How to Use SQL PIVOT To Compare Two Tables in Your Database
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This can happen ever so easily. You adapt a table by adding a new column:
ALTER TABLE payments ADD code NUMBER(3);
You go on, implementing your business logic – absolutely no problem. But then, later on (perhaps in production), some batch job fails because it makes some strong assumptions about data types. Namely, it assumes that the two tables
payments_archive are of the same row type:
CREATE TABLE payments ( id NUMBER(18) NOT NULL, account_id NUMBER(18) NOT NULL, value_date DATE, amount NUMBER(25, 2) NOT NULL ); CREATE TABLE payments_archive ( id NUMBER(18) NOT NULL, account_id NUMBER(18) NOT NULL, value_date DATE, amount NUMBER(25, 2) NOT NULL );
Being of the same row type, you can simply move a row from one table to the other, e.g. using a query like this one:
INSERT INTO payments_archive SELECT * FROM payments WHERE value_date < SYSDATE - 30;
(not that using the above syntax is a good idea in general, it’s actually a bad idea. but you get the point)
What you’re getting now is this:
ORA-00913: too many values
The fix is obvious, but probably, the poor soul who has to fix this is not you, but someone else who has to figure out among possibly hundreds of columns, which ones don’t match. Here’s how (in Oracle):
Use PIVOT to compare two tables!
You could of course not use
PIVOT and simply select all columns from either table from the dictionary views:
SELECT table_name, column_name FROM all_tab_cols WHERE table_name LIKE 'PAYMENTS%'
This will produce the following result:
TABLE_NAME COLUMN_NAME ------------------ --------------- PAYMENTS ID PAYMENTS ACCOUNT_ID PAYMENTS VALUE_DATE PAYMENTS AMOUNT PAYMENTS CODE PAYMENTS_ARCHIVE ID PAYMENTS_ARCHIVE ACCOUNT_ID PAYMENTS_ARCHIVE VALUE_DATE PAYMENTS_ARCHIVE AMOUNT
Not very readable. You could of course use set operations and apply
EXCEPT) to filter out matching values. But much better:
SELECT * FROM ( SELECT table_name, column_name FROM all_tab_cols WHERE table_name LIKE 'PAYMENTS%' ) PIVOT ( COUNT(*) AS cnt FOR (table_name) IN ( 'PAYMENTS' AS payments, 'PAYMENTS_ARCHIVE' AS payments_archive ) ) t;
And the above now produces:
COLUMN_NAME PAYMENTS_CNT PAYMENTS_ARCHIVE_CNT ------------ ------------ -------------------- CODE 1 0 ACCOUNT_ID 1 1 ID 1 1 VALUE_DATE 1 1 AMOUNT 1 1
It is now very easy to identify the column that is missing from the
PAYMENTS_ARCHIVE table. As you can see, the result from the original query produced one row per column AND per table. We took that result and pivoted it “FOR” the table name, such that we will now only get one row per column
How to read PIVOT?
It’s easy. Comments are inline:
SELECT * -- This is the table that we're pivoting. Note that -- we select only the minimum to prevent side-effects FROM ( SELECT table_name, column_name FROM all_tab_cols WHERE table_name LIKE 'PAYMENTS%' ) -- PIVOT is a keyword that is applied to the above -- table. It generates a new table, similar to JOIN PIVOT ( -- This is the aggregated value that we want to -- produce for each pivoted value COUNT(*) AS available -- This is the source of the values that we want to -- pivot FOR (table_name) -- These are the values that we accept as pivot -- columns. The columns names are produced from -- these values concatenated with the corresponding -- aggregate function name IN ( 'PAYMENTS' AS payments, 'PAYMENTS_ARCHIVE' AS payments_archive ) ) t;
That’s it. Not so hard, was it?
The nice thing about this syntax is that we can generate as many additional columns as we want, very easily:
SELECT * FROM ( SELECT table_name, column_name, cast(data_type as varchar(6)) data_type FROM all_tab_cols WHERE table_name LIKE 'PAYMENTS%' ) PIVOT ( COUNT(*) AS cnt, MAX(data_type) AS type -- new function here FOR (table_name) IN ( 'PAYMENTS' AS p, 'PAYMENTS_ARCHIVE' AS a ) ) t;
… producing (after additional erroneous DDL) …
COLUMN_NAME P_CNT P_TYPE A_CNT A_TYPE ----------- ---------- ------ ---------- ------ CODE 1 NUMBER 0 ACCOUNT_ID 1 NUMBER 1 NUMBER ID 1 NUMBER 1 NUMBER VALUE_DATE 1 DATE 1 TIMESTAMP AMOUNT 1 NUMBER 1 NUMBER
This way, we can discover even more flaws between the different row types of the tables. In the above example, we’ve used
MAX(), because we have to provide an aggregation function, even if each pivoted column corresponds to exactly one row in our example – but that doesn’t have to be.
What if I’m not using Oracle?
SQL Server also supports PIVOT, but other databases don’t. You can always emulate
GROUP BY and
CASE. The following statement is equivalent to the previous one:
SELECT t.column_name, count(CASE table_name WHEN 'PAYMENTS' THEN 1 END) p_cnt, max (CASE table_name WHEN 'PAYMENTS' THEN data_type END) p_type, count(CASE table_name WHEN 'PAYMENTS_ARCHIVE' THEN 1 END) a_cnt, max (CASE table_name WHEN 'PAYMENTS_ARCHIVE' THEN data_type END) a_type FROM ( SELECT table_name, column_name, data_type FROM all_tab_cols WHERE table_name LIKE 'PAYMENTS%' ) t GROUP BY t.column_name;
This query will now produce the same result on all the other databases as well.
Isn’t that… ?
Yes, it is! The above usage of aggregate functions in combination with
CASE can be shortened even more, using the SQL standard
FILTER clause, which we’ve blogged about recently.
So, in PostgreSQL, you could write the following query:
SELECT t.column_name, count(table_name) FILTER (WHERE table_name = 'payments') p_cnt, max(data_type) FILTER (WHERE table_name = 'payments') p_type, count(table_name) FILTER (WHERE table_name = 'payments_archive') a_cnt, max(data_type) FILTER (WHERE table_name = 'payments_archive') a_type FROM ( SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_name LIKE 'payments%' ) t GROUP BY t.column_name;
Excited? Yes. There are more awesome SQL features in various databases. Read on about:
Published at DZone with permission of Lukas Eder, DZone MVB. See the original article here.
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