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@xxxxxxxxx> 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
