Case Study: ScyllaDB vs. MongoDB
ScyllaDB outperforms MongoDB across the board, reducing cost and footprint by 80%, reducing latency, and increasing throughput.
Introduction
Numberly is a programmatic advertising firm that helps brands collect and activate consumer data across digital channels.
Numberly CTO Alexys Jacob-Monier needed a database that could sustain their ID matching tables’ workloads while maintaining consistently low upsert/write and lookup/read latencies.
They knew that the right database would not only reduce costs but also lead to better data consistency and result in greater operational simplicity.
Challenges with MongoDB and Hadoop
Numberly had previously handled real-time ID matching using MongoDB and batch ID matching using Apache Hive. This required them to maintain two copies of every ID matching table.
Latency challenges
Neither MongoDB nor Hive was able to sustain both the read/write lookup/update ratio while performing within the low latencies that Numberly’s SLAs required.
The company was saddled with the operational burden of ensuring data consistency between the two data stores.
Throughput challenges
They found that MongoDB’s primary/secondary architecture hindered performance because of a loss of write throughput.
Numberly CTO Alexys summarized his 7-year MongoDB experience. “To say the least, it is inefficient and cumbersome to operate and maintain.”
ScyllaDB Lowers Cost and Complexity
Numberly started by running ScyllaDB through rigorous load testing. ScyllaDB passed with flying colors, leading Numberly to replace their 15-node Mongo cluster with a 3-node ScyllaDB cluster.
With its production ScyllaDB Enterprise deployment, Numberly has realized a number of benefits, including:
- Cost savings
- Ease of operations–reducing the footprint by 80%
- Production reliability
- Data consistency
- Datacenter awareness
- Performance boosts
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