pymysql connection & pool manager for python3
- Parameter 'charset' default is utf8
 - Parameter 'autocommit' default is True
 - Added parameter 'timezone', default is '+00:00'
 - Use pymysql.cursors.DictCursor by default
 - Reconnect after the database connection is lost
 - Add logs for creating connections, mysql warnings, exceptions, database queries, etc.
 - Using the with...as syntax for transaction operations
 - Provide simplified query methods such as fetch_all/fetch_row/fetch_column/fetch_first
 - Provide simplified methods such as insert/insert_many/update/delete
 
import pymysql
from pymysql_manager import Connection
db = Connection(host='192.0.0.1', database='foo', timezone='+8:00')Before code:
try:
  db.begin()
  db.execute(....)
catch Exception:
  db.rollback()
else:
  db.commit()Now:
with db.transaction():
  db.execute(...)# executed: select * from foo where id between 5 and 10
all_rows = db.fetch_all('select * from foo where id between %s and %s', 5, 10)
# executed: select * from foo limit 1
first_row = db.fetch_row('select * from foo')
# executed: select * from foo limit 1
first_column_on_first_row = db.fetch_first('select * from foo')
# executed: select * from foo limit 1
third_column_on_first_row = db.fetch_column('select * from foo', column=3)When a result is large, it may be used SSCursor. But sometimes using limit ... offset ... can reduce the pressure on the database
by SSCursor
cursor = db.cursor(pymysql.cursors.SSCursor)
cursor.execute(sql)
while True:
  row = cursor.fetchone()
  if not row:
    breakby fetch_iterator
for row in db.fetch_iterator(sql, per=1000, max=100000):
  pass# insert ignore into mytable (foo, bar) values (1, 2)
db.insert('insert ignore into mytable', foo=1, bar=2)
# insert ignore into mytable (foo, bar) values (1, 2) on duplicate key update ...
db.insert('insert ignore into mytable on duplicate key update ...', **dict(foo=1, bar=2))
# insert ignore into mytable (id, name) values (1, 'foo'), (2, 'bar') on duplicate key update ...
db.insert_many('insert ignore into mytable on duplicate key update ...', ['id', 'name'], [(1, 'foo'), (2, 'bar')])
# update mytable set foo=1, bar=2 where id between %s and %s
db.update('update mytable where id between %s and %s', 10, 5, foo=1, bar=2)
db.update('update mytable where id between %s and %s', [10, 5], foo=1, bar=2)
db.update('update mytable where id between %s and %s', *[10, 5], **dict(foo=1, bar=2))
# update from mytable where id between %s and %s
db.delete('delete from mytable id between %s and %s', 10, 5)
db.delete('delete from mytable id between %s and %s', [10, 5])from pymysql_manager import ConnectionPooled
db = ConnectionPooled(host='192.0.0.1', database='foo',
                          pool_options=dict(max_size=10, max_usage=100000, idle=60, ttl=120))db.execute(sql)
db.connection.execute(sql)with db.pool() as connection:
  connection.execute(sql)from pymysql_manager import ConnectionManager
db = ConnectionManager(default='foo',
                       foo=dict(host='192.0.0.1', database='foo', user='root', passwd=''),
                       bar=dict(host='192.0.0.1', database='bar', user='root', passwd=''))db.execute(sql) # use default connection
db['foo].execute(sql)
db.connection('foo').exeucte(sql)with db.pool() as connection: pass  # use default connection
with db['foo'].pool() as connection: pass
with db.connection('foo').pool() as connection: passThe MIT License (MIT). Please see License File for more information.