CASE STUDY Takes Flight with Scylla

About Kiwi is an online flight booking platform that builds customized travel itineraries by assembling flight combinations from multiple airlines. saves travellers money on airline tickets by generating itineraries that mix-and match global airlines with local carriers, finding the best price for the trip as a whole.’s latest service, Nomad, enables users to add multiple cities, with no requirements on ordering or specific travel dates. Nomad leverages the ‘traveling salesman’ algorithm to generate an itinerary that falls within the traveler’s general requirements, and at the best price.

The Challenge must handle heavy traffic, with 90,000 daily queries, and 25,000 seats booked per day. But the underlying challenge is the ever-increasing size of the graph composed of travel segments and routes. While there are about 100,00 flights per day, is interested in storing flight combinations — for example, Lufthansa’s route from Prague to JFK, connecting in Munich. The combinatorics of the data set result in 7 billion combinations, a number that is continuously growing. At the same time, the data itself is constantly updating, as airlines modify prices for various flight combinations. The data refreshes at the rate of 60% of data per day, 80% over 3 days, and 100% every 10 days.

“If you’re considering moving from Cassandra to Scylla, I don’t know what’s holding you back!”

Martin Strycek, Engineering Manager, initially built their service on Postgres, attempting to scale through sharding and adding instances. This created a management nightmare, since the team had to resort to manually distributing data over clusters running individual Postgres. SQL was clearly not a good choice for’s use case.

To improve performance and scalability, first migrated to Cassandra. But they found their demands never stopped growing, and Cassandra was unable to keep up. “It’s not a uniform unit,” said Martin Strycek, Engineering Manager, “It’s 70 different systems running all over the place.” Ultimately, Cassandra proved unable to scale up, even as the team added more and more nodes. Even worse, the team was required to write custom code to read Cassandra SStables, creating problems with maintenance, upgrades, and so on.

As an international company, wanted to go global, with at least three datacenters in three different cities, running on bare metal. They also wanted to eliminate the headaches of running custom code against the data layer. Another component of the migration was a move from AWS to GCP. To accomplish this, needed to find an alternative to Cassandra.

The Solution quickly settled on Scylla as a drop-in replacement for Cassandra, but they wanted to prove it out under real-life conditions before making the leap. With a healthy scepticism for vendor benchmarks, set out to independently evaluate Cassandra versus Scylla. To do so, the team defined equivalent configurations, traffic volumes, and workloads based on the Cassandra benchmark.

The goal was to test Scylla raw speed and performance, along with Scylla’s support for’s specific workloads. They also wanted some insight into running on bare metal or on a cloud platform, testing GCP versus OVH. The final goal of the POC was to evaluate Scylla’s cost relative to the Cassandra cluster they were running. worked closely with the Scylla team to establish success criteria for the POC. Once the test bed of five nodes each was set up, ran a set of synthetic benchmarks, shadowed production traffic, and used internal monitoring tools for reads.

Their tests demonstrated a stark difference between the two databases. With a replication factor of 4, Cassandra required 100 nodes to achieve 40K reads per second. With only 21 nodes, Scylla was able to achieve 900K reads per second.

Best of all, discovered that the running cost of Scylla would be about 25% the cost of Cassandra. “This is the bottom line,” said Martin Strycek, Engineering Manager, “If you are on the money side, just sign on the dotted line and go with Scylla.”

Having made the decision to go with Scylla, the team undertook the migration to GCP and OVH instances running in multiple cities and geographical regions.

“I like Scylla from the technical point of view,” said Jan Plhák, Head of C++ Development at “I love that it’s written in C++ and runs on the Seastar framework, which is an amazing open-source project. But from the business point of view, it also helps us grow our company.”

Kiwi is also excited about Scylla’s roadmap. The ability to prioritize production traffic over analytics will be a huge advantage, since the many algorithms that runs against the Scylla clusters will have no discernable impact on the customer experience.

By the Numbers

  • Dataset: 7 billion flight entries
  • Storage: 11TB in multiple replicas
  • Writes per second: 700,000
  • Reads per second: 500,000