Slow(er) queries This process of accessing all records in one go is not every efficient. I have a sqlite3 database created in Python 3.4. PyMySQL Evaluation. The exact same query in Python runs in 5 minutes or more. As a result MySQLdb has fetchone() and fetchmany() methods of cursor object to fetch records more efficiently. The difference is quite obvious. If I didn’t look at it, I might have […] That helped a little, but it's still unusably slow. I felt that I could write it too. Example of executing and reading a query into a pandas dataframe - cx_oracle_to_pandas.py I did a job that scribe data from web site and put it in MSSQL. Looks like the prefetching does not work. Because If I do the same thing with less complex query I got following times: InteractiveSQL execution time: 8ms. The original Perl script pulls the data in a loop, like while (@row=sth->fetchrowarray) { do stuff; } Originally I simply translated that into Python, but then I updated the script to use the fetchall method an set the array size to 5000. In python execute time is 4ms and fetchall time is 328.8ms. In this example, the result printed after "all persons" will be the result of the second query (the list where salesrep='John Doe') and the result printed after “John Doe” will be empty.This happens because the underlying TDS protocol does not have client side cursors. Rationale. And here are the results:… Up until now we have been using fetchall() method of cursor object to fetch the records. But I discovered writing multiple record to MSSQL server using pyodbc is very slow So after some digging in I found a way to make it much faster. I don’t know how many people are cheated in by this head map:) The reason for this is that last week, a classmate asked Xiaobian and looked at the sample code of Xiaobian and knocked on the code. Now python is doing the heavy lifting parsing and interpreting the data received from the socket while MySQLdb offloads that to the C library. This page will capture issues related to Openstack moving to the PyMySQL driver for MySQL/MariaDB dbapi access.. This makes large dataset providing queries even less efficient and the difference is growing with the number of returned rows. Finally, close the communication with the PostgreSQL by calling the close() method of the cursor and connection objects I wrote it in python using pyodbc. Which really suggest that it queried the database once fetchall was called. The fetchall() fetches all rows in the result set and returns a list of tuples. The protocol requires that the client flush the results from the first query before it can begin another query. An inordinately large number of rows would be indicated by a very slow call to fetchall() at the DBAPI level: 2 0.300 0.600 0.300 0.600 { method 'fetchall' of 'sqlite3.Cursor' objects } If there are no rows to fetch, the fetchall() method returns an empty list. Sample script: import sqlite3 conn = sqlite3.connect('example.db') c = conn.cursor() c.execute(''' SELECT * FROM companies WHERE EXISTS (SELECT 1 FROM companies_branches, branches WHERE companies.id = … After upgrade to Python 3.5.0 1 I noticed that one query generated by SQLAlchemy in my web app executes very slow.. I noticed that when you use python sqlanydb module on a WAN (when the server is in a different datacenter), the sqlanydb module is very slow : it can take 16-30 seconds to fetch 1000 rows. Python execute time: 2.3ms and fetchall time 0.9ms. dbisql works fine with the same query and connection. 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