Given the following table:
| A | B | holdingtemp1 | holdingTemp2 | holdingtemp3 | holdingTemp4 | idx |
| Grits| 7:06:24 AM | 180.199 | - | - | - | 0 |
| | 8:35:56 AM | - | 174.30 | - | - | 1 |
| | 9:25:18 AM | - | - | 182 | - | 2 |
| | 10:02:0 AM | - | - | - | 180 | 3 |
How to properly index column B to perform field comparisons in order to implement conditional logic where each temperature recording must be taken within a two hour interval relative to the previous recording, such that:
if(B[0] + 2 > B[1], green(), red()) // 9:06 > 8:35, turns cell green
if(B[1] + 2 > B[2], green(), red()) // 10:35 > 9:25, turns cell green
Edit:
Found the right function to use for this implementation, but peak() is returning 4:43:21 AM for all records with
Peek('B', 0) as peekB, // 4:43:21 AM
and 5:11:03 AM with
Peek('B', 1) as peekB, // 5:11:03 AM
If so, does peek() fetch the field value of the preloaded or postloaded data?
I am working on a PowerBI report that is grabbing information from SQL and I cannot find a way to solve my problem using PowerBI or how to write the required code. My first table, Certifications, includes a list of certifications and required trainings that must be obtained in order to have an active certification.
My second table, UserCertifications, includes a list of UserIDs, certifications, and the trainings associated with a certification.
How can I write a SQL code or PowerBI measure to tell if a user has all required trainings for a certification? ie, if UserID 1 has the A certification, how can I verify that they have the TrainingIDs of 1, 10, and 150 associated with it?
Certifications:
CertificationsTable
UserCertifications:
UserCertificationsTable
This is a DAX pattern to test if contains at least some values.
| Certifications |
|----------------|------------|
| Certification | TrainingID |
|----------------|------------|
| A | 1 |
| A | 10 |
| A | 150 |
| B | 7 |
| B | 9 |
| UserCertifications |
|--------------------|---------------|----------|
| UserID | Certification | Training |
|--------------------|---------------|----------|
| 1 | A | 1 |
| 1 | A | 10 |
| 1 | A | 300 |
| 2 | A | 150 |
| 2 | B | 9 |
| 2 | B | 90 |
| 3 | A | 7 |
| 4 | A | 1 |
| 4 | A | 10 |
| 4 | A | 150 |
| 4 | A | 1000 |
In the above scenario, DAX needs to find out if the mandatory trainings (Certifications[TrainingID]) by Certifications[Certification] is completed by
UserCertifications[UserID ]&&UserCertifications[Certifications] partition.
In the above scenario, DAX should only return true for UserCertifications[UserID ]=4 as it is the only User that completed at least all the mandatory trainings.
The way to achieve this is through the following measure
areAllMandatoryTrainingCompleted =
VAR _alreadyCompleted =
CONCATENATEX (
UserCertifications,
UserCertifications[Training],
"-",
UserCertifications[Training]
) // what is completed in the fact Table; the fourth argument is very important as it decides the sort order
VAR _0 =
MAX ( UserCertifications[Certification] )
VAR _supposedToComplete =
CONCATENATEX (
FILTER ( Certifications, Certifications[Certification] = _0 ),
Certifications[TrainingID],
"-",
Certifications[TrainingID]
) // what is comeleted in the training Table; the fourth argument is very important as it decides the sort order
VAR _isMandatoryTrainingCompleted =
CONTAINSSTRING ( _alreadyCompleted, _supposedToComplete ) // CONTAINSSTRING (<Within Text>,<Search Text>); return true false
RETURN
_isMandatoryTrainingCompleted
I try to create a json select query which can give me back the result on next way.
1 row contains 1 main_message_id and belonging messages. (Like the bottom image.) The json format is not a requirement, if its work with other methods, it will be fine.
I store the data as like this:
+-----------------+---------+----------------+
| main_message_id | message | sub_message_id |
+-----------------+---------+----------------+
| 1 | test 1 | 1 |
| 1 | test 2 | 2 |
| 1 | test 3 | 3 |
| 2 | test 4 | 4 |
| 2 | test 5 | 5 |
| 3 | test 6 | 6 |
+-----------------+---------+----------------+
I would like to create a query, which give me back the data as like this:
+-----------------+-----------------------+--+
| main_message_id | message | |
+-----------------+-----------------------+--+
| 1 | {test1}{test2}{test3} | |
| 2 | {test4}{test5}{test6} | |
| 3 | {test7}{test8}{test9} | |
+-----------------+-----------------------+--+
You can use json_agg() for that:
select main_message_id, json_agg(message) as messages
from the_table
group by main_message_id;
Note that {test1}{test2}{test3} is invalid JSON, the above will return a valid JSON array e.g. ["test1", "test2", "test3"]
If you just want a comma separated list, use string_agg();
select main_message_id, string_ag(message, ', ') as messages
from the_table
group by main_message_id;
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 a report in reporting services. In this report, I am displaying the Top N values. But my Grand Total is displaying the sum of all the values.
Right now I am getting something like this.Here N = 2
+-------+------+-------------+
| Area |ID | Count |
+-------+------+-------------+
| - A | | 4 |
| | a1 | 1 |
| | b1 | 1 |
| | c1 | 1 |
| | d1 | 1 |
| | | |
| - B | | 3 |
| | a2 | 1 |
| | b2 | 1 |
| | c2 | 1 |
| | | |
|Grand | | 10 |
|Total | | |
+-------+------+-------------+
The correct Grand Total should be 7 instead of 10. A and B are toggle items(You can expand and contract)
How can I display the correct Grand Total using Top N filter?
I also want to use the filter in the report and not in the SQL query.
You should use the filter on the Dataset. Filtering the report object itself only turns off the items (rows, for example) visibility. The item / row itself will still be part of the group and will be used for calculations.
I found a way to solve my question. As Ido said I worked on the dataset. I am using Analysis Cube. So in this cube I created a Named Set Calculation.
In this set I used the TopCount() function. It filters out the TOP N values where N can be integer according to your choice.
So the final Named Set in this case is :-
TopCount([Dim Area].[Area].[Area], 2, ([Measures].[Count]))
This will give you Grand total of Top N filtered values.