THL Simplifies Architecture with MongoDB Atlas Search

Tourism Holdings Limited (THL) originally became a MongoDB customer in 2019, using MongoDB Atlas to help manage a wide variety of telematics data.

I was very excited to welcome Charbel Abdo, Solutions Architect for THL at MongoDB .local Sydney in July 2024 to hear more about how the company has significantly expanded its use of MongoDB.

The largest RV rental company in the world, THL has branches in New Zealand (where it is headquartered), Australia, the US, Canada, the UK and Europe. Specializing in building, renting, and selling camper vans, THL has a number of well-known brands under its umbrella.

In recent years, THL has made a number of significant digital transformation and technology stack optimization efforts, moving from a ‘bolt-on’ approach that necessitated the use of a distributed search and analytics engine to an integrated search solution with MongoDB Atlas.

THL operates a complex ecosystem managed by their in-house platform, Motek, which handles booking, pricing, fleet management, and more—with MongoDB Atlas as the central database.

Its +7,000 RVs are fitted with telematics devices that send information—such as location, high-speed events, engine problems, and geofences or restricted areas (for example, during the Australian bushfires of 2020)—to vehicles’ onboard computers.

THL initially used a bolt-on approach for complex search functionalities by extending their deployment footprint to include a stand-alone instance of Elasticsearch.

This setup, while functional, introduced significant data synchronization and performance issues, as well as increased maintenance overhead. Elasticsearch struggled under heavy loads which led to critical failures and system instability, resulting in THL experiencing frequent outages and data inconsistencies.

After two years of coping with these challenges, THL resolved to migrate away from ElasticSearch. After doing due diligence, they identified the MongoDB developer data platform’s integrated Search capabilities as the optimum solution.

"A couple of months later, we had migrated everything," said Abdo. "Kudos to the MongoDB account team. They were exceptional."

The migration process turned out to be relatively straightforward. By iteratively replacing Elasticsearch with MongoDB Atlas Search, THL was able to simplify its architecture, reduce costs, and eliminate the synchronization issues that had plagued the system. The simplification also led to significant performance and reliability improvements.

Because it no longer needed the dedicated sync resources processing millions upon millions of records per day, THL was able to turn off its Elasticsearch cluster and to consolidate its resources.

“All data sync related issues were gone, eliminated. But also we got our Friday afternoons back, which is always a good thing!” added Abdo.

Abdo’s team can now also use existing monitoring tools rather than having to set up something completely separate from the standalone search engine they were using.

“Sometimes, changes are easier than you think,” said Abdo. “We spent two-and-a-half years with our faulty solutions just looking for ways to patch up all the problems that we were having. We tried everything except actually looking into how much it would actually take to migrate. We wasted so much time, so much effort, so much money. While if we had thought about this a couple of years ago, it would have been a breeze.”

“Over-engineering is bad, simple is better,” he noted.