We are now, simply, Redis
One of the first uses of cookies on the web was pioneered by Netscape way back in the mid ’90s, and ultimately resulted in the login session cookies we just talked about. Originally, cookies were intended to offer a way for a web retailer to keep a sort of shopping cart for the user, in order to track what items they wanted to buy. Prior to cookies, there were a few different solutions for keeping track of shopping carts, but none were particularly easy to use.
The use of shopping cart cookies is common, as is the storage of the entire cart itself in the cookie. One huge advantage to storing shopping carts in cookies is that you don’t need to write to a database to keep them. But one of the disadvantages is that you also need to keep reparsing and validating the cookie to ensure that it has the proper format and contains items that can actually be purchased. Yet another disadvantage is that cookies are passed with every request, which can slow down request sending and processing for large cookies.
Because we’ve had such good luck with session cookies and recently viewed items, we’ll push our shopping cart information into Redis. Since we’re already keeping user session cookies in Redis (along with recently viewed items), we can use the same cookie ID for referencing the shopping cart.
The shopping cart that we’ll use is simple: it’s a HASH that maps an item ID to the quantity of that item that the customer would like to purchase. We’ll have the web application handle validation for item count, so we only need to update counts in the cart as they change. If the user wants more than 0 items, we add the item(s) to the HASH (replacing an earlier count if it existed). If not, we remove the entry from the hash. Our add_to_cart() function can be seen in this listing.
While we’re at it, we’ll update our session cleanup function to include deleting old shopping carts as clean_full_sessions() in the next listing.
We now have both sessions and the shopping cart stored in Redis, which helps to reduce request size, as well as allows the performing of statistical calculations on visitors to our site based on what items they looked at, what items ended up in their shopping carts, and what items they finally purchased. All of this lets us build (if we want to) features similar to many other large web retailers: “People who looked at this item ended up buying this item X% of the time,” and “People who bought this item also bought these other items.” This can help people to find other related items, which is ultimately good for business.
With both session and shopping cart cookies in Redis, we now have two major pieces for performing useful data analysis. Continuing on, let’s look at how we can further reduce our database and web front-end load with caching.