Thread: update faster way
Hello,
We have to update a column value(from numbers like '123' to codes like 'abc' by looking into a reference table data) in a partitioned table with billions of rows in it, with each partition having 100's millions rows. As we tested for ~30million rows it's taking ~20minutes to update. So if we go by this calculation, it's going to take days for updating all the values. So my question is
1) If there is any inbuilt way of running the update query in parallel (e.g. using parallel hints etc) to make it run faster?
2) should we run each individual partition in a separate session (e.g. five partitions will have the updates done at same time from 5 different sessions)? And will it have any locking effect or we can just start the sessions and let them run without impacting our live transactions?
UPDATE tab_part1
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = subquery.column1;
We have to update a column value(from numbers like '123' to codes like 'abc' by looking into a reference table data) in a partitioned table with billions of rows in it, with each partition having 100's millions rows. As we tested for ~30million rows it's taking ~20minutes to update. So if we go by this calculation, it's going to take days for updating all the values. So my question is
1) If there is any inbuilt way of running the update query in parallel (e.g. using parallel hints etc) to make it run faster?
2) should we run each individual partition in a separate session (e.g. five partitions will have the updates done at same time from 5 different sessions)? And will it have any locking effect or we can just start the sessions and let them run without impacting our live transactions?
UPDATE tab_part1
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = subquery.column1;
Regards
Yudhi
Hii, On Fri, Sep 13, 2024 at 10:22 PM yudhi s <learnerdatabase99@gmail.com> wrote: > > Hello, > We have to update a column value(from numbers like '123' to codes like 'abc' by looking into a reference table data) ina partitioned table with billions of rows in it, with each partition having 100's millions rows. As we tested for ~30millionrows it's taking ~20minutes to update. So if we go by this calculation, it's going to take days for updating allthe values. So my question is > > 1) If there is any inbuilt way of running the update query in parallel (e.g. using parallel hints etc) to make it run faster? > 2) should we run each individual partition in a separate session (e.g. five partitions will have the updates done at sametime from 5 different sessions)? And will it have any locking effect or we can just start the sessions and let them runwithout impacting our live transactions? Do you have any indexes? If not - you should, if yes - what are they? Thank you. > > UPDATE tab_part1 > SET column1 = reftab.code > FROM reference_tab reftab > WHERE tab_part1.column1 = subquery.column1; > > Regards > Yudhi
Do you have any indexes?
If not - you should, if yes - what are they?
Yes we have a primary key on this table which is on a UUID type column and also we have other indexes in other timestamp columns . But how is this going to help as we are going to update almost all the rows in the table?
On Sat, 2024-09-14 at 08:43 +0530, yudhi s wrote: > We have to update a column value(from numbers like '123' to codes like 'abc' > by looking into a reference table data) in a partitioned table with billions > of rows in it, with each partition having 100's millions rows. As we tested > for ~30million rows it's taking ~20minutes to update. So if we go by this > calculation, it's going to take days for updating all the values. So my > question is > > 1) If there is any inbuilt way of running the update query in parallel > (e.g. using parallel hints etc) to make it run faster? > 2) should we run each individual partition in a separate session (e.g. five > partitions will have the updates done at same time from 5 different > sessions)? And will it have any locking effect or we can just start the > sessions and let them run without impacting our live transactions? Option 1 doesn't exist. Option 2 is possible, and you can even have more than one session workingr on a single partition. However, the strain on your system's resources and particularly the row locks will impair normal database work. Essentially, you can either take an extended down time or perform the updates in very small chunks with a very low "lock_timeout" over a very long period of time. If any of the batches fails because of locking conflicts, it has to be retried. Investigate with EXPLAIN (ANALYZE) why the updates take that long. It could be a lame disk, tons of (unnecessary?) indexes or triggers, but it might as well be the join with the lookup table, so perhaps there is room for improvement (more "work_mem" for a hash join?). Yours, Laurenz Albe
On Sat, 14 Sept, 2024, 1:09 pm Laurenz Albe, <laurenz.albe@cybertec.at> wrote:
On Sat, 2024-09-14 at 08:43 +0530, yudhi s wrote:
> We have to update a column value(from numbers like '123' to codes like 'abc'
> by looking into a reference table data) in a partitioned table with billions
> of rows in it, with each partition having 100's millions rows. As we tested
> for ~30million rows it's taking ~20minutes to update. So if we go by this
> calculation, it's going to take days for updating all the values. So my
> question is
>
> 1) If there is any inbuilt way of running the update query in parallel
> (e.g. using parallel hints etc) to make it run faster?
> 2) should we run each individual partition in a separate session (e.g. five
> partitions will have the updates done at same time from 5 different
> sessions)? And will it have any locking effect or we can just start the
> sessions and let them run without impacting our live transactions?
Option 1 doesn't exist.
Option 2 is possible, and you can even have more than one session workingr
on a single partition.
However, the strain on your system's resources and particularly the row
locks will impair normal database work.
Essentially, you can either take an extended down time or perform the updates
in very small chunks with a very low "lock_timeout" over a very long period
of time. If any of the batches fails because of locking conflicts, it has
to be retried.
Investigate with EXPLAIN (ANALYZE) why the updates take that long. It could
be a lame disk, tons of (unnecessary?) indexes or triggers, but it might as
well be the join with the lookup table, so perhaps there is room for
improvement (more "work_mem" for a hash join?)
Thank you so much Laurenz.
We have mostly insert/update happen on current day/live partition. So considering that, if we will run batch updates(with batch size of 1000) from five different sessions in parallel on different historical partition, at any time they will lock 5000 rows and then commit. And also those rows will not collide with each other. So do you think that approach can anyway cause locking issues? We will ensure the update of live partition occurs when we have least activity. So in that way we will not need extended down time. Please correct me if wrong.
Never used lock_timeout though, but in above case do we need lock_timeout?
Regarding batch update with batch size of 1000, do we have any method exists in postgres (say like forall statement in Oracle) which will do the batch dml. Can you please guide me here, how we can do it in postgres.
And yes will need to see what happens in the update using explain analyze. And I was trying to see, if we can run explain analyze without doing actual update , but seems that is not possible.
On Fri, Sep 13, 2024 at 11:59 PM yudhi s <learnerdatabase99@gmail.com> wrote:
Do you have any indexes?
If not - you should, if yes - what are they?Yes we have a primary key on this table which is on a UUID type column and also we have other indexes in other timestamp columns . But how is this going to help as we are going to update almost all the rows in the table?
But do you have an index on tab_part1.column1?
And how slow is subquery?
On Sat, Sep 14, 2024 at 4:55 PM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
Which in turn means that you want as little overhead as possible per
batch which means finding those 5000 rows should be quick. Which brings
us back to Igor's question: Do you have any indexes in place which speed
up finding those 5000 rows (the primary key almost certainly won't help
with that). EXPLAIN (ANALYZE) (as suggested by Laurenz) will certainly
help answering that question.
> And also those rows will not collide with each other. So do you think
> that approach can anyway cause locking issues?
No, I don't think so. With a batch size that small I wouldn't expect
problems even on the live partition. But of course many busy parallel
sessions will put additional load on the system which may or may not be
noticeable by users (you might saturate the disks writing WAL entries
for example, which would slow down other sessions trying to commit).
> Regarding batch update with batch size of 1000, do we have any method exists in
> postgres (say like forall statement in Oracle) which will do the batch dml. Can
> you please guide me here, how we can do it in postgres.
Postgres offers several server side languages. As an Oracle admin you
will probably find PL/pgSQL most familiar. But you could also use Perl
or Python or several others. And of course you could use any
programming/scripting language you like on the client side.
When you said "(the primary key almost certainly won't help with that)", I am trying to understand why it is so ?
I was thinking of using that column as an incrementing filter and driving the eligible rows based on that filter. And if it would have been a sequence. I think it would have helped but in this case it's UUID , so I may not be able to do the batch DML using that as filter criteria. but in that case will it be fine to drive the update based on ctid something as below? Each session will have the range of 5 days of data or five partition data and will execute a query something as below which will update in the batches of 10K and then commit. Is this fine? Or is there some better way of doing the batch DML in postgres plpgsql?
DO $$
DECLARE
l_rowid_array ctid[];
l_ctid ctid;
l_array_size INT := 10000;
l_processed INT := 0;
BEGIN
FOR l_cnt IN 0..(SELECT COUNT(*) FROM part_tab WHERE part_date > '1-sep-2024' and part_date < '5-sep-2024'
) / l_array_size LOOP
l_rowid_array := ARRAY(
SELECT ctid
FROM part_tab
WHERE part_date > '1-sep-2024' and part_date < '5-sep-2024'
LIMIT l_array_size OFFSET l_cnt * l_array_size
);
FOREACH l_ctid IN ARRAY l_rowid_array LOOP
update part_tab
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = reftab.column1
and ctid = l_ctid;
l_processed := l_processed + 1;
END LOOP;
COMMIT;
END LOOP;
END $$;
DECLARE
l_rowid_array ctid[];
l_ctid ctid;
l_array_size INT := 10000;
l_processed INT := 0;
BEGIN
FOR l_cnt IN 0..(SELECT COUNT(*) FROM part_tab WHERE part_date > '1-sep-2024' and part_date < '5-sep-2024'
) / l_array_size LOOP
l_rowid_array := ARRAY(
SELECT ctid
FROM part_tab
WHERE part_date > '1-sep-2024' and part_date < '5-sep-2024'
LIMIT l_array_size OFFSET l_cnt * l_array_size
);
FOREACH l_ctid IN ARRAY l_rowid_array LOOP
update part_tab
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = reftab.column1
and ctid = l_ctid;
l_processed := l_processed + 1;
END LOOP;
COMMIT;
END LOOP;
END $$;
On Sat, 2024-09-14 at 16:10 +0530, yudhi s wrote:
> > However, the strain on your system's resources and particularly the row
> > locks will impair normal database work.
> >
> > Essentially, you can either take an extended down time or perform the updates
> > in very small chunks with a very low "lock_timeout" over a very long period
> > of time. If any of the batches fails because of locking conflicts, it has
> > to be retried.
> >
> > Investigate with EXPLAIN (ANALYZE) why the updates take that long. It could
> > be a lame disk, tons of (unnecessary?) indexes or triggers, but it might as
> > well be the join with the lookup table, so perhaps there is room for
> > improvement (more "work_mem" for a hash join?)
>
> We have mostly insert/update happen on current day/live partition. So
> considering that, if we will run batch updates(with batch size of 1000) from
> five different sessions in parallel on different historical partition, at any
> time they will lock 5000 rows and then commit. And also those rows will not
> collide with each other. So do you think that approach can anyway cause locking
> issues?
The updates won't lock with each other. I thought that other database activity
might modify rows in these partitions. If that is not the case, you don't need
to worry about locks.
In that case I would also choose a much higher batch size.
You should make sure to back off every now and then and VACUUM the partition,
so that you avoid excessive table bloat.
> We will ensure the update of live partition occurs when we have least activity.
> So in that way we will not need extended down time. Please correct me if wrong.
That sounds right.
> Never used lock_timeout though, but in above case do we need lock_timeout?
It can be useful if your updating process is blocked by a lock from the
application. Setting the parameter to a low value will keep your update
from hanging for a long time and will throw an error instead.
Erroring out early reduces the danger of a deadlock.
> Regarding batch update with batch size of 1000, do we have any method exists
> in postgres (say like forall statement in Oracle) which will do the batch dml.
> Can you please guide me here, how we can do it in postgres.
I would certainly not perform the update row for row in PL/pgSQL code.
Perhaps something like this:
DO
$$DECLARE
i bigint;
BEGIN
FOR i IN 1..1000000 by 100000 LOOP
UPDATE tab SET ...
WHERE id >= i AND id < i + 100000;
COMMIT;
END LOOP;
END;$$;
VACUUM tab;
Then repeat for the next million rows, and so on.
> And yes will need to see what happens in the update using explain analyze.
> And I was trying to see, if we can run explain analyze without doing
> actual update , but seems that is not possible.
You can do it in a transaction and roll the transaction back.
Yours,
Laurenz Albe
The only way that I see as plausible to use a subquery, both in the query and in the setting of the variable, is that the relationship is one to one, and that there is an index that responds to the predicate
UPDATE table1 t1
SET column_value = (SELECT <value> FROM table2 t2 WHERE t2.column_relation = t1.column_relation)
WHERE (colum_relation) IN (SELECT column_relation FROM table2)
WHERE (colum_relation) IN (SELECT column_relation FROM table2)
PD: the index of being in table2
Atte
JRBM
El sáb, 14 sept 2024 a las 0:22, yudhi s (<learnerdatabase99@gmail.com>) escribió:
Hello,
We have to update a column value(from numbers like '123' to codes like 'abc' by looking into a reference table data) in a partitioned table with billions of rows in it, with each partition having 100's millions rows. As we tested for ~30million rows it's taking ~20minutes to update. So if we go by this calculation, it's going to take days for updating all the values. So my question is
1) If there is any inbuilt way of running the update query in parallel (e.g. using parallel hints etc) to make it run faster?
2) should we run each individual partition in a separate session (e.g. five partitions will have the updates done at same time from 5 different sessions)? And will it have any locking effect or we can just start the sessions and let them run without impacting our live transactions?
UPDATE tab_part1
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = subquery.column1;RegardsYudhi
Hi,
You can solve this problem using Citus in PostgreSQL, which is specifically designed for parallelism
SELECT create_distributed_table('tab_part1', 'partition_key');
SELECT create_distributed_table('reference_tab', 'reference_key');
UPDATE tab_part1
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = reftab.column1;
On Sat, 14 Sept 2024 at 08:22, yudhi s <learnerdatabase99@gmail.com> wrote:
Hello,
We have to update a column value(from numbers like '123' to codes like 'abc' by looking into a reference table data) in a partitioned table with billions of rows in it, with each partition having 100's millions rows. As we tested for ~30million rows it's taking ~20minutes to update. So if we go by this calculation, it's going to take days for updating all the values. So my question is
1) If there is any inbuilt way of running the update query in parallel (e.g. using parallel hints etc) to make it run faster?
2) should we run each individual partition in a separate session (e.g. five partitions will have the updates done at same time from 5 different sessions)? And will it have any locking effect or we can just start the sessions and let them run without impacting our live transactions?
UPDATE tab_part1
SET column1 = reftab.code
FROM reference_tab reftab
WHERE tab_part1.column1 = subquery.column1;RegardsYudhi
On 2024-Sep-14, yudhi s wrote: > Hello, > We have to update a column value(from numbers like '123' to codes like > 'abc' by looking into a reference table data) in a partitioned table with > billions of rows in it, with each partition having 100's millions rows. Another option is to not update anything, and instead create a view on top of the partitioned table (joined to the reference table) that returns the reference value instead of the original number value from the column; when the application wants to receive those reference values, it queries the view instead of the partitioned table directly. -- Álvaro Herrera 48°01'N 7°57'E — https://www.EnterpriseDB.com/ "I think my standards have lowered enough that now I think 'good design' is when the page doesn't irritate the living f*ck out of me." (JWZ)