I have Postgres 9.4 and using json_populate_record function with type which has 60 columns defined.
The table with the json, old_table, has 100,000 records and it takes over 2 minutes to do the following parse and insert command:
select (json_populate_record(null::v_type, row_to_json_output)).*
into new_table
from from old_table
Can I optimize it in any way? Alternatives? Are newer versions better in doing it?
Thanks
In my hands, using jsonb_populate_record with jsonb values is about 40% faster than using json_populate_record with json values.
Starting in v11, this statement could run in parallel. However, the cost estimate of the function json_populate_record is very low, so to get it to actually choose parallelization, you should probably do something like:
alter function json_populate_record cost 1000;
And the same with jsonb_populate_record.
But really, why are you doing this often enough to care if it takes 2 minutes? Certainly cycling data back and forth between table to json and back to table on a routine ongoing basis surely doesn't make much sense.
Related
Postgres 13.4
I've got some pg_cron jobs set up to periodically delete older records out of log-like files. What I'd like to do is to run VACUUM ANALYZE after performing a purge. Unfortunately, I can't work out how to do this in a stored function. Am I missing a trick? Is a stored procedure more appropriate?
As an example, here's one of my purge routines
CREATE OR REPLACE FUNCTION dba.purge_event_log (
retain_days_in integer_positive default 14)
RETURNS int4
AS $BODY$
WITH -- Use a CTE so that we've got a way of returning the count easily.
deleted AS (
-- Normal-looking code for this requires a literal:
-- where your_dts < now() - INTERVAL '14 days'
-- Don't want to use a literal, SQL injection, etc.
-- Instead, using a interval constructor to achieve the same result:
DELETE
FROM dba.event_log
WHERE dts < now() - make_interval (days => $1)
RETURNING *
),
----------------------------------------
-- Save details to a custom log table
----------------------------------------
logit AS (
insert into dba.event_log (name, details)
values ('purge_event_log(' || retain_days_in::text || ')',
'count = ' || (select count(*)::text from deleted)
)
)
----------------------------------------
-- Return result count
----------------------------------------
select count(*) from deleted;
$BODY$
LANGUAGE sql;
COMMENT ON FUNCTION dba.purge_event_log (integer_positive) IS
'Delete dba.event_log records older than the day count passed in, with a default retention period of 14 days.';
The truth is, I don't really care about the count(*) result from this routine, in this case. But I might want a result and an additional action in some other, similar context. As you can see, the routine deletes records, uses a CTE to insert a report into another table, and then returns a result. No matter what, I figure this example is a good way to get me head around the alternatives and options in stored functions. The main thing I want to achieve here is to delete records, and then run maintenance. if this is an awkward fit for a stored function or procedure, I could write out an entry to a vacuum_list table with the table name, and have another job to run though that list.
If there's a smarter way to approach vacuum without the extra, I'm of course interested in that. But I'm also interested in understanding the limits on what operationa you can combine in PL/PgSQL routines.
Pavel Stehule' answer is correct and complete. I decided to follow-up a bit here as I like to dig in on bugs in my code, behaviors in Postgres, etc. to get a better sense of what I'm dealing with. I'm including some notes below for anyone who finds them of use.
COMMAND cannot be executed...
The reference to "VACUUM cannot be executed inside a transaction block" gave me a better way to search the docs for similarly restricted commands. The information below probably doesn't cover everything, but it's a start.
Command Limitation
CREATE DATABASE
ALTER DATABASE If creating a new table space.
DROP DATABASE
CLUSTER Without any parameters.
CREATE TABLESPACE
DROP TABLESPACE
REINDEX All in system catalogs, database, or schema.
CREATE SUBSCRIPTION When creating a replication slot (the default behavior.)
ALTER SUBSCRIPTION With refresh option as true.
DROP SUBSCRIPTION If the subscription is associated with a replication slot.
COMMIT PREPARED
ROLLBACK PREPARED
DISCARD ALL
VACUUM
The accepted answer indicates that the limitation has nothing to do with the specific server-side language used. I've just come across an older thread that has some excellent explanations and links for stored functions and transactions:
Do stored procedures run in database transaction in Postgres?
Sample Code
I also wondered about stored procedures, as they're allowed to control transactions. I tried them out in PG 13 and, no, the code is treated like a stored function, down to the error messages.
For anyone that goes in for this sort of thing, here are the "hello world" samples of sQL and PL/PgSQL stored functions and procedures to test out how VACCUM behaves in these cases. Spoiler: It doesn't work, as advertised.
SQL Function
/*
select * from dba.vacuum_sql_function();
Fails:
ERROR: VACUUM cannot be executed from a function
CONTEXT: SQL function "vacuum_sql_function" statement 1. 0.000 seconds. (Line 13).
*/
DROP FUNCTION IF EXISTS dba.vacuum_sql_function();
CREATE FUNCTION dba.vacuum_sql_function()
RETURNS VOID
LANGUAGE sql
AS $sql_code$
VACUUM ANALYZE activity;
$sql_code$;
select * from dba.vacuum_sql_function(); -- Fails.
PL/PgSQL Function
/*
select * from dba.vacuum_plpgsql_function();
Fails:
ERROR: VACUUM cannot be executed from a function
CONTEXT: SQL statement "VACUUM ANALYZE activity"
PL/pgSQL function vacuum_plpgsql_function() line 4 at SQL statement. 0.000 seconds. (Line 22).
*/
DROP FUNCTION IF EXISTS dba.vacuum_plpgsql_function();
CREATE FUNCTION dba.vacuum_plpgsql_function()
RETURNS VOID
LANGUAGE plpgsql
AS $plpgsql_code$
BEGIN
VACUUM ANALYZE activity;
END
$plpgsql_code$;
select * from dba.vacuum_plpgsql_function();
SQL Procedure
/*
call dba.vacuum_sql_procedure();
ERROR: VACUUM cannot be executed from a function
CONTEXT: SQL function "vacuum_sql_procedure" statement 1. 0.000 seconds. (Line 20).
*/
DROP PROCEDURE IF EXISTS dba.vacuum_sql_procedure();
CREATE PROCEDURE dba.vacuum_sql_procedure()
LANGUAGE SQL
AS $sql_code$
VACUUM ANALYZE activity;
$sql_code$;
call dba.vacuum_sql_procedure();
PL/PgSQL Procedure
/*
call dba.vacuum_plpgsql_procedure();
ERROR: VACUUM cannot be executed from a function
CONTEXT: SQL statement "VACUUM ANALYZE activity"
PL/pgSQL function vacuum_plpgsql_procedure() line 4 at SQL statement. 0.000 seconds. (Line 23).
*/
DROP PROCEDURE IF EXISTS dba.vacuum_plpgsql_procedure();
CREATE PROCEDURE dba.vacuum_plpgsql_procedure()
LANGUAGE plpgsql
AS $plpgsql_code$
BEGIN
VACUUM ANALYZE activity;
END
$plpgsql_code$;
call dba.vacuum_plpgsql_procedure();
Other Options
Plenty. As I understand it, VACUUM, and a handful of other commands, are not supported in server-side code running within Postgres. Therefore, you code needs to start from somewhere else. That can be:
Whatever cron you've got in your server's OS.
Any exteral client you like.
pg_cron.
As we're deployed on RDS, those last two options are where I'll look. And there's one more:
Let AUTOVACCUM and an occasional VACCUM do their thing.
That's pretty easy to do, and seems to work fine for the bulk of our needs.
Another Idea
If you do want a bit more control and some custom logging, I'm imagining a table like this:
CREATE TABLE IF NOT EXISTS dba.vacuum_list (
database_name text,
schema_name text,
table_name text,
run boolean,
run_analyze boolean,
run_full boolean,
last_run_dts timestamp)
ALTER TABLE dba.vacuum_list ADD CONSTRAINT
vacuum_list_pk
PRIMARY KEY (database_name, schema_name, table_name);
That's just a sketch. The idea is like this:
You INSERT into vacuum_list when a table needs some vacuuming, at least as far as you're concerned.
In my case, that would be an UPSERT as I don't need a full log-like table, just a single row per table of interest with the last outcome and/or pending state.
Periodically, a remote client, etc. connects, reads the table, and executes each specified VACUUM, according to the options specified in the record.
The external client updates the row with the last run timestamp, and whatever else you're including in the row.
Optionally, you could include fields for duration and change in relation size pre:post vacuuming.
That last option is what I'm interested in. None of our VACUUM calls were working for quite some time as there was a months-old dead connection from something sever-side. VACUUM appears to run fine, in such a case, it just can't delete a whole lot of rows. (Because of the super old "open" transaction ID, visibility maps, etc.) The only way to see this sort of thing seems to be to VACUUM VERBOSE and study the output. Or to record vacuum time and, more important, relation size change to flag cases where nothing seems to happen, when it seems like it should.
VACUUM is "top level" command. It cannot be executed from PL/pgSQL ever or from any other PL.
I have been working with postgis recently,and in my query if I use ST_GeomFromText it execute faster than running a sub-query to get geom.
I thought ST_GeomFromText will be more expensive but after running many tests every time I got the result faster, my question Is there any explanation behind this?
because for me getting the geom directly in sub-query is better than getting geom as text then added as GeomFromText.
Thanks,
Sara
Your issue is that the issues are different. ST_GeomFromText is going to be an immutable function, so the output depends on the input only. This means the planner can execute it once at the beginning of the query. Running a subquery means you are having to look up the geometry, which means disk access, etc. In the first, you have a little bit of CPU activity, executed once for the query, and on the second you have disk lookups.
So the answer to some extent depends on what you are doing with it. In general, you can assume that the optimizer will handle things like type conversions on input, where those are not dependent on settings, quite well.
Think about it this way.
SELECT * FROM mytable WHERE my_geom = ST_GeomFromText(....);
This gets converted into something like the following pseudocode:
private_geom = ST_GeomFromText(....);
SELECT * FROM mytable WHERE my_geom = private_geom;
Then that query gets planned and executed.
Obviously you don't want to be adding round trips just to avoid in-query lookups, but where you know the geometry, you might as well specify it via ST_GeomFromText(....) in your query.
I am working on third party data which I need to load into my postgresql database. I am running into problems where sometimes I get the time '24:00:30' when it actually should be '00:00:30'. This rejects the data.
I tried to cast but it did not work.
insert into stop_times_test trip_id, cast(arrival_time as time), feed_id, status
from external_source;
Is there any way to convert to the correct one internally?
This may work for your case:
> select '0:0:0'::time + '24:00:30'::interval;
00:00:30
Cast to interval, then cast to time:
SELECT '24:00:30'::interval::time
If you want to bulk load the data with COPY or mass INSERT make the target column data type interval and convert it to time later. This works out of the box:
ALTER TABLE mytable ALTER col1 TYPE time;
No, there is no magic way of doing it. No cast will help you. 24:00:30 is an invalid time. Period.
You could try adding that value on a varchar and then using regular expressions to update the right values and insert them on the right columns. This sort of things happen a lot when doing data transformation.
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.
I use both Firebird embedded and Firebird Server, and from time to time I need to reindex the tables using a procedure like the following:
CREATE PROCEDURE MAINTENANCE_SELECTIVITY
ASDECLARE VARIABLE S VARCHAR(200);
BEGIN
FOR select RDB$INDEX_NAME FROM RDB$INDICES INTO :S DO
BEGIN
S = 'SET statistics INDEX ' || s || ';';
EXECUTE STATEMENT :s;
END
SUSPEND;
END
I guess this is normal using embedded, but is it really needed using a server? Is there a way to configure the server to do it automatically when required or periodically?
First, let me point out that I'm no Firebird expert, so I'm answering on the basis of how SQL Server works.
In that case, the answer is both yes, and no.
The indexes are of course updated on SQL Server, in the sense that if you insert a new row, all indexes for that table will contain that row, so it will be found. So basically, you don't need to keep reindexing the tables for that part to work. That's the "no" part.
The problem, however, is not with the index, but with the statistics. You're saying that you need to reindex the tables, but then you show code that manipulates statistics, and that's why I'm answering.
The short answer is that statistics goes slowly out of whack as time goes by. They might not deteriorate to a point where they're unusable, but they will deteriorate down from the perfect level they're in when you recreate/recalculate them. That's the "yes" part.
The main problem with stale statistics is that if the distribution of the keys in the indexes changes drastically, the statistics might not pick that up right away, and thus the query optimizer will pick the wrong indexes, based on the old, stale, statistics data it has on hand.
For instance, let's say one of your indexes has statistics that says that the keys are clumped together in one end of the value space (for instance, int-column with lots of 0's and 1's). Then you insert lots and lots of rows with values that make this index contain values spread out over the entire spectrum.
If you now do a query that uses a join from another table, on a column with low selectivity (also lots of 0's and 1's) against the table with this index of yours, the query optimizer might deduce that this index is good, since it will fetch many rows that will be used at the same time (they're on the same data page).
However, since the data has changed, it'll jump all over the index to find the relevant pieces, and thus not be so good after all.
After recalculating the statistics, the query optimizer might see that this index is sub-optimal for this query, and pick another index instead, which is more suited.
Basically, you need to recalculate the statistics periodically if your data is in flux. If your data rarely changes, you probably don't need to do it very often, but I would still add a maintenance job with some regularity that does this.
As for whether or not it is possible to ask Firebird to do it on its own, then again, I'm on thin ice, but I suspect there is. In SQL Server you can set up maintenance jobs that does this, on a schedule, and at the very least you should be able to kick off a batch file from the Windows scheduler to do something like it.
That does not reindex, it recomputes weights for indexes, which are used by optimizer to select most optimal index. You don't need to do that unless index size changes a lot. If you create the index before you add data, you need to do the recalculation.
Embedded and Server should have exactly same functionality apart the process model.
I wanted to update this answer for newer firebird. here is the updated dsql.
SET TERM ^ ;
CREATE OR ALTER PROCEDURE NEW_PROCEDURE
AS
DECLARE VARIABLE S VARCHAR(300);
begin
FOR select 'SET statistics INDEX ' || RDB$INDEX_NAME || ';'
FROM RDB$INDICES
WHERE RDB$INDEX_NAME <> 'PRIMARY' INTO :S
DO BEGIN
EXECUTE STATEMENT :s;
END
end^
SET TERM ; ^
GRANT EXECUTE ON PROCEDURE NEW_PROCEDURE TO SYSDBA;