I am working on a generalized problem where I am given only schema definition of multiple tables that i have.
Now i have to retrieve certain columns by joining multiple tables such that number of joins are minimized.
Example: Suppose i have 3 tables and here is the list of columns that they have.
Table 1:(1,2,3,4,5),
Table 2:(5,6,7),
Table 3:(5,6,7,8)
Now suppose I have a query in which i want all the columns 1,2,3,4,5,6,7,8.
Now i can join either table 1,table 2 and table 3 OR
table 1 and table 3.I would get the required information in both the cases but joining table 1 and table 3 would require only 1 join rather than 2 join in other case.
What i was trying was a greedy algorithm in which first i would consider table that has maximum number of required columns then eliminate the common columns between the query and table(from both query and table) and then consider updated required columns and update tables and so on.But i guess it would be slow.
So is there a generalized algorithm or if anyone can give me any hint in this direction?
first of all, I have to mention that it's not "join", but "union".
Then I have to mention that if you want to use the greedy algorithm, you have to first join the 2 most short, cause when you join a table 2 times, it would be of o(n), and so you will have 2n operations to do, and so it would be better if n be as smaller as possible.
Beside these, the following link may be useful for you:
Merging 3 tables/queries using MS Access Union Query
Related
I am trying to select a number of rows by the value of a column called ID. I know you can do this pretty easily by:
SELECT col1, col2, col3 FROM mytable WHERE id IN (1,2,3,4,5...)
However, what if there are a few million IDs I want to select and the IDs don't always have pattern (which means I can't use something like BETWEEN x AND y)? Does this select statement still work or is there better ways of doing so?
The actual application is this. Filters are specified by users, which is compared to some attributes of the records. From those filters, we create a subset of the data which is of interest to a particular user. There are about 30 million records each with roughly ~3000 attributes (which is stored in roughly 30 tables, but every table has ID as a primary key), so every time someone makes a query about their desired subset of records, we'd have to join many tables, apply those filters, and figure out what his subset looks like. In order to avoid joining many tables all the time, I thought maybe it's a better idea to join the tables once, figure out the id of the selected subset, and this way each time a new query is made, all we have to do is select the relevant columns of the rows that match the filtered ids.
This depends on the database and the interface you are using. For a few hundred or thousand values, no problem. But your question specifies millions. And that could start to get into limits on the length of the query -- either specified by the database, the tool you are using, or intermediate libraries.
If you have so many ids, I would strongly recommend that you load them into a table in the database with the id as the primary key. Then use join or exists to identify the rows in your table that match.
Often, such a list would be generated in the database anyway. In that case, you can use a subquery or CTE and just include that code in your final query.
I have the following tables:
In table 2 (yellow looking fields), the first field is part of the following:
name1 RECORD NULLABLE
name1. name2 RECORD REPEATED
name1.name2. date_inserted TIMESTAMP NULLABLE
As you can see the last (sub-row?) of the row 25 is greyed because it is part of the repeated record name1.name2
I am trying to join table 2, with table 1(orange looking fields) on another field. I have 0 experience with records or repeated records but using FLATTEN() I managed to join them.
The problem is, I noticed that some dates from the 2nd after the join return NULL although there aren't any NULLS before it. So since I can't figure out what the greyed cells are I guess I am doing something wrong.
All this sums up to: How can I totally flatten all tables that I want to use so that there won't be any records at all and so I can go through the data with simple SQL statements? Please provide an example as well. Looking for something generic.
How can I totally flatten all tables that I want to use so that there won't be any records at all and so I can go through the data with simple SQL statements?
It really depends on the schemas you are working with. You can preprocess them, flatten the arrays and rename the structs fields, then use that as your base table to work with simple SQL statements
For your scenario, you can start by flattening the table 2, name2 column like this
SELECT
name2.date_inserted -- Add additional fields you want on the result
FROM table2, table2.name1.name2
You can do CROSS JOIN and LEFT JOIN to further adjust your results.
Please provide an example as well. Looking for something generic.
I'm not sure about a generic approach, since each schema would probably have distinct requirements. The key concept is to know how to flatten arrays and how to query struct with arrays and arrays of structs
You can find plenty examples in that documentation
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 am a newbie to sql. I have three tables mr1,mr2,mr3. Caseid is the primary keys in all these tables. I need to join all these table columns and display result.
Problem is that i dont know which join to use.
when i joined all these just like below query:
select mr1.col1,mr1.col2,mr2.col1,mr2.col2,mr3.col1,mr3.col2
from mr1,mr2,mr3
where mr1.caseid = mr2.caseid
and mr2.caseid = mr3.caseid;
it displays 4 records, eventhough the maximum number of records is two, which is in table mr2.
records are duplicated, can anyone help me in this regard?
Distinct will do it but it's not the correct approch.
You need to add another join (mr1.caseid = mr3.caseid) because mr2 and mr3 rows must be related to the same row in mr1, otherwise you end up with 2 pairs, onde for each tabled joined to your primary table (mr2).
First answer in SO, so forgive me if it wasn't that clear.
Your problem is that your tables are in a one-to many relationship. When you join them, it is expected that the number of rows will go up unless you take steps to limit the records returned. How to fix depends on the meaning of the data.
If all the fields are exactly the same, then adding DISTINCT will fix the problem. However, it may be faster, depending on the size of the tables and the number of records you are returning, to use a derived table to limit the records in the join to only one from the table with multiple records.
If at least one of the fields is different however, then you need to know the business rule that will allow you to pick the correct record. It might be accomplished by adding a where clause or by using an aggregate function and group by or even both. This really depends on the meaning of the result set which is why you need to ask further question in your own organization as they are the only ones who will know which of the multiple records is the correct one to pick from the perspectives of the people who will be using the results of the query. Further, the business might actually want to see all of the records and you have no problem at all.
I'm always discouraged from using one, but is there a circumstance when it's the best approach?
It's rare, but I have a few cases where it's used. Typically in exception reports or ETL or other very peculiar situations where both sides have data you are trying to combine.
The alternative is to use an INNER JOIN, a LEFT JOIN (with right side IS NULL) and a RIGHT JOIN (with left side IS NULL) and do a UNION - sometimes this approach is better because you can customize each individual join more obviously (and add a derived column to indicate which side is found or whether it's found in both and which one is going to win).
I noticed that the wikipedia page provides an example.
For example, this allows us to see
each employee who is in a department
and each department that has an
employee, but also see each employee
who is not part of a department and
each department which doesn't have an
employee.
Note that I never encountered the need of a full outer join in practice...
I've used full outer joins when attempting to find mismatched, orphaned data, from both of my tables and wanted all of my result set, not just matches.
Just today I had to use Full Outer Join. It is handy in situations where you're comparing two tables. For example, the two tables I was comparing were from different systems so I wanted to get following information:
Table A has any rows that are not in Table B
Table B has any rows that are not in Table A
Duplicates in either Table A or Table B
For matching rows whether values are different (Example: The table A and Table B both have Acct# 12345, LoanID abc123, but Interest Rate or Loan Amount is different
In addition, I created an additional field in SELECT statement that uses a CASE statement to 'comment' why I am flagging this row. Example: Interest Rate does not match / The Acct doesn't exist in System A, etc.
Then saved it as a view. Now, I can use this view to either create a report and send it to users for data correction/entry or use it to pull specific population by 'comment' field I created using a CASE statement (example: all records with non-matching interest rates) in my stored procedure and automate correction, etc.
If you want to see an example, let me know.
The rare times i have used it has been around testing for NULLs on both sides of the join in case i think data is missing from the initial INNER JOIN used in the SQL i'm testing on.
They're handy for finding orphaned data but I rarely use then in production code. I wouldn't be "always discouraged from using one" but I think in the real world they are less frequently the best solution compared to inners and left/right outers.
In the rare times that I used Full Outer Join it was for data analysis and comparison purpose such as when comparing two customers tables from different databases to find out duplicates in each table or to compare the two tables structures, or to find out null values in one table compared to the other, or finding missing information in one tables compared to the other.
For example, suppose you have two tables: one containing customer data and another containing order data. A full outer join would allow you to see all customers and all orders, even if some customers have no orders or some orders have no corresponding customer. This can help you identify any gaps in the data and ensure that all relevant information is included in the result set.
It's important to note that a full outer join can produce a huge result set since it includes all rows from both tables. This can be inefficient in terms of performance, so it's best to use a full outer join only when it is necessary to include all rows from both tables.
SELECT *
FROM table1
FULL OUTER JOIN table2
ON table1.column_name = table2.column_name;
This will return all rows from both table1 and table2, filling in NULL values for missing matches on either side.