OpenText Analytics Database 26.2.x – Changing scalar column data type

Admin: Changing column width

Mon, 01 Jan 0001 00:00:00 +0000

You can expand columns within the same class of data type. Doing so is useful for storing larger items in a column. The database validates the data before it performs the conversion.

In general, you can also reduce column widths within the data type class. This is useful to reclaim storage if the original declaration was longer than you need, particularly with strings. You can reduce column width only if the following conditions are true:

Existing column data is no greater than the new width.
All nodes in the database cluster are up.

Otherwise, the database returns an error and the conversion fails. For example, if you try to convert a column from varchar(25) to varchar(10), the database allows the conversion as long as all column data is no more than 10 characters.

In the following example, columns y and z are initially defined as VARCHAR data types, and loaded with values 12345 and 654321, respectively. The attempt to reduce column z's width to 5 fails because it contains six-character data. The attempt to reduce column y's width to 5 succeeds because its content conforms with the new width:

=> CREATE TABLE t (x int, y VARCHAR, z VARCHAR);
CREATE TABLE
=> CREATE PROJECTION t_p1 AS SELECT * FROM t SEGMENTED BY hash(x) ALL NODES;
CREATE PROJECTION
=> INSERT INTO t values(1,'12345','654321');
 OUTPUT
--------
      1
(1 row)

=> SELECT * FROM t;
 x |   y   |   z
---+-------+--------
 1 | 12345 | 654321
(1 row)

=> ALTER TABLE t ALTER COLUMN z SET DATA TYPE char(5);
ROLLBACK 2378:  Cannot convert column "z" to type "char(5)"
HINT:  Verify that the data in the column conforms to the new type
=> ALTER TABLE t ALTER COLUMN y SET DATA TYPE char(5);
ALTER TABLE

Changing collection columns

If a column is a collection data type, you can use ALTER TABLE to change either its bounds or its maximum binary size. These properties are set at table creation time and can then be altered.

You can make a collection bounded, setting its maximum number of elements, as in the following example.

=> ALTER TABLE test.t1 ALTER COLUMN arr SET DATA TYPE array[int,10];
ALTER TABLE

=> \d test.t1
                                     List of Fields by Tables
 Schema | Table | Column |      Type       | Size | Default | Not Null | Primary Key | Foreign Key
--------+-------+--------+-----------------+------+---------+----------+-------------+-------------
  test  |  t1   | arr    | array[int8, 10] |   80 |         | f        | f           |
(1 row)

Alternatively, you can set the binary size for the entire collection instead of setting bounds. Binary size is set either explicitly or from the DefaultArrayBinarySize configuration parameter. The following example creates an array column from the default, changes the default, and then uses ALTER TABLE to change it to the new default.

=> SELECT get_config_parameter('DefaultArrayBinarySize');
 get_config_parameter
----------------------
 100
(1 row)

=> CREATE TABLE test.t1 (arr array[int]);
CREATE TABLE

=> \d test.t1
                                     List of Fields by Tables
 Schema | Table | Column |      Type       | Size | Default | Not Null | Primary Key | Foreign Key
--------+-------+--------+-----------------+------+---------+----------+-------------+-------------
  test  |  t1   | arr    | array[int8](96) |   96 |         | f        | f           |
(1 row)

=> ALTER DATABASE DEFAULT SET DefaultArrayBinarySize=200;
ALTER DATABASE

=> ALTER TABLE test.t1 ALTER COLUMN arr SET DATA TYPE array[int];
ALTER TABLE

=> \d test.t1
                                     List of Fields by Tables
 Schema | Table | Column |      Type       | Size | Default | Not Null | Primary Key | Foreign Key
--------+-------+--------+-----------------+------+---------+----------+-------------+-------------
  test  |  t1   | arr    | array[int8](200)|  200 |         | f        | f           |
(1 row)

Alternatively, you can set the binary size explicitly instead of using the default value.

=> ALTER TABLE test.t1 ALTER COLUMN arr SET DATA TYPE array[int](300);

Purging historical data

You cannot reduce a column's width if the database retains any historical data that exceeds the new width. To reduce the column width, first remove that data from the table:

Advance the AHM to an epoch more recent than the historical data that needs to be removed from the table.
Purge the table of all historical data that precedes the AHM with the function PURGE_TABLE.

For example, given the previous example, you can update the data in column t.z as follows:

=> UPDATE t SET z = '54321';
 OUTPUT
--------
      1
(1 row)

=> SELECT * FROM t;
 x |   y   |   z
---+-------+-------
 1 | 12345 | 54321
(1 row)

Although no data in column z now exceeds 5 characters, the database retains the history of its earlier data, so attempts to reduce the column width to 5 return an error:

=> ALTER TABLE t ALTER COLUMN z SET DATA TYPE char(5);
ROLLBACK 2378:  Cannot convert column "z" to type "char(5)"
HINT:  Verify that the data in the column conforms to the new type

You can reduce the column width by purging the table's historical data as follows:

=> SELECT MAKE_AHM_NOW();
         MAKE_AHM_NOW
-------------------------------
 AHM set (New AHM Epoch: 6350)
(1 row)

=> SELECT PURGE_TABLE('t');
                                                     PURGE_TABLE
----------------------------------------------------------------------------------------------------------------------
 Task: purge operation
(Table: public.t) (Projection: public.t_p1_b0)
(Table: public.t) (Projection: public.t_p1_b1)

(1 row)

=> ALTER TABLE t ALTER COLUMN z SET DATA TYPE char(5);
ALTER TABLE

Admin: Working with column data conversions

Mon, 01 Jan 0001 00:00:00 +0000

The database conforms to the SQL standard by disallowing certain data conversions for table columns. However, you sometimes need to work around this restriction when you convert data from a non-SQL database. The following examples describe one such workaround, using the following table:

=> CREATE TABLE sales(id INT, price VARCHAR) UNSEGMENTED ALL NODES;
CREATE TABLE
=> INSERT INTO sales VALUES (1, '$50.00');
 OUTPUT
--------
      1
(1 row)

=> INSERT INTO sales VALUES (2, '$100.00');
 OUTPUT
--------
      1
(1 row)

=> COMMIT;
COMMIT
=> SELECT * FROM SALES;
 id |  price
----+---------
  1 | $50.00
  2 | $100.00
(2 rows)

To convert the price column's existing data type from VARCHAR to NUMERIC, complete these steps:

Add a new column for temporary use. Assign the column a NUMERIC data type, and derive its default value from the existing price column.
Drop the original price column.
Rename the new column to the original column.

Add a new column for temporary use

Add a column temp_price to table sales. You can use the new column temporarily, setting its data type to what you want (NUMERIC), and deriving its default value from the price column. Cast the default value for the new column to a NUMERIC data type and query the table:
```
=> ALTER TABLE sales ADD COLUMN temp_price NUMERIC(10,2) DEFAULT
SUBSTR(sales.price, 2)::NUMERIC;
ALTER TABLE

=> SELECT * FROM SALES;
 id |  price  | temp_price
----+---------+------------
  1 | $50.00  |      50.00
  2 | $100.00 |     100.00
(2 rows)
```
Use ALTER TABLE to drop the default expression from the new column temp_price. The database retains the values stored in this column:
```
=> ALTER TABLE sales ALTER COLUMN temp_price DROP DEFAULT;
ALTER TABLE
```

Drop the original price column

Drop the extraneous price column. Before doing so, you must first advance the AHM to purge historical data that would otherwise prevent the drop operation:

Advance the AHM:

=> SELECT MAKE_AHM_NOW();
         MAKE_AHM_NOW
-------------------------------
 AHM set (New AHM Epoch: 6354)
(1 row)

Drop the original price column:

=> ALTER TABLE sales DROP COLUMN price CASCADE;
ALTER COLUMN

Rename the new column to the original column

You can now rename the temp_price column to price:

Use ALTER TABLE to rename the column:

=> ALTER TABLE sales RENAME COLUMN temp_price to price;

Query the sales table again:

=> SELECT * FROM sales;
 id | price
----+--------
  1 |  50.00
  2 | 100.00
(2 rows)