier]
ON [n_cell_name] = [cell_name]
So any one have any idea how to solve this?
Please check if this will help.
update df_umts_relation
set target_ops_num = ( select case when dur.technology = 'UMTS' then du.cell_name_umts
when dur.technology = 'GSM' then dg.cellname
end
from df_umts_relation dur
left join df_umts_carrier du on dur.n_cell_name = du.opsnum_umts
left join df_gsm_carrier dg on dur.n_cell_name = dg.opsnum_umts
where dur.id= df_umts_relation.id)
Here is a demo
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.
I have these two tables:
I need to join the payment table with the discount table. The expected output seems not possible since the discounts table doesn't have a payment date. I can only get the net_amount
payment table:
id | net_amount | payment_dt | person_id
1001 | 2765.36 | 2016-05-28 | 372
1002 | 2474.76 | 2016-05-29 | 372
1003 | 22694.25 | 2016-05-29 | 384
1004 | 1911.92 | 2016-05-29 | 384
discounts table:
id | person_id | gross_amount | sc_discount | other_discount_amount | other_discount_type
1 | 372 | 3566.7 | 713.34 | 88.00 | MISC
2 | 372 | 3202.2 | 640.44 | 87.00 | PAT
3 | 384 | 3566.7 | 713.34 | 285.34 | MISC
4 | 384 | 27953.10 | 5590.62 | 2236.25 | PAT
5 | 384 | 2655.45 | 531.09 | 212.44 | MISC
*1 - payment_dt is 2016-05-28
expected output: (where payment_dt=2016-05-29)
total_gross_amount | total_sc_discount | total_misc_discount | total_pat_discount | total_net_amount
37,377.45 | 7475.49 | 497.78 | 2,323.25 | 27,080.93
As I see in both tables common column is person_id, you can try to join with it. And for more information you can have in mind natural joins, read about it on The Internet ;)
"A NATURAL JOIN is a JOIN operation that creates an implicit join clause for you based on the common columns in the two tables being joined. Common columns are columns that have the same name in both tables. A NATURAL JOIN can be an INNER join, a LEFT OUTER join, or a RIGHT OUTER join. The default is INNER join. "
By assuming :
net_amount = gross_amount - sc_discount - other_discount_amount
You do not need to go to payments table for total_net_amount in the expected output
You can write it like this :
Select sum(gross_amount) as total_gross_amount,
sum(sc_discount) as total_sc_discount,
sum(gross_amount - sc_discount - other_discount_amount) as total_net_amount
sum(CASE other_discount_type when 'MISC' THEN other_discount_amount WHEN 'PAT' THEN 0) as total_misc_discount,
sum(CASE other_discount_type when 'MISC' THEN 0 WHEN 'PAT' THEN other_discount_amount) as total_pat_discount
from discounts
And in case the above assumption is not true, As the aggregation is complete, only 1 row will come in output, you can get all columns except total_net_amount as in above query, get sum(net_amount) from payments table, and join them on true as only 1,1 row.