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.
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'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".
I am new to VBA so I apologize in advance if this seems basic to you experts but I appreciate all of the help I can get.
I have a table containing a column of reference numbers that can grow or shrink weekly. I also have a query pulling back price list data that has changed since last week. The query results vary weekly. What I need to do is assign all of the query results to each reference number and have all of that end up in a make table. For example if there are 10 reference numbers and the query result is 10 rows then 100 lines would be added to the table (adding the reference number to the beginning of each row). This sounds like some sort of loop but your the experts, not me.
Thanks in advance!
You can solve it with a cross join. In a cross join you join two tables without specifying a join clause. Such a query returns all possible combinations of rows of the two tables (this is called a Cartesian product)
SELECT col_a, col_b INTO newTable
FROM table_a, table_b
If table_a contains 10 rows and table_b contains 5 rows, this returns 50 rows.
I am trying to develop a SQL Server 2005 query but I'm being unsuccessful at the moment. I trying every different approach that I know, like derived tables, sub-queries, CTE's, etc, but I couldn't solve the problem. I won't post the queries I tried here because they involve many other columns and tables, but I will try to explain the problem with a simpler example:
There are two tables: PARTS_SOLD and PARTS_PURCHASED. The first contains products that were sold to customers, and the second contains products that were purchased from suppliers. Both tables contains a foreign key associated with the movement itself, that contains the dates, etc.
Here is the simplified schema:
Table PARTS_SOLD:
part_id
date
other columns
Table PARTS_PURCHASED
part_id
date
other columns
What I need is to join every row in PARTS_SOLD with a unique row from PARTS_PURCHASED, chose by part_id and the maximum "date", where the "date" is equal of before the "date" column from PARTS_PURCHASED. In other words, I need to collect some information from the last purchase event for the item for every event of selling this item.
The problem itself is that I didn't find a way of joining the PARTS_PURCHASED table with PARTS_SOLD table using the column "date" from PARTS_SOLD to limit the MAX(date) of the PARTS_PURCHASED table.
I could have done this with a cursor to solve the problem with the tools I know, but every table has millions of rows, and perhaps using cursors or sub-queries that evaluate a query for every row would make the process very slow.
You aren't going to like my answer. Your database is designed incorrectly which is why you can't get the data back out the way you want. Even using a cursor, you would not get good data from this. Assume that you purchased 5 of part 1 on May 31, 2010. Assume on June 1, you sold ten of part 1. Matching just on date, you would match all ten to the May 31 purchase even though that is clearly not correct, some parts might have been purchased on May 23 and some may have been purchased on July 19, 2008.
If you want to know which purchased part relates to which sold part, your database design should include the PartPurchasedID as part of the PartsSold record and this should be populated at the time of the purchase, not later for reporting when you have 1,000,000 records to sort through.
Perhaps the following would help:
SELECT S.*
FROM PARTS_SOLD S
INNER JOIN (SELECT PART_ID, MAX(DATE)
FROM PARTS_PURCHASED
GROUP BY PART_ID) D
ON (D.PART_ID = S.PART_ID)
WHERE D.DATE <= S.DATE
Share and enjoy.
I'll toss this out there, but it's likely to contain all kinds of mistakes... both because I'm not sure I understand your question and because my SQL is... weak at best. That being said, my thought would be to try something like:
SELECT * FROM PARTS_SOLD
INNER JOIN (SELECT part_id, max(date) AS max_date
FROM PARTS_PURCHASED
GROUP BY part_id) AS subtable
ON PARTS_SOLD.part_id = subtable.part_id
AND PARTS_SOLD.date < subtable.max_date
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.