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Remember that the in-memory database is just a cache for the true state of the system.

Alter the inventory in the system for items in the cart as Sold instead of Order Pending via a SQL transaction, and by updating the in-memory database so that they stay in sync.

Authorize the charge on the credit card.

All these operations can be done through message queue subscribers: Once you have the order stored in a durable queue, you can have queue workers read the order messages and process them at a rate that will not overwhelm your database servers.

If the user tries to edit an order while it is in this state, you can tell them that the order is still pending and can’t be changed until it is processed.

Tell them to expect an email shortly regarding the status of their order.

Return an Order Submitted message to the user and display it in their web browser.

Alter the inventory in the system for items in the cart as Sold instead of Order Pending (do it in a fast in-memory database such as Redis instead of in a SQL database).

Publish a message containing the order ID and the information in the shopping cart checkout forms (name, address, credit card information) to a queue.

If you introduce transactional asynchronous messaging and the Publish / Subscribe pattern, you can pare down the amount of work you have to do at checkout time to a much more minimal set: However, this will kill your application and melt your servers when you release a popular, limited quantity item and announce to your customers that it goes on sale at noon on Monday and you face a traffic spike with 1,000+ users trying to order at the same time. When traffic is light, it might be OK to have users wait 10 seconds to have the order complete. Remember – latency is the enemy of throughput due to Little’s Law. Some of these items are processing-intensive, such as updating inventory systems, retrieving all the customer and order details and composing a good-looking email. However, completing all these items could take 10 seconds or more, especially when interactions with external systems such as credit card authorization systems or 3rd-party APIs have unpredictable latencies. In many systems, all these operations happen in order, synchronously, before the customer sees the results from hitting the Submit Order button.

Return an Order Complete message to the user and display it in the application.

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Update 3rd-party sales analytics systems with information about what has sold via an external REST API.

Create a shipment notification and send a pick list to the warehouse via an internal SOAP API.

Send an email to the purchaser with the order details.

Alter the inventory in the system for items in the cart as sold instead of pending order via a SQL transaction.

Mark the order as “purchased” in the database via a SQL transaction.

Authorize the charge on their credit card.

Verify that the name and address are something you could actually ship to.

Verify that the credit card passes the Luhn algorithm check.

In order to fully complete an order from the checkout screen, your system might have to do many things:

#Masstransit retry selected full

Processing a Shopping Cart OrderĪssume you already have a durable order number stored in a SQL database, and that your user has a cart full of goods or services. This article expands on the most advanced technique in my earlier blog post, New Relic and JMeter Performance Remediation – Part 3, Introduce transactional asynchronous messaging. Things get much worse when you have 1,000 or 10,000 customers all trying to buy at the same time! Unless you have applied some advanced performance optimization techniques, your application may fail at the worst time possible. If they can’t complete the transaction fast, you may lose the sale. Imagine that you are writing an e-commerce application and you have a customer with a full shopping cart who is ready to check out and give you their money.