Using PyMySQL: Python's MySQL Library
It's almost Friday night, and the squad at H+S is ready to get cooking. Dim down the lights and slip into something more comfortable as we take you on this 100% organic flavor extravaganza. Tonight's menu? A Python MySQL library: PyMySQL.
PyMySQL is a lightweight library perfect for simple scripts. If you're looking to connect to a MySQL database and perform some simple queries, look no further. PyMySQL won't give us the sort of bells and whistles that come with libraries like SQLAlchemy, which is fine if we're looking to build a quick endpoint. A great use case for PyMySQL is for usage in AWS Lambda (or other serverless functions). We'll get to that, but for now, let's cook up something good.
Heat the Stove
Turn on the gas and prep the table to set with your favorite collection of plates! That's right; we're talking boilerplate. You knew it was coming: every time you want to do something tangibly cool, we need to get into the business of managing connections and whatnot. To ease the pain, I'll share with you a preferred method of handling opening connections with PyMySQL.
First things first, we need to create a config file. Unlike SQLAlchemy, PyMySQL doesn't support database URI strings out of the box. Because of this, we need to set variables for each part of a database connection such as username, password, etc.:
We're pulling the values for each of these from a .env file: a practice I highly recommend for security purposes.
Meat and Potatoes
I'll be containing all our app's database logic in a class called Database to contain all variables and functions related to database connections:
Initializing this class saves our database connection variables to the instance of the class, as well as creates a
self.conn variable for managing connections. We create an instance of this class by passing our config object to Database:
With our class initialized, we're ready to get cookin'.
Set The Table
The first method we'll add to our class will be called
open_connection() to manage opening connections to our database. Here we set a function to separate basic connection logic and error messaging from our app:
We look at our class's variable
self.conn to serve as our connection object. By setting conn on our class's self, we can allow all methods of this class to work against the same databases connection instead of opening & closing connections for every query.
openConnection() checks to see if
self.conn already exists. If a connection doesn't exist, our function will attempt to connect to our MySQL database with the given credentials. We also have some logic in here to throw an error if something goes wrong by leveraging PyMySQL's built-in
pymysql.MySQLError exception type. Get used to seeing a lot of try/except/finally statements when working with databases.
We can now share
openConnection() between all the methods of our class and open connections only when needed.
With the boring stuff out of the way, let's dig into some goodness. We'll start off with a basic use case: selecting all rows from a table:
run_query() tries to open a connection using the
open_connection() function we created earlier. With that connection open, we're free to run whichever queries we want.
Our function is passed a parameter called query, which represents whichever SQL query we want to run (AKA:
SELECT * FROM [table]). To execute such a query, we need to open a cursor, which we do with this line:
We're able to run all the queries we want while cur is open. Let's kick it up a notch.
Selecting Rows of Data
Now with cur open, we can call a few methods against cur.
execute()on cur will run a query inside our cursor object.
cur.fetchall(): After running a query which results in rows, we can see all the rows returned by our query by calling
fetchall()on cur. As you can see, we should only execute one query per cursor. Otherwise, we'd be writing over the results of our previous query over and over. Attempting to print the result of
.fetchall()will result in a single integer, which represents the number of rows fetched.
fetchone()only fetches the first row returned by our query. If there are instances where we know only one record should be returned,
.fetchone()should be used.
cur.close(): When we're finally done with our query, we should close the cursor with
run_query() assumes that the query we're passing is a SELECT query, which is why we're saving the results to an array. If we're running an UPDATE or DELETE query, this wouldn't make much sense since nothing would be returned.
Updating Rows of Data
What if we're not selecting data, but rather modifying it? Check our how our function changes:
There are a few new things here:
commit()actually commits the changes in our query. If you forget this, changes to your data won't actually be saved (this goes for DELETE and INSERT statements as well).
cur.rowcount: Returns the number of rows affected by a query which changes data.
Combining the Above
So now we have a version of
run_query() which expects SELECT statements, and another which expects mutations. How could we make this function smart enough to handle either case and return the result which makes the most sense? How about checking to see if the word SELECT exists in the query before executing?:
Now we have a function that handles both scenarios! Of course, this implementation isn't exactly foolproof: if one of your columns happens to named SELECT (or something), you might run into some problems. Don't do that, I suppose.
Hopefully you've found our little dinner date to be useful. If you're looking for some copy & paste source to get started with PyMySQL on our own, feel free to check out the source code for this up on Github here. If that's too much, feel free to copy + paste the below:
We thank you all for joining us in this adventure of tantalizing treats. May your data be clean and your stomachs full.