sqlalchemy和PostgreSQL中的巨大表

Question

I have a table in a postgresql database that is ~900,000 rows. I want to copy it row by row to another table with some extra columns after transforming each row and adding data to the new columns. The problem is that RAM gets full.

Here is the relevant part of the code:

engine = sqlalchemy.create_engine(URL(**REMOTE), echo=False)
Session = sessionmaker(bind=engine)
session = Session()
n=1000
counter=1
for i in range(1,total+1,n):
    ids=str([j for j in range(i,i+n)])
    **q="SELECT * from table_parts where id in (ids)"%ids**
    r=session.execute(q).fetchall()
    for element in r:
        data={}
        ....
       [taking data from each row, extracting string,calculation,
        and filling extra columns that the new table has]
       ...
    query=query.bindparams(**data)
    try:
        session.execute(query)
    except:
        session.rollback()
        raise 
    if counter%n==0:
        print COMMITING....",counter,datetime.datetime.now("%H:%M:%S")
           session.commit()
    counter+=1

The queries are correct, so there is no errors there. Before I press Ctrl+C, the new table gets correctly updated.

The problem seems to the query: " SELECT * from table_parts where id in (1,2,3,4...1000) " I already tried with a postgresql array.

Things I have already tried:

• results = (connection .execution_options(stream_results=True) # Added this line .execute(query)) from here . As far as I know this uses a server side cursor when used with postgresql. I ditched the session object I have in my posted code and used engine.connect()

  • creating a new connection object on each iteration, surprisingly this does not work either. RAM gets full

from the documentation ,

Note that the stream_results execution option is enabled automatically if the yield_per() method is used.

so the yield_per from the query api is the same with the stream_result option mentioned above

thanks

Answer 1

create table table_parts ( id serial primary key, data text );
-- Insert 1M rows of about 32kB data =~ 32GB of data
-- Needs only 0.4GB of disk space because of builtin compression
-- Might take a few minutes
insert into table_parts(data)
  select rpad('',32*1024,'A') from generate_series(1,1000000);

This code below using SQLAlchemy.Core does not use a lot of memory:

import sqlalchemy
import datetime
import getpass

metadata = sqlalchemy.MetaData()
table_parts = sqlalchemy.Table('table_parts', metadata,
    sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True),
    sqlalchemy.Column('data', sqlalchemy.String)
)

engine = sqlalchemy.create_engine(
    'postgresql:///'+getpass.getuser(),
    echo=False
)
connection = engine.connect()

n = 1000

select_table_parts_n = sqlalchemy.sql.select([table_parts]).\
    where(table_parts.c.id>sqlalchemy.bindparam('last_id')).\
    order_by(table_parts.c.id).\
    limit(n)

update_table_parts = table_parts.update().\
    where(table_parts.c.id == sqlalchemy.bindparam('table_part_id')).\
    values(data=sqlalchemy.bindparam('table_part_data'))

last_id=0
while True:
    with connection.begin() as transaction:
        row = None
        for row in connection.execute(select_table_parts_n, last_id=last_id):
            data = row.data.replace('A','B')
            connection.execute(
                update_table_parts,
                table_part_id=row.id,
                table_part_data=data
            )
        if not row:
            break
        else:
            print "COMMITING {} {:%H:%M:%S}".\
                format(row.id,datetime.datetime.now())
            transaction.commit()
            last_id=row.id

You don't seem to use ORM features, so I suppose you should also use SQLAlchemy.Core.