I have a table that I am trying to match against itself and create a record when the primary key does not match. Here is an example of the table
Location Value
A 5
B 10
C 15
I want to obtain the following table
Location Value Location(2) Value(2)
A 5 B 10
A 5 C 15
B 10 A 5
B 10 C 15
C 15 A 5
C 15 B 10
I have duplicated the first table and tried the various joins but I cannot get the result. Could anyone provide a suggestion on how this can be performed?
Looks like you want the vector product i.e. outer join. But to have a join you need a column with rows matching. A trick here would be to create a new column with just a single value like "1" for all of the rows (use "Insert Calculated Column" - be sure to freeze the column so you can join to it later). Then do a full outer join of that table with a copy of itself (use the "Insert Columns" feature for the join) using the column with that dummy column as the key field. You will then get the combinations you showed above, but it will also have rows where the keys matched.
To remove the matches, you can easily create a new column with an expression testing if the primary keys match like:
if([Location]=[Location(2)],"Match","NoMatch")
Then filter to the matching rows and delete if you don't want them in the data set.
You can certainly ask Spotfire questions here, but you can also try the Spotfire section of the TIBCO Community here:
https://community.tibco.com/products/spotfire
Related
Problem
I have a situation in which I have two tables in which I would like the entries from table 2 (lets call it table_2) to be matched up with the entries in table 1 (table_1) such that there are no duplicates rows of table_2 used in the match up.
Discussion
Specifically, in this case there are datetime stamps in each table (field is utcdatetime). For each row in table_1, I want to find the row in table_2 in which has the closed utcdatetime to the table 1 utcdatetime such that the table2.utcdatetime is older than the table_1 utcdatetime and within 30 minutes of the table 1 utcdatetime. Here is the catch, I do not want any repeats. If a row in table 2 gets gobbled up in a match on an earlier row in table 1, then I do not want it considered for a match later.
This has currently been implemented in a Python routine, but it is slow to iterate over all of the rows in table 1 as it is large. I thought I was there with a single SQL statement, but I found that my current SQL results in duplicate table 2 rows in the output data.
I would recommend using a nested select to get whatever results you're looking for.
For instance:
select *
from person p
where p.name_first = 'SCCJS'
and not exists (select 'x' from person p2 where p2.person_id != p.person_id
and p.name_first = 'SCCJS' and p.name_last = 'SC')
I am using Postgres and have this query:
select d.id,d.santoral_id,d.tipointencion_id from intencion_detalle d
join intencion i ON (i.id=d.intencion_id)
where i.fecha='2017-08-29';
and this is the output:
I want the row 6 and 7 that has the same santoral_id and tipointencion_id to be shown only once not multiple times, and in the same query I want to show the column description that is not in the image but exist in the table. How can I do this?
I need to delete records from 2 different tables that are linked via job#
One table contains dates for completion and I need to delete all records that were completed between 1999 and 2001. In the second table I need to delete all phases of the job where Job number from table 1 match job number in table 2
I've researched a bit and came up with something like this but when running it I receive "Too Many Fields Selected"
DELETE a.,b.
FROM PUB_jc_job a
LEFT JOIN PUB_jc_phase b
ON b.jph_job = a.job_num
WHERE PUB_jc_job.job_compdate BETWEEN #3/31/1999# AND #12/31/2001#
Please remove dot(.) from table alias "a., b." and retry.
DELETE a, b FROM PUB_jc_job a INNER JOIN PUB_jc_phase b ON b.jph_job = a.job_num WHERE PUB_jc_job.job_compdate BETWEEN #3/31/1999# AND #12/31/2001#
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
Currently struggling with finding a way to validate 2 tables (efficiently lots of rows for Table A)
I have two tables
Table A
ID
A
B
C
Table matched
ID Number
A 1
A 2
A 9
B 1
B 9
C 2
I am trying to write a SQL Server query that basically checks to make sure for every value in Table A there exists a row for a variable set of values ( 1, 2,9)
The example above is incorrect because t should have for every record in A a corresponding record in Table matched for each value (1,2,9). The end goal is:
Table matched
ID Number
A 1
A 2
A 9
B 1
B 2
B 9
C 1
C 2
C 9
I know its confusing, but in general for every X in ( some set ) there should be a corresponding record in Table matched. I have obviously simplified things.
Please let me know if you all need clarification.
Use:
SELECT a.id
FROM TABLE_A a
JOIN TABLE_B b ON b.id = a.id
WHERE b.number IN (1, 2, 9)
GROUP BY a.id
HAVING COUNT(DISTINCT b.number) = 3
The DISTINCT in the COUNT ensures that duplicates (IE: A having two records in TABLE_B with the value "2") from being falsely considered a correct record. It can be omitted if the number column either has a unique or primary key constraint on it.
The HAVING COUNT(...) must equal the number of values provided in the IN clause.
Create a temp table of values you want. You can do this dynamically if the values 1, 2 and 9 are in some table you can query from.
Then, SELECT FROM tempTable WHERE NOT IN (SELECT * FROM TableMatched)
I had this situation one time. My solution was as follows.
In addition to TableA and TableMatched, there was a table that defined the rows that should exist in TableMatched for each row in TableA. Let’s call it TableMatchedDomain.
The application then accessed TableMatched through a view that controlled the returned rows, like this:
create view TableMatchedView
select a.ID,
d.Number,
m.OtherValues
from TableA a
join TableMatchedDomain d
left join TableMatched m on m.ID = a.ID and m.Number = d.Number
This way, the rows returned were always correct. If there were missing rows from TableMatched, then the Numbers were still returned but with OtherValues as null. If there were extra values in TableMatched, then they were not returned at all, as though they didn't exist. By changing the rows in TableMatchedDomain, this behavior could be controlled very easily. If a value were removed TableMatchedDomain, then it would disappear from the view. If it were added back again in the future, then the corresponding OtherValues would appear again as they were before.
The reason I designed it this way was that I felt that establishing an invarient on the row configuration in TableMatched was too brittle and, even worse, introduced redundancy. So I removed the restriction from groups of rows (in TableMatched) and instead made the entire contents of another table (TableMatchedDomain) define the correct form of the data.