Hi I want to search through table 2 with a value (names CustID) from table 1, and if all the values are found in table 2 (CustID) that matches table 1 (CustID) the values must be shown together with all the values from table 1 that does not match table 2's values.
Is it possible to do it this way and if it is can you please show me how, I need this for a project.
Thanks in advance
It sounds like you want a "LEFT" JOIN, which will pull up all records in table1 plus the matching ones in table2.
SELECT A.,B. FROM table1 A LEFT JOIN table2 B ON A.CustID = B.CustID
Related
Just to level set: i'm working within a Vertica database using SQL.
Let's say i have two tables: Table A and Table B. Let's also say that Table A is my final/master table used for data vis within Tableau (or something akin), and that Table B feeds certain columns into Table A based on matches within a tertiary table, Table C (which is not relevant to this conversation).
As is, Table A has columns:
ProgramName [varchar(50)]
CustomerName [varchar(50)]
Total_Cost [numeric(18,4)]
As is, Table B has columns:
CustomerCode [varchar(10)]
Total_Cost [numeric(18,4)]
What I would like to do is update Table A's CustomerName column to equal CustomerCode in Table B where the columns of total_cost_dollars equal each other across tables.
I've run this left join query to ensure that, when I do update Table A's CustomerName to equal CustomerCode, the total cost columns are exact/true matches for my entire data set.
SELECT
A.ProgramName,
A.CustomerName,
A.total_cost_dollars,
B.CustomerCode,
B.total_cost_dollars
FROM
TableA A
LEFT JOIN
TableB B
ON
B.total_cost_dollars = A.total_cost_dollars
WHERE
A.CustomerName IS NULL;
Any idea on how to solve this problem?
Since Vertica supports merge query, you can use merge statement:
merge into TableA A
using TableB B
ON (B.total_cost_dollars = A.total_cost_dollars)
when matched then
update
set
A.CustomerName = B.CustomerCode
where
A.CustomerName IS NULL;
I have 2 tables with 1 column in common. I want to select all data from table1 and want to restrict it with a condition. The thing I could not do is writing the where condition with the non-common column.
Here is the code:
$sql="SELECT kategoriler.adi as katadi, urunler.* FROM `urunler`
LEFT JOIN `kategoriler`
ON urunler.kategori_id=kategoriler.Id
WHERE urunler.kategori_id=$id
OR
kategoriler.ust_id = $id";
ust_id is a parent column of kategori_id and kategoriler.Id. I want to select the child values of it.
(Btw as you can see the common columns are urunler.kategori_id and kategoriler.Id)
I have two tables bottle and case that are filled out. They both have a column labeled case_id however in bottle table all the values are 0 while in case table they have the correct id.
How can I update the first table bottle values 0 with the new values from other table case? I believe I will need to use an UPDATE or INSERT or INNER JOIN.
If you give more detail for table struct will be better
UPDATE b SET b.case_id=c.case_id
FROM bottle AS b INNER JOIN [Case] AS c ON b.some_coloumn=c.some_cloumn
I have 3 columns in table A. I am trying to design a query that will call out all the values (in the three columns) that do not apepar in the 1 column I have in table B. If it helps to make it more clear, table B is a list of currencies in ISO codes and table A is three columns of currencies being used, I am identifying all those values that are NOT using ISO codes to denote their currency.
Currently, I can't seem to get them all to match to the one column, so I made 2 more columns in table B so I can match them individually. My constraints are, I cannot change table A and I must do this in one query. What I got so far is below.
SELECT m.Currency1, i.ISO_Code, m.Currency2 , i.ISO_Code1, m.Currency3, i.ISO_Code2
FROM A AS m
LEFT JOIN B AS i
ON m.Currency=i.ISO_Code
AND m.Currency2=i.ISO_Code1
AND m.Currency3=i.ISO_Code2
WHERE i.ISO_Code is NULL
OR i.ISO_Code1 is NULL
OR i.ISO_Code2 is NULL;
I wouldn't bother making multiple columns in 'B'. I played with this in SQLFiddle and got it to work.
Something like this:
SELECT
m.Currency1, i.ISO_Code,
m.Currency2, j.ISO_Code AS ISO_Code1,
m.Currency3, k.ISO_Code AS ISO_Code2
FROM A AS m
LEFT JOIN B as i
ON m.Currency1 = i.ISO_Code
LEFT JOIN B as j
ON m.Currency2 = j.ISO_Code
LEFT JOIN B as k
ON m.Currency3 = k.ISO_Code
WHERE
i.ISO_Code IS NULL OR
j.ISO_Code IS NULL OR
k.ISO_Code IS NULL
I was surprised by the outcome of these two queries. I was expecting same from both. I have two tables that share a common field but there is not a relationship set up. The table (A) has a field EventID varchar(10) and table (B) has a field XXNumber varchar(15).
Values from table B column XXNumber are referenced in table A column EventID. Even though XXNumber can hold 15 chars, none of the 179K rows of data is longer than 10 chars.
So the requirement was:
"To avoid Duplicate table B and table A entries, if the XXNumber is contained in a table A >“Event ID” number, then it should not be counted."
To see how many common records I have I ran this query first - call it query alpha"
SELECT dbo.TableB.XXNumber FROM dbo.TableB WHERE dbo.TableB.XXNumber in
( select distinct dbo.TableA.EventId FROM dbo.TableA )
The result was 5322 rows.
The following query - call it query delta which looks like this:
SELECT DISTINCT dbo.TableB.XXNumber, dbo.TableB.EventId
FROM dbo.TableB INNER JOIN dbo.TableA ON dbo.TableB.XXNumber= dbo.TableB.EventId
haas returned 4308 rows.
Shouldn't the resulting number of rows be the same?
The WHERE ID IN () version will select all rows that match each distinct value in the list (regardless of whether you code DISTINCT indide the inner select or not - that's irrelevant). If a given value appears in the parent table more than once, you'll get multipke rows selected from the parent table for that single value found in the child table.
The INNER JOIN version will select each row from the parent table once for every successful join, so if there are 3 rows in the child table with the value, and 2 in the parent, then there will be 6 rows rows in the result for that value.
To make them "the same", add 'DISTINCT' to your main select.
To explain what you're seeing, we'd need to know more about your actual data.