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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 and 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 INTERSECT and MINUS (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 PIVOT using 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;

Further reading

Excited? Yes. There are more awesome SQL features in various databases. Read on about:

Thrive in the application economy with an APM model that is strategic. Be E.P.I.C. with CA APM.  Brought to you in partnership with CA Technologies.

Topics:

Published at DZone with permission of Lukas Eder, DZone MVB. See the original article here.

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