Hi,
There are many methods to achieve this and one of them is pg_bulkload
utility as described in previous email but I always preferred using python
multiprocessing which I think is more efficient. Below is the code which
you can modify as per your requirement:
import multiprocessing
import psycopg2
def insert_partition(date_range):
conn = psycopg2.connect("dbname=your_db user=your_user
password=your_password")
cur = conn.cursor()
query = f"""
INSERT INTO partitioned_table (column1, column2, ...)
SELECT column1, column2, ...
FROM source_table
WHERE partition_key BETWEEN '{date_range[0]}' AND '{date_range[1]}';
"""
cur.execute(query)
conn.commit()
cur.close()
conn.close()
if __name__ == "__main__":
ranges = [
('2024-01-01', '2024-03-31'),
('2024-04-01', '2024-06-30'),
# Add more ranges as needed
]
pool = multiprocessing.Pool(processes=4) # Adjust based on CPU cores
pool.map(insert_partition, ranges)
pool.close()
pool.join()
On Mon, 14 Oct 2024 at 22:59, Wong, Kam Fook (TR Technology) <
kamfook.w...@thomsonreuters.com> wrote:
> I am trying to copy a table (Postgres) that is close to 1 billion rows
> into a Partition table (Postgres) within the same DB. What is the fastest
> way to copy the data? This table has 37 columns where some of which are
> text data types.
>
> Thank you
> Kam Fook Wong
>
>
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