I was practicing a subqueries in sql and all of a sudden i jumped into an unsual query which i never thought of could happen.
The question of my query is....
Write a query to display the average rate of Australian dollar,where the currency rate date is July 1 2005??
And the query was...
USE AdventureWorks2012
SELECT AverageRate FROM Sales.CurrencyRate
WHERE ToCurrencyCode='AUD' AND CurrencyRateDate IN
(SELECT CurrencyRateDate FROM Sales.Currency
WHERE CurrencyRateDate='2005-07-01')
So,my question is how is it possible to get the column name "CurrencyRateDate" in the sub query when it is actually from the table "CurrencyRate"??
I know my query is not in the correct format as it should be.
I'm extremely sorry if my title doesn't make sense.If you guys can give any better please change it..
Thanks
AND CurrencyRateDate IN
(SELECT CurrencyRateDate FROM Sales.Currency
WHERE CurrencyRateDate='2005-07-01')
All the CurrencyRateDate references here point to the column from the outer query.
So for each row in the outer query, you are getting a list consisting of only that row's CurrencyRateDate, repeated once for every row in the Sales.Currency table (if the CurrencyRateDate of that row is 2005-07-01, otherwise the list is empty).
Then you check whether the outer CurrencyRateDate value is in that list. Which it is, if and only if it's equal to 2005-07-01 (assuming there is at least one row in Sales.Currency).
So your query is equivalent to:
SELECT * FROM Sales.CurrencyRate
WHERE ToCurrencyCode='AUD' AND CurrencyRateDate='2005-07-01'
Related
I have two tables I am left joining together. The first tables has transnational level detail, causing the key I join to the second table to duplicate. When I left join the second table, the measure "company_spend" is highly inflated.
I need a way to keep only a single value of the duplicated data, and my thought was to run a distinct function on only those columns, but I am not seeing that Bigquery supports distinct functions on only a few columns, but not all.
SELECT UPPER(cwnextt.Current_Contract_Number) AS Current_Contract_Number,
UPPER(cwnextt.Replacement_Contract_Number) AS Replacement_Contract_Number,
UPPER(cwnextt.Current_Contract_Name) AS Current_Contract_Name,
UPPER(cwnextt.Supplier_Top_Parent_Entity_Code) AS Supplier_Top_Parent_Entity_Code,
UPPER(cwnextt.Supplier_Top_Parent_Name) AS Supplier_Top_Parent_Name,
UPPER(cwnextt.company_Entity_Code) AS company_Entity_Code,
UPPER(cwnextt.Facility_Name) AS Facility_Name,
smart.company_Spend AS companySpend
FROM `test_etl_field.contracts_with_member_entity_codes_test_view_2` cwnextt
--this table is what is causing the below table to duplicate,
--but I need all of this data AS well in its current format.
LEFT JOIN `test.trans_analysis` tsa
ON TRIM(UPPER(cwnextt.company_entity_code)) = TRIM(UPPER(tsa.company_entity_code))
AND TRIM(UPPER(cwnextt.Supplier_Top_Parent_Entity_Code)) = TRIM(UPPER(tsa.manufacturer_top_parent_entity_code))
AND TRIM(UPPER(cwnextt.Current_Contract_Name)) = TRIM(UPPER(tsa.contract_category))
AND cwnextt.spend_period_yyyyqmm = tsa.spend_period_yyyyqmm
--this table contains "company_spend" which is now duplicated
LEFT JOIN `test_etl_field.ecr_smart_data` smart
ON smart.company_entity_code = cwnextt.company_entity_code
AND (smart.contract_number = cwnextt.current_contract_number
OR smart.contract_number = cwnextt.replacement_contract_number)
AND smart.month_key = cwnextt.spend_period_yyyyqmm
If something can be created that will keep company_spend from duplicating on the second left join, that is what I am after.
Not sure to understand all the details of your problem but here's a fact from BigQuery doc :
SELECT DISTINCT
A SELECT DISTINCT statement discards duplicate rows
and returns only the remaining rows.
You can't apply DISTINCT on specific columns because it doesn't make sense. Let's say you have 4 columns and call DISTINCT on 3 columns, what is SQL supposed to do with the last one ?
You must tell SQL which value to keep for the remaining column and GROUP BY is the right solution here.
So if you want to:
Remove a column that has been duplicated : Just adjust your SELECT to get only the columns you want
Remove lines that have the same value in specific columns : I would suggest a GROUP BY on the targeted column and taking the aggregation you want (first, avg, sum or whatever) for the remaining ones.
Remove the value from a row if another row has the same : You may not want to do that. A row has to keep its value and you won't get it back. Besides, same problem, which row do you want to keep ?
Hope this helps ! Feel free to give clarification on your problem if you want more specific answers.
While I couldn't resolve this issue in SQL, I used Tableau via a FIXED LOD to aggregate the data passed duplicates so the end user could visualize the output with accuracy. Not ideal, but the SQL route wasn't make sense.
I'm trying to achieve 2 joins. If I run the 1st join alone it pulls 4 lots of results, which is correct. However when I add the 2nd join which queries the same reference table using the results from the select statement it pulls in additional results. Please see attached. The squared section should not be being returned
So I removed the 2nd join to try and explain better. See pic2. I'm trying to get another column which looks up InvolvedInternalID against the initial reference table IRIS.Practice.idvClient.
Your database is simply doing as you tell it. When you add in the second join (confusingly aliased as tb1 in a 3 table query) the database is finding matching rows that obey the predicate/truth statement in the ON part of the join
If you don't want those rows in there then one of two things must be the case:
1) The truth you specified in the ON clause is faulty; for example saying SELECT * FROM person INNER JOIN shoes ON person.age = shoes.size is faulty - two people with age 13 and two shoes with size 13 will produce 4 results, and shoe size has nothing to do with age anyway
2) There were rows in the table joined in that didn't apply to the results you were looking for, but you forgot to filter them out by putting some WHERE (or additional restriction in the ON) clause. Example, a table holds all historical data as well as current, and the current record is the one with a NULL in the DeletedOn column. If you forget to say WHERE deletedon IS NULL then your data will multiply as all the past rows that don't apply to your query are brought in
Don't alias tables with tbX, tbY etc.. Make the names meaningful! Not only do aliases like tbX have no relation to the original table name (so you encounter tbX, and then have to go searching the rest of the query to find where it's declared so you can say "ah, it's the addresses table") but in this case you join idvclient in twice, but give them unhelpful aliases like tb1, tb3 when really you should have aliased them with something that describes the relationship between them and the rest of the query tables
For example, ParentClient and SubClient or OriginatingClient/HandlingClient would be better names, if these tables are in some relationship with each other.
Whatever the purpose of joining this table in twice is, alias it in relation to the purpose. It may make what you've done wriong easier to spot, for example "oh, of course.. i'm missing a WHERE parentclient.type = 'parent'" (or WHERE handlingclient.handlingdate is not null etc..)
The first step to wisdom is by calling things their proper names
I've 250+ columns in customer table. As per my process, there should be only one row per customer however I've found few customers who are having more than one entry in the table
After running distinct on entire table for that customer it still returns two rows for me. I suspect one of column may be suffixed with space / junk from source tables resulting two rows of same information.
select distinct * from ( select * from customer_table where custoemr = '123' ) a;
Above query returns two rows. If you see with naked eye to results there is not difference in any of column.
I can identify which column is causing duplicates if I run query every time for each column with distinct but thinking that would be very manual task for 250+ columns.
This sounds like very dumb question but kind of stuck here. Please suggest if you have any better way to identify this, thank you.
Solving this one-time issue with sql is too much effort. Simply copy-paste to excel, transpose data into columns and use some simple function like "if a==b then 1 else 0".
So I have a data table which looks like
Where each row has a timestamp column in Unix time. I need to find all the places where two entries with the same resource_id are x(day month, year etc) amount of time apart, so I need a query that will go through and look at the differences between one row and the next and spit back the ones which differ by more than a specified amount.
Anybody have any ideas on how to do this? Thanks in advance
You may use a cross join to compare every row in the table with every other row in the table then compare the field. For example the following will return where the two rows are 2 months apart.
SELECT t.resource_id, s.resource_id
FROM table t CROSS JOIN table s
WHERE TIMESTAMPDIFF(MONTH,t.timestamp,s.timestamp) = 2
Note that this could be extremely slow if the table is large. Or according to the MySQL docs just saying JOIN without specifying the condition will result in a cartesian product which is equivalent to a cross join.
This query takes about a minute to give results:
SELECT MAX(d.docket_id), MAX(cus.docket_id) FROM docket d, Cashup_Sessions cus
Yet this one:
SELECT MAX(d.docket_id) FROM docket d UNION MAX(cus.docket_id) FROM Cashup_Sessions cus
gives its results instantly. I can't see what the first one is doing that would take so much longer - I mean they both simply check the same two lists of numbers for the greatest one and return them. What else could it be doing that I can't see?
I'm using jet SQL on an MS Access database via Java.
the first one is doing a cross join between 2 tables while the second one is not.
that's all there is to it.
The first one uses Cartesian product to form a source data, which means that every row from the first table is paired with each row from the second one. After that, it searches the source to find out the max values from the columns.
The second doesn't join tables. It just find max from the fist table and the max one from the second table and than returns two rows.
The first query makes a cross join between the tables before getting the maximums, that means that each record in one table is joined with every record in the other table.
If you have two tables with 1000 items each, you get a result with 1000000 items to go through to find the maximums.