I'm trying to insert data into a PostgreSQL table using a nested SQL statement. I'm finding that my inserts work with a small (a few thousand) rows being returned from the nested query. For instance, when I attempt:
insert into the_target_table (a_few_columns, average_metric)
SELECT a_few_columns, AVG(a_metric)
FROM a table
GROUP BY a_few_columns LIMIT 5000)
However, this same query fails when I remove my LIMIT (the inner query without limit returns about 30,000 rows):
ERROR: Integer out of range
a_metric is a double precision, and a_few_columns are text. I've played around with the LIMIT rows, and it seems like the # of rows it can insert without throwing an error is 14,000. I don't know if this is non-deterministic, or a constant threshold before the error is thrown.
I've looked through a few other SO posts on this topic, including this one, and changed my table primary key data type to BIGINT. I still get the same error. I don't think it's an issue w/ numerical overflow, however, as the number of inserts I'm making is small and nowhere even close to hitting the threshold.
Anyone have any clues what is causing this error?
The issue here was an improper definition of the avg_metric field in my table that I wanted to insert it into. I accidentally had defined it as an integer. This normally isn’t a huge issue, but I also had a handful of infinity values ( inf). Once I switched my field data type to double precision I was able to insert successfully. Of course, it’s probably best if my application had checked beforehand for finite values prior to attempting the insert- normally I’d do this programmatically via asserts, but with a nested query I hadn’t bothered to check.
The final query I used was
insert into the_target_table (a_few_columns, average_metric)
SELECT a_few_columns, CASE WHEN AVG(a_metric) = 'inf' THEN NULL ELSE AVG(a_metric) END
FROM a_table
GROUP BY a_few_columns LIMIT 5000)
An even better solution would have been to go through a_table and replace all inf values first.
I would like to know what gets executed first in the SAS SQL pass thru in this code:
Connect To OLEDB As MYDB ( %DBConnect( Catalog = MYDB ) ) ;
Create table MYDB_extract as
select put(Parent,$ABC.) as PARENT,
put(PFX,z2.) as PFX,*
From Connection To MYDB
( SELECT
Appointment,Parents,Children,Cats,Dogs
FROM MYDB.dbo.FlatRecord
WHERE Appointment between '20150801' and '20150831'
And Children > 2);
Disconnect from MYDB;
Since MS SQL-Server doesn't support the PUT function will this query cause ALL of the records to be processed locally or only the resultant records from the DBMS?
The explicit pass-through query will still process and will return to SAS what it returns (however many records that is). Then, SAS will perform the put operations on the returned rows.
So if 10000 rows are in the table, and 500 rows meet the criteria in where, 500 records will go to SAS and then be put; SQL will handle the 10000 -> 500.
If you had written this in implicit pass through, then it's possible (if not probable) that SAS might have done all of the work.
First the code in the inline view will be executed on the server:
SELECT Appointment,Parents,Children,Cats,Dogs
FROM MYDB.dbo.FlatRecord
WHERE Appointment between '20150801' and '20150831' And Children > 2
Rows that meet that WHERE clause will be returned by the DBMS to SAS over the OLDEB connection.
Then SAS will (try and) select from that result set, applying any other code, including the put functions.
This isn't really any different from how an inline view works in any other DBMS, except that here you have two different database engines, one running the inner query and SAS running the outer query.
1.I want to write a DB2 procedure to do common insert/update/delete to a table, problem is how to generate SQL statement with random values? for example, if a column of integer type, the store procedure could generate numbers between 1 to 10000, or for a column of varchar type, the store procedure could generate string of random chosen characters with a fixed length,say 10;
2.if the DB2 SQL syntax support sth to put the data from file into a LOB column for a randomly chosen row, say, I have a table t1(c0 integer,c1 clob), then how could I do sth like "insert into t1 values(100，some_path_to_a_text_file)" ?
3.using DB2 "import" to load data, if the file contains 10000 rows,it seems DB2 by default will commit the entire 10000 rows of insertion in one single transaction. Is there any configuration/option I could use to divide the "import" process into like 10 transaction, each with 1000 rows?
Thank you very much!
1) To do a random operation, get a random value, and process it according to set of rules. I have a similar case in an utility I am currently developping.
https://github.com/angoca/log4db2/blob/master/src/examples/sql-pl/bank/DemoBankRandom.sql
It realizes an insert, a select, an update or a delete based on a random value.
2) No idea. What is sth?
3) For more frequent commits, you put commitcount. For more info please check the infoCenter http://publib.boulder.ibm.com/infocenter/db2luw/v10r1/topic/com.ibm.db2.luw.admin.cmd.doc/doc/r0008304.html
I am migrating a large database from oracle 11g to SQL SERVER 2008R2 using SSIS. How can the data integrity can be validated for numeric data using checksum?
In the past, I've always done this using a few application controls. It should be something that is easy to compute on both platforms.
Frequently, the end result is a query like:
select count(*)
, count(distinct col1) col1_dist_cnt
...
, count(distinct col99) col99_dist_cnt
, sum(col1) col1_sum
...
, sum(col99) col99_sum
from table
Spool to file, Excel or database and compare outcome. Save for project management and auditors.
Please read more on application control here. I wrote it for checks between various financial reporting systems for the regulatory reporting, so this approach serves most cases.
If exactly one field value is wrong, it will always show up. Two errors might compensate each other. For example row 1 col 1 gets the value from row 2 col 1.
To detect for that, multiply each value with something unique for the row. For instance, if you have a unique ID column or identity that is included in the migration too:
, sum(ID * col1) col1_sum
...
, sum(ID * col99) col99_sum
When you get number overflows, first try using the largest available precision (especially on SQL Server sometimes difficult). If not feasibly anymore, use mod function. Only few types of error are hidden by mod.
Icing on the cake is to auto generate these statements. On Oracle look at user_tables, user_tab_columns. On SQL Server look at syscolumns etc.
I want to do a large update on a table in PostgreSQL, but I don't need the transactional integrity to be maintained across the entire operation, because I know that the column I'm changing is not going to be written to or read during the update. I want to know if there is an easy way in the psql console to make these types of operations faster.
For example, let's say I have a table called "orders" with 35 million rows, and I want to do this:
UPDATE orders SET status = null;
To avoid being diverted to an offtopic discussion, let's assume that all the values of status for the 35 million columns are currently set to the same (non-null) value, thus rendering an index useless.
The problem with this statement is that it takes a very long time to go into effect (solely because of the locking), and all changed rows are locked until the entire update is complete. This update might take 5 hours, whereas something like
UPDATE orders SET status = null WHERE (order_id > 0 and order_id < 1000000);
might take 1 minute. Over 35 million rows, doing the above and breaking it into chunks of 35 would only take 35 minutes and save me 4 hours and 25 minutes.
I could break it down even further with a script (using pseudocode here):
for (i = 0 to 3500) {
db_operation ("UPDATE orders SET status = null
WHERE (order_id >" + (i*1000)"
+ " AND order_id <" + ((i+1)*1000) " + ")");
}
This operation might complete in only a few minutes, rather than 35.
So that comes down to what I'm really asking. I don't want to write a freaking script to break down operations every single time I want to do a big one-time update like this. Is there a way to accomplish what I want entirely within SQL?
Column / Row
... I don't need the transactional integrity to be maintained across
the entire operation, because I know that the column I'm changing is
not going to be written to or read during the update.
Any UPDATE in PostgreSQL's MVCC model writes a new version of the whole row. If concurrent transactions change any column of the same row, time-consuming concurrency issues arise. Details in the manual. Knowing the same column won't be touched by concurrent transactions avoids some possible complications, but not others.
Index
To avoid being diverted to an offtopic discussion, let's assume that
all the values of status for the 35 million columns are currently set
to the same (non-null) value, thus rendering an index useless.
When updating the whole table (or major parts of it) Postgres never uses an index. A sequential scan is faster when all or most rows have to be read. On the contrary: Index maintenance means additional cost for the UPDATE.
Performance
For example, let's say I have a table called "orders" with 35 million
rows, and I want to do this:
UPDATE orders SET status = null;
I understand you are aiming for a more general solution (see below). But to address the actual question asked: This can be dealt with in a matter milliseconds, regardless of table size:
ALTER TABLE orders DROP column status
, ADD column status text;
The manual (up to Postgres 10):
When a column is added with ADD COLUMN, all existing rows in the table
are initialized with the column's default value (NULL if no DEFAULT
clause is specified). If there is no DEFAULT clause, this is merely a metadata change [...]
The manual (since Postgres 11):
When a column is added with ADD COLUMN and a non-volatile DEFAULT
is specified, the default is evaluated at the time of the statement
and the result stored in the table's metadata. That value will be used
for the column for all existing rows. If no DEFAULT is specified,
NULL is used. In neither case is a rewrite of the table required.
Adding a column with a volatile DEFAULT or changing the type of an
existing column will require the entire table and its indexes to be
rewritten. [...]
And:
The DROP COLUMN form does not physically remove the column, but
simply makes it invisible to SQL operations. Subsequent insert and
update operations in the table will store a null value for the column.
Thus, dropping a column is quick but it will not immediately reduce
the on-disk size of your table, as the space occupied by the dropped
column is not reclaimed. The space will be reclaimed over time as
existing rows are updated.
Make sure you don't have objects depending on the column (foreign key constraints, indices, views, ...). You would need to drop / recreate those. Barring that, tiny operations on the system catalog table pg_attribute do the job. Requires an exclusive lock on the table which may be a problem for heavy concurrent load. (Like Buurman emphasizes in his comment.) Baring that, the operation is a matter of milliseconds.
If you have a column default you want to keep, add it back in a separate command. Doing it in the same command applies it to all rows immediately. See:
Add new column without table lock?
To actually apply the default, consider doing it in batches:
Does PostgreSQL optimize adding columns with non-NULL DEFAULTs?
General solution
dblink has been mentioned in another answer. It allows access to "remote" Postgres databases in implicit separate connections. The "remote" database can be the current one, thereby achieving "autonomous transactions": what the function writes in the "remote" db is committed and can't be rolled back.
This allows to run a single function that updates a big table in smaller parts and each part is committed separately. Avoids building up transaction overhead for very big numbers of rows and, more importantly, releases locks after each part. This allows concurrent operations to proceed without much delay and makes deadlocks less likely.
If you don't have concurrent access, this is hardly useful - except to avoid ROLLBACK after an exception. Also consider SAVEPOINT for that case.
Disclaimer
First of all, lots of small transactions are actually more expensive. This only makes sense for big tables. The sweet spot depends on many factors.
If you are not sure what you are doing: a single transaction is the safe method. For this to work properly, concurrent operations on the table have to play along. For instance: concurrent writes can move a row to a partition that's supposedly already processed. Or concurrent reads can see inconsistent intermediary states. You have been warned.
Step-by-step instructions
The additional module dblink needs to be installed first:
How to use (install) dblink in PostgreSQL?
Setting up the connection with dblink very much depends on the setup of your DB cluster and security policies in place. It can be tricky. Related later answer with more how to connect with dblink:
Persistent inserts in a UDF even if the function aborts
Create a FOREIGN SERVER and a USER MAPPING as instructed there to simplify and streamline the connection (unless you have one already).
Assuming a serial PRIMARY KEY with or without some gaps.
CREATE OR REPLACE FUNCTION f_update_in_steps()
RETURNS void AS
$func$
DECLARE
_step int; -- size of step
_cur int; -- current ID (starting with minimum)
_max int; -- maximum ID
BEGIN
SELECT INTO _cur, _max min(order_id), max(order_id) FROM orders;
-- 100 slices (steps) hard coded
_step := ((_max - _cur) / 100) + 1; -- rounded, possibly a bit too small
-- +1 to avoid endless loop for 0
PERFORM dblink_connect('myserver'); -- your foreign server as instructed above
FOR i IN 0..200 LOOP -- 200 >> 100 to make sure we exceed _max
PERFORM dblink_exec(
$$UPDATE public.orders
SET status = 'foo'
WHERE order_id >= $$ || _cur || $$
AND order_id < $$ || _cur + _step || $$
AND status IS DISTINCT FROM 'foo'$$); -- avoid empty update
_cur := _cur + _step;
EXIT WHEN _cur > _max; -- stop when done (never loop till 200)
END LOOP;
PERFORM dblink_disconnect();
END
$func$ LANGUAGE plpgsql;
Call:
SELECT f_update_in_steps();
You can parameterize any part according to your needs: the table name, column name, value, ... just be sure to sanitize identifiers to avoid SQL injection:
Table name as a PostgreSQL function parameter
Avoid empty UPDATEs:
How do I (or can I) SELECT DISTINCT on multiple columns?
Postgres uses MVCC (multi-version concurrency control), thus avoiding any locking if you are the only writer; any number of concurrent readers can work on the table, and there won't be any locking.
So if it really takes 5h, it must be for a different reason (e.g. that you do have concurrent writes, contrary to your claim that you don't).
You should delegate this column to another table like this:
create table order_status (
order_id int not null references orders(order_id) primary key,
status int not null
);
Then your operation of setting status=NULL will be instant:
truncate order_status;
First of all - are you sure that you need to update all rows?
Perhaps some of the rows already have status NULL?
If so, then:
UPDATE orders SET status = null WHERE status is not null;
As for partitioning the change - that's not possible in pure sql. All updates are in single transaction.
One possible way to do it in "pure sql" would be to install dblink, connect to the same database using dblink, and then issue a lot of updates over dblink, but it seems like overkill for such a simple task.
Usually just adding proper where solves the problem. If it doesn't - just partition it manually. Writing a script is too much - you can usually make it in a simple one-liner:
perl -e '
for (my $i = 0; $i <= 3500000; $i += 1000) {
printf "UPDATE orders SET status = null WHERE status is not null
and order_id between %u and %u;\n",
$i, $i+999
}
'
I wrapped lines here for readability, generally it's a single line. Output of above command can be fed to psql directly:
perl -e '...' | psql -U ... -d ...
Or first to file and then to psql (in case you'd need the file later on):
perl -e '...' > updates.partitioned.sql
psql -U ... -d ... -f updates.partitioned.sql
I am by no means a DBA, but a database design where you'd frequently have to update 35 million rows might have… issues.
A simple WHERE status IS NOT NULL might speed up things quite a bit (provided you have an index on status) – not knowing the actual use case, I'm assuming if this is run frequently, a great part of the 35 million rows might already have a null status.
However, you can make loops within the query via the LOOP statement. I'll just cook up a small example:
CREATE OR REPLACE FUNCTION nullstatus(count INTEGER) RETURNS integer AS $$
DECLARE
i INTEGER := 0;
BEGIN
FOR i IN 0..(count/1000 + 1) LOOP
UPDATE orders SET status = null WHERE (order_id > (i*1000) and order_id <((i+1)*1000));
RAISE NOTICE 'Count: % and i: %', count,i;
END LOOP;
RETURN 1;
END;
$$ LANGUAGE plpgsql;
It can then be run by doing something akin to:
SELECT nullstatus(35000000);
You might want to select the row count, but beware that the exact row count can take a lot of time. The PostgreSQL wiki has an article about slow counting and how to avoid it.
Also, the RAISE NOTICE part is just there to keep track on how far along the script is. If you're not monitoring the notices, or do not care, it would be better to leave it out.
Are you sure this is because of locking? I don't think so and there's many other possible reasons. To find out you can always try to do just the locking. Try this:
BEGIN;
SELECT NOW();
SELECT * FROM order FOR UPDATE;
SELECT NOW();
ROLLBACK;
To understand what's really happening you should run an EXPLAIN first (EXPLAIN UPDATE orders SET status...) and/or EXPLAIN ANALYZE. Maybe you'll find out that you don't have enough memory to do the UPDATE efficiently. If so, SET work_mem TO 'xxxMB'; might be a simple solution.
Also, tail the PostgreSQL log to see if some performance related problems occurs.
I would use CTAS:
begin;
create table T as select col1, col2, ..., <new value>, colN from orders;
drop table orders;
alter table T rename to orders;
commit;
Some options that haven't been mentioned:
Use the new table trick. Probably what you'd have to do in your case is write some triggers to handle it so that changes to the original table also go propagated to your table copy, something like that... (percona is an example of something that does it the trigger way). Another option might be the "create a new column then replace the old one with it" trick, to avoid locks (unclear if helps with speed).
Possibly calculate the max ID, then generate "all the queries you need" and pass them in as a single query like update X set Y = NULL where ID < 10000 and ID >= 0; update X set Y = NULL where ID < 20000 and ID > 10000; ... then it might not do as much locking, and still be all SQL, though you do have extra logic up front to do it :(
PostgreSQL version 11 handles this for you automatically with the Fast ALTER TABLE ADD COLUMN with a non-NULL default feature. Please do upgrade to version 11 if possible.
An explanation is provided in this blog post.