Parallelizing pandas pyodbc SQL database calls

Question

I am currently querying data into dataframe via the pandas.io.sql.read_sql() command. I wanted to parallelize the calls similar to what this guys is advocating: (Embarrassingly parallel database calls with Python (PyData Paris 2015 ))

Something like (very general):

pools = [ThreadedConnectionPool(1,20,dsn=d) for d in dsns]
connections = [pool.getconn() for pool in pools]
parallel_connection = ParallelConnection(connections)
pandas_cursor = parallel_connection.cursor()
pandas_cursor.execute(my_query)

Is something like that possible?

Answer 1

Yes, this should work, although with the caveat that you'll need to change parallel_connection.py in that talk that you site. In that code there's a fetchall function which executes each of the cursors in parallel, then combines the results. This is the core of what you'll change:

Old Code:

def fetchall(self):
    results = [None] * len(self.cursors)
    def do_work(index, cursor):
        results[index] = cursor.fetchall()
    self._do_parallel(do_work)
    return list(chain(*[rs for rs in results]))

New Code:

def fetchall(self):
    results = [None] * len(self.sql_connections)
    def do_work(index, sql_connection):
        sql, conn = sql_connection  #  Store tuple of sql/conn instead of cursor
        results[index] = pd.read_sql(sql, conn)
    self._do_parallel(do_work)
    return pd.DataFrame().append([rs for rs in results])

Repo: https://github.com/godatadriven/ParallelConnection