Marketplace Matching with Xing Chen

The labor market is moving online.

Taxi drivers are joining Uber and Lyft. Digital freelancers are selling their services through Fiverr. Experienced software contractors are leaving contract agencies to join Gigster. Online labor marketplaces create market efficiency by improving the communications between buyers and sellers. Workers make their own hours, and their performance is judged by customers and algorithms, rather than the skewed perspective of a human manager.

These marketplaces for human labor are in different verticals, but they share a common problem: how do you most efficiently match supply and demand?

Perfect marketplace matching is an unsolved problem. Hundreds of computer science papers have been written about the problems of stable matching, which often turn out to be NP-Complete. The stock market has been attempting to automate marketplace matching for decades, and inefficiencies are discovered every year.

Today’s show is about matching buyers and sellers on Thumbtack, a marketplace for local services.

For the first seven years, Thumbtack was building liquidity in its 2-sided market. During those years, the model for job requests was as follows: let’s say I was on Thumbtack looking for someone to paint my house. I would post a job that would say I am looking for house painters. The workers on Thumbtack that paint houses could see my job and place a bid on it. Then I would choose from the bids and get my house painted.

This was the “asynchronous” model. The actions of the buyer and seller were not synchronized. There was a significant delay between the time when the buyer posted a job and the time when a seller places a bid, and then another delay before the buyer selects from the sellers.

Thumbtack recently moved to an “instant matching” model. After gathering data about the people selling services on the platform, Thumbtack is now able to avoid the asynchronous bidding process. In the new experience, a buyer goes on the platform, requests a house painter, and is instantly matched to someone who has a history of accepting house painting tasks that fit the parameters of the buyer.

From the user’s perspective, this is a simple improvement. From Thumbtack’s perspective, there was significant architectural change required. In the asynchronous model, the user requests lined up in a queue, and were matched with pros who placed bids on the items in that queue. In the instant matching model, a user request became more like a search query–the parameters of that request hit an index of pros and returns a response immediately.

Xing Chen is an engineer from Thumbtack, and joins the show to describe the rearchitecture process–how Thumbtack went from an asynchronous matching system to synchronous, instant matching. We also explore some of the other architectural themes of Thumbtack, which we dive into in further detail in tomorrow’s episode about scaling Thumbtack’s infrastructure, which uses both AWS and Google Cloud.

On Software Engineering Daily, we have explored the software architecture and business models of different labor marketplaces–from Uber to Fiverr. To find these old episodes, you can download the Software Engineering Daily app for iOS and for Android. In other podcast players, you can only access the most recent 100 episodes. With these apps, we are building a new way to consume content about software engineering. They are open-sourced at github.com/softwareengineeringdaily. If you are looking for an open source project to get involved with, we would love to get your help.

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