I have a table with 200 column, I have to drop 96 columns from this table.
Using the statement
alter table XYZ drop column a,b,c,d...................
is taking forever to drop the column on SQL Server2000
Can anyone help or give me some idea how can this be done effitiently..
thanks
This is not a trivial change to ask of any RDBMS and IS likely to take a while. Depending on the platform, it might also be exacerbated if there are a lot of rows and the columns you're dropping contain non-null data.
Perhaps it would be more feasible to SELECT the columns you want to keep into a new table, drop this one, and rename the new table to the original table name.
Answer intentionally written generically
Related
It's been a while.
Using DB2 10 for z/OS, I've been asked to change a specific column in a table from decimal(7,2) to decimal(7,4). Sounds easy, right?
alter table MySchema.MyTable
alter column myColumn
set data type decimal(7,4);
But, DB2 responds with this error: "Attributes specified for column 'MYCOLUMN' are incompatible with existing column definition."
I had thought that converting from decimal(7,2) to decimal(7,4) would be pretty straightforward, but DB2 disagrees.
Outside of dropping the table and recreating it from scratch, what alternatives do I have?
Thanks in advance!
Dave
The reason Db2 doesn't like that change is you're going from from 99999.99 to 999.9999
Is that really what you want? Going from (7,2) to (9,4) would just add two more decimal places without losing any data and should be allowed by the Db.
Db2 for i gives a warning, but allows you to ignore the warning...
Create a new column ALTER ADD COLUMN of the right type, use an UPDATE to populate it, ALTER DROP COLUMN the old column. RENAME COLUMN so set the name of the original column.
In a PostgreSQL db I'm working on, half of the tables have one particular column, always named the same, that is of type varchar(5). The size became a bit too restricting and I want to change it to varchar(10).
The number of tables in my particular case is actually very manageable to do it by hand. But I was wondering how one could script this with a query for larger dbs. It generally should be possible in just a few steps.
Identify all the tables in the schema, then (?) filter by condition if column present.
Create ALTER TABLE statements for each table found
I have some idea about how to write a query that identifies all tables in the schema. But I wouldn't know how to filter them. And if I didn't filter them, I assume the generated alter table statements would break.
Would be great if someone could share their knowledge on this.
Thanks to Abelisto for providing some guidance. Eventually, this is how I did it.
First, I created a query that in turn creates the ALTER TABLE statements. MyDB and MyColumn need to reflect actual values.
SELECT
'ALTER TABLE '||columns.table_name||' ALTER COLUMN '||MyColumn||' TYPE varchar(20);'
FROM
information_schema.columns
WHERE
columns.table_catalog = 'MyDB' AND
columns.table_schema = 'public' AND
columns.column_name = 'MyColumn';
Then it was just a matter of executing the output as a new query. All done.
Assume I have a table named tracker with columns (issue_id,ingest_date,verb,priority)
I would like to add 50 columns to this table.
Columns being (string_ch_01,string_ch_02,.....,string_ch_50) of datatype varchar.
Is there any better way to add columns with single procedure rather than executing the following alter command 50 times?
ALTER TABLE tracker ADD COLUMN string_ch_01 varchar(1020);
Yes, a better way is to issue a single ALTER TABLE with all the columns at once:
ALTER TABLE tracker
ADD COLUMN string_ch_01 varchar(1020),
ADD COLUMN string_ch_02 varchar(1020),
...
ADD COLUMN string_ch_50 varchar(1020)
;
It's especially better when there are DEFAULT non-null clauses for the new columns, since each of them would rewrite the entire table, as opposed to rewriting it only once if they're grouped in a single ALTER TABLE.
Docs for Redshift say:
ALTER TABLE locks the table for reads and writes until the operation completes.
My question is:
Say I have a table with 500 million rows and I want to add a column. This sounds like a heavy operation that could lock the table for a long time - yes? Or is it actually a quick operation since Redshift is a columnar db? Or it depends if column is nullable / has default value?
I find that adding (and dropping) columns is a very fast operation even on tables with many billions of rows, regardless of whether there is a default value or it's just NULL.
As you suggest, I believe this is a feature of the it being a columnar database so the rest of the table is undisturbed. It simply creates empty (or nearly empty) column blocks for the new column on each node.
I added an integer column with a default to a table of around 65M rows in Redshift recently and it took about a second to process. This was on a dw2.large (SSD type) single node cluster.
Just remember you can only add a column to the end (right) of the table, you have to use temporary tables etc if you want to insert a column somewhere in the middle.
Personally I have seen rebuilding the table works best.
I do it in following ways
Create a new table N_OLD_TABLE table
Define the datatype/compression encoding in the new table
Insert data into N_OLD(old_columns) select(old_columns) from old_table Rename OLD_Table to OLD_TABLE_BKP
Rename N_OLD_TABLE to OLD_TABLE
This is a much faster process. Doesn't block any table and you always have a backup of old table incase anything goes wrong
I have a need to change the length of CHAR columns in tables in a PostgreSQL v7.4 database. This version did not support the ability to directly change the column type or size using the ALTER TABLE statement. So, directly altering a column from a CHAR(10) to CHAR(20) for instance isn't possible (yeah, I know, "use varchars", but that's not an option in my current circumstance). Anyone have any advice/tricks on how to best accomplish this? My initial thoughts:
-- Save the table's data in a new "save" table.
CREATE TABLE save_data AS SELECT * FROM table_to_change;
-- Drop the columns from the first column to be changed on down.
ALTER TABLE table_to_change DROP column_name1; -- for each column starting with the first one that needs to be modified
ALTER TABLE table_to_change DROP column_name2;
...
-- Add the columns back, using the new size for the CHAR column
ALTER TABLE table_to_change ADD column_name1 CHAR(new_size); -- for each column dropped above
ALTER TABLE table_to_change ADD column_name2...
-- Copy the data bace from the "save" table
UPDATE table_to_change
SET column_name1=save_data.column_name1, -- for each column dropped/readded above
column_name2=save_date.column_name2,
...
FROM save_data
WHERE table_to_change.primary_key=save_data.primay_key;
Yuck! Hopefully there's a better way? Any suggestions appreciated. Thanks!
Not PostgreSQL, but in Oracle I have changed a column's type by:
Add a new column with a temporary name (ie: TMP_COL) and the new data type (ie: CHAR(20))
run an update query: UPDATE TBL SET TMP_COL = OLD_COL;
Drop OLD_COL
Rename TMP_COL to OLD_COL
I would dump the table contents to a flat file with COPY, drop the table, recreate it with the correct column setup, and then reload (with COPY again).
http://www.postgresql.org/docs/7.4/static/sql-copy.html
Is it acceptable to have downtime while performing this operation? Obviously what I've just described requires making the table unusable for a period of time, how long depends on the data size and hardware you're working with.
Edit: But COPY is quite a bit faster than INSERTs and UPDATEs. According to the docs you can make it even faster by using BINARY mode. BINARY makes it less compatible with other PGSQL installs but you won't care about that because you only want to load the data to the same instance that you dumped it from.
The best approach to your problem is to upgrade pg to something less archaic :)
Seriously. 7.4 is going to be removed from "supported versions" pretty soon, so I wouldn't wait for it to happen with 7.4 in production.