Currently we are having 6 columns in our database table which we are showing in the SSRS report but in future if we add 1 more column then without any manual changes on RDL it will included in the report.
Current report Example :-
Name Address Code City County Country
xyz Lane 1 466001 Bang dbc Africa
abc Lane 2 466002 Bpl bbn Nepal
dcb Lane 3 466003 sbc wad Bhutan
Expected report without adding the column manually in SSRS.
Name Address Code City County Country DOB
xyz Lane 1 466001 Bang dbc Africa 19/06/1986
abc Lane 2 466002 Bpl bbn Nepal 20/06/1990
dcb Lane 3 466003 sbc wad Bhutan 21/8/2000
Thanks for any help.
Please follow below steps..
Step 1. Create Proc using UNPIVOT and Property(ColunName) & Value with ID column (PKey) like
SELECT Pkey,tblPivot.Property, tblPivot.Value
FROM (SELECT EmpNo AS Pkey, CONVERT(sql_variant,EmpNo) AS EmpNo, CONVERT(sql_variant,EName) AS EName, CONVERT(sql_variant,JOB) AS JOB,
CONVERT(sql_variant,Sal) AS Sal FROM EMP) EMP
UNPIVOT (Value For Property In (EmpNo,EName, JOB, Sal)) as tblPivot
Step 2.
Create a Matrix Report using above SP with row-grouping on [Pkey] and col-grouping on [Property] and Display value ...
Step 3 Now you can add/remove column in SP (step 1) based on your requirement
Related
I have a table named sales in a MySQL database that looks like this:
company manufactured shipped
Mercedes Germany United States
Mercedes Germany Germany
Mercedes Germany United States
Toyota Japan Canada
Toyota Japan England
Audi Germany United States
Audi Germany France
Audi Germany Canada
Tesla United States Mexico
Tesla United States Canada
Tesla United States United States
Here is a Fiddle: http://www.sqlfiddle.com/#!17/145ff/3
I would like to return the list of companies that ship ALL of their products internationally (that is, where the value in the manufactured column differs from the value in the shipped column for ALL records of a particular company).
Using the example above, the desired result set would be:
company
Toyota
Audi
Here is my (hackish) attempt:
WITH temp_table AS (
SELECT
s.company AS company
, SUM(CASE
WHEN s.manufactured != s.shipped THEN 1
ELSE 0
END
) AS count_international
, COUNT(s.company) AS total_within_company
FROM
sales s
GROUP BY
s.company
)
SELECT
company
FROM
temp_table
WHERE count_international = total_within_company
Essentially, I count the instances where the columns do not match. Then I check whether the sum of those mismatched instances matches the number of records within a given group.
This approach works, but it's far from an elegant solution!
Can anyone offer advice as to a more idiomatic way to implement this query?
Thanks!
We can GROUP BY company and use a HAVING clause to say all countries in shipped must differ to the country in manufactured:
SELECT company
FROM sales
GROUP BY company
HAVING COUNT(CASE WHEN manufactured = shipped THEN 1 END) = 0;
Try out here: db<>fiddle
The fiddle linked in the question is a Postgres DB, but MySQL is taged as DBMS.
In a MySQL DB, the above query can be simplified to:
SELECT company
FROM sales
GROUP BY company
HAVING SUM(manufactured = shipped) = 0;
In a Postgres DB, this is not possible.
You have to think in sets... you want to display all without a match -- find the matches display the rest
SELECT DISTINCT company
FROM sales
WHERE company NOT IN (
SELECT company
FROM sales
WHERE manufactured = shipped
)
I have two tables in MS Access, one with foods and associated companies:
FoodID
Food
Company
1
Apple
Vino Farms
2
Orange
Citrus Co.
3
Banana
Vino Farms
and one with information about whether someone ate the food
ClientID
ClientName
Apple
Orange
Banana
1
Bob
Yes
No
Yes
2
Tyler
Yes
Yes
Yes
3
Joe
No
No
No
I'd like to write a query that creates a column populated by the company that makes the foods someone reported eating, separated by commas. If someone reports eating a more than one food made by the same company, I only want the company's name listed once:
ClientID
ClientName
AssociatedCompanies
1
Bob
Vino Farms
2
Tyler
Vino Farms, Citrus Co.
3
Joe
Any help would be greatly appreciated!
As indicated by Gustav, start by normalizing your "ate food" Table. It should look like:
1 Bob Apple
1 Bob Banana
2 Tyler Apple
2 Tyler Orange
2 Tyler Banana
You then get what you want with the following Query:
-- Group companies into one field, sepparated with commas
SELECT ClientID, ClientName, RemoveCommas(Company1 & ", " & Company2 & ", " ... & CompanyN)
FROM
( -- Convert rows (records) into columns (fields)
SELECT ClientID, ClientName
, Max(Iif(Company="Vino Farms", "Vino Farms", Null)) AS Company1
, Max(Iif(Company="Citrus Co.", "Citrus Co.", Null)) AS Company2
, ...
, Max(Iif(Company="Last_company", "Last_company", Null)) AS CompanyN
FROM
(-- Replace food by its manufacturing company
SELECT DISTINCT ClientID, ClientName, Company
FROM
Table_ate_food_normalized AS Ate
INNER JOIN
Table_food AS Comp
ON Ate.Food = Comp.Food
)
GROUP BY ClientID, ClientName
)
Notice the following relevant points:
This will only work, as you notice, for a fixed number of companies with names hardcoded in the Query. This is a very severe limitation. If these restrictions are not satisfied, I suggest that you use a TRANSFORM Query, but, you would not get the companies as a single text field with values sepparated by commas, and you would rather get them as a variable number of fields, each field having a Yes/No value (similar to your current "ate food" table).
I am assuming that the values of "food" and "Company" are each a candidate key in its Table. Otherwise, use the corresponding ID fields, and get the actual values using an inner join.
If you want to understand better the part of converting rows into columns, you may check the Query "K_rows_into_columns_1" from the database of examples dowloadable from LightningGuide.net.
You have to code the user defined VBA function "RemoveCommas()" to remove unncessary commas from the string containing the listing of companies. If unncessesary commas do not bother you, then you can do the Query without coding this functions.
If you want to code the TRANSFORM alternative that I suggested above, you may check the Query "K_rows_into_columns_2" from the database of examples dowloadable from LightningGuide.net.
Hello Stack overflow (and anyone googling similar questions in the future)!
I have a dataset that regularly reports which products are absent on a warehouse stockcheck, which I am trying to use to analyse when stock is or isn’t available. I’m essentially trying to identify “Has a part been reported as missing? -> If so, count the number of days it is missing until another part in the same category is reported as missing, but the original part was not reported as missing on that date (as we can assume it’s back in stock)”.
I’ve managed to make this work in excel, but my spreadsheet began to die from the calculation of 5 locations worth of categories and parts, let alone across the 600+ I’m working on! As a result, I’m trying to set up a similar function in Access to analyse which, and for how long, parts were out of stock.
My dataset looks something like:
Location number
Location
Category
Date reported
Part Number
Part Description
Order number
1
London
Car
03/06/2021
2021
Wheel A
1
2
London
Bus
03/06/2021
1491
Seat C
2
3
Manchester
Car
01/06/2021
2021
Wheel A
3
My assumptions are that:-
• My data is fed by individual workers who each cover a location, and check all stock for a random selection of categories each visit (with the idea that they cover all of their location’s categories within a certain number of visits) and record which parts are missing. There is no particular visit plan – it can be a random number of days between each visit. This data gets fed into a central table, which I have access to.
• As my workers may not check all categories in a location on each visit, I must assume that a previously reported missing part is OOS until they check products in the same category, but do not report that part again.
I made this work on excel by setting up another column that concatenated my location, part number, and date reported, and then set up three tables (all of which are essentially locations, categories, and parts down my X axis, and dates across the Y axis):-
• Table1, to look if my concatenated code was reported for each day (and if so, output 1 – essentially working in days) – essentially, was each part reported as missing for each category and location?
• Table2, to look if any parts were reported for each category, for each location – essentially, how many parts were reported for each category for each location, and a value greater than 0 means we can assume that that category at that location has been checked by my workers for that date.
• Table3, that for each location+category+day asked as a formula – IF(category was checked as per table2 = yes , pull the value of 1 for that part/location/category in table 1 , re-use yesterday’s value for this part/location/day in this table). For the 1st day in my date range, I used the values for table1 for that day as a “starting up” point.
When I look at table 3, I can visually the run of days products were out of stock, and can from there crunch numbers related to that, which is what I want!
My initial Access plan was to set up three crosstab queries, to mirror my three excel tables. I can make Table1 and Table2 very easily, but for the life of me can’t make table3 work (currently have a calculated expression that mirrors the formula I had in table 3, but something has gone amiss…).
I’m looking for a steer/advice on setting up the expression in my crosstab query, or other ideas/approaches I could use to calculate how long each part is missing for. Any help would be greatly appreciated, as I’ve lost my mind going in circles today!
Edit:-
Simplified dataset I'm working with:-
Location
Category
Date Reported
Part number
Part Description
Order number
Concatenate code
Concatenate Code 2
1
London
Car
03/06/2021
2021
Wheel
1
1443502021
1
London
Bus
03/06/2021
1491
Seat
2
1443501491
2
Manchester
Car
05/06/2021
2021
Wheel
3
2443522021
1
London
Car
05/06/2021
2021
Wheel
4
1443522021
1
London
Car
07/06/2021
2021
Wheel
5
1443542021
1
London
Bus
05/06/2021
1860
Seatbelt
6
1443521860
1
London
Bus
05/06/2021
1860
Seatbelt
7
1443521860
2
manchester
Bus
01/06/2021
1860
Seatbelt
8
2443481860
2
Manchester
Bus
06/06/2021
1860
Seatbelt
9
2443531860
2
manchester
Bus
04/06/2021
1491
Seat
10
2443511491
2
Manchester
Bus
06/06/2021
1491
Seat
11
2443531491
I'm trying to output something like (which I've made work in Excel):-
Location
Category
Part code
01/06/2021
02/06/2021
03/06/2021
04/06/2021
05/06/2021
06/06/2021
07/06/2021
1
London
Car
2021
1
1
1
1
1
London
Car
2626
1
London
Bus
1491
1
1
1
London
Bus
1860
1
1
2
Manchester
Car
2021
1
1
2
Manchester
Car
2626
2
Manchester
Bus
1491
1
1
1
2
Manchester
Bus
1860
1
1
1
1
3
Liverpool
Car
2021
3
Liverpool
Car
2626
3
Liverpool
Bus
1491
Or to return the value for how many concurrent days out of stock a part has been, like per day of this version:-
Location
Category
Part code
01/06/2021
02/06/2021
03/06/2021
04/06/2021
05/06/2021
06/06/2021
07/06/2021
1
London
Car
2021
1
2
3
4
1
London
Car
2626
1
London
Bus
1491
1
2
1
London
Bus
1860
1
2
2
Manchester
Car
2021
1
2
2
Manchester
Car
2626
2
Manchester
Bus
1491
1
2
3
2
Manchester
Bus
1860
1
2
3
1
3
Liverpool
Car
2021
3
Liverpool
Car
2626
3
Liverpool
Bus
1491
My Access sql (that I then turned into a crosstab) to identify ordered parts per day:
SELECT DISTINCT T_stores.[Store Nos], T_stores.[Store Name], t_Stands.Brand, t_Productlookup.TPND, t_Productlookup.TITLE, t_gapdata.Quantity, t_gapdata.[Requested Date]
FROM ((T_stores
INNER JOIN t_Stands ON T_stores.[Store Nos] = t_Stands.[Store Nos])
INNER JOIN t_gapdata ON (t_Stands.[Brand] = t_gapdata.[Brand]) AND (t_Stands.[Store Nos] = t_gapdata.[Store No]))
INNER JOIN t_Productlookup ON t_gapdata.[Part Number] = t_Productlookup.[EAN];
And likewise, to identfy is parts were ordered for a location's category:-
SELECT DISTINCT T_stores.[Store Nos], T_stores.[Store Name], t_Stands.Brand, t_Productlookup.TPND, t_Productlookup.TITLE, t_gapdata.Quantity, t_gapdata.[Requested Date]
FROM ((T_stores
INNER JOIN t_Stands ON T_stores.[Store Nos] = t_Stands.[Store Nos])
INNER JOIN t_gapdata ON (t_Stands.[Brand] = t_gapdata.[Brand]) AND (t_Stands.[Store Nos] = t_gapdata.[Store No]))
INNER JOIN t_Productlookup ON t_gapdata.[Part Number] = t_Productlookup.[EAN];
These first two work fine, but I'm struggling to put them together with some sort of Iif calculated field for a third query:-
SELECT First(q_gaps_per_product.[Store Nos]) AS [FirstOfStore Nos], First(q_gaps_per_product.[Store Name]) AS [FirstOfStore Name], First(q_gaps_per_product.Brand) AS FirstOfBrand, First(q_gaps_per_brand_store.[Order Id]) AS [FirstOfOrder Id], First(q_gaps_per_product.TPND) AS FirstOfTPND, First(q_gaps_per_product.TITLE) AS FirstOfTITLE, First(q_gaps_per_product.[Requested Date]) AS [FirstOfRequested Date], First(IIf([q_gaps_per_brand_store]![Requested Date]>=[q_gaps_per_product]![Requested Date],[Quantity],"PREVIOUS DAY")) AS Expr1, [q_gaps_per_product]![Store Nos] & [q_gaps_per_product]![Quantity] & [q_gaps_per_product]![TPND] AS Expr2
FROM q_gaps_per_product LEFT JOIN q_gaps_per_brand_store ON q_gaps_per_product.[Brand] = q_gaps_per_brand_store.[Brand]
GROUP BY [q_gaps_per_product]![Store Nos] & [q_gaps_per_product]![Quantity] & [q_gaps_per_product]![TPND];
Expr1 is supposed to be how many days a product is out of stock, with the idea that "PREVIOUS DAY" would return the same criteria for the previous day, to show either running gaps or that a product was in fact available as a 0, but I haven't got that far yet.
Expr2 is basically something I tried to make up to group the results by, as I had an insane number of results due to my janky table relationships.
I sort of think this query is DOA, and I need to go back to the drawing board to reproduce something like my Excel tables / how many days out of stock products have been concurrently out of stock before.
Sorry for the sheer storm of words!
Query: Network
I am having 2 Combobox ( Country and Network), i need to populate Network combo on the basis of item selected in Country combo.
Table1: Network
Platform Network Location_ID
Platform 1 Operator 1 56
Platform 1 Operator 2 59
Table2 : Location
Location_ID City Country
56 Hong Kong Hong Kong
109 Tafuna American Samoa
59 Delhi India
Help me to write sql query for same please.
Thanks in advance.
Bind your country dropdown to Location_ID and Country of Location table as value and text respectively.
You can use the following query for binding Location -
select Location_ID, Country from Location
And then for binding your Network combo box, use the following query :
select Network, Platform
from Network
where Location_ID = #Location_ID
#Location_ID is the selected value in your country dropdown list.
Note : Populate your network combo in the change event of country combo box.
I have 2 tables with different number of columns, and I need to export the data using SSIS to a text file. For example, I have customer table, tblCustomers; order table, tblOrders
tblCustomers (id, name, address, state, zip)
id name address state zip’
100 custA address1 NY 12345
99 custB address2 FL 54321
and
tblOrders(id, cust_id, name, quantity, total, date)
id cust_id name quantity total date
1 100 candy 10 100.00 04/01/2014
2 99 veg 1 2.00 04/01/2014
3 99 fruit 2 0.99 04/01/2014
4 100 veg 1 3.99 04/05/2014
The result file would be as following
“custA”, “100”, “recordtypeA”, “address1”, “NY”, “12345”
“custA”, “100”, “recordtypeB”, “candy”, “10”, “100.00”, “04/01/2014”
“custA”, “100”, “recordtypeB”, “veg”, “1”, “3.99”, “04/05/2014”
“custB”, “99”, “recordtypeA”, “address2”, “FL”, “54321”
“custB”, “99”, “recordtypeB”, “veg”, “1”, “2.00”, “04/01/2014”
“custB”, “99”, “recordtypeB”, “fruit”, “2”, “0.99”, “04/01/2014”
Can anyone please guild me as how to do this?
I presume you meant "guide", not "guild" - I hope your typing is more careful when you code?
I would create a Data Flow Task in an SSIS package. In that I would first add an OLE DB Source and point it at tblOrders. Then I would add a Lookup to add the data from tblCustomers, by matching tblOrders.Cust_id to tblCustomers.id.
I would use a SQL Query that joins the tables, and sets up the data, use that as a source and export that.
Note that the first row has 6 columns and the second one has 7. It's generally difficult (well not as easy as a standard file) to import these types of header/detail files. How is this file being used once created? If it needs to be imported somewhere you'd be better of just joining the data up and having 10 columns, or exporting them seperately.