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The world of distributed databases is constantly evolving. Developers and system architects are always on the lookout for databases that provide high throughput, ultra-low latency, and minimal operational overhead. In this pursuit, many organizations using Apache Cassandra have begun migrating to ScyllaDB, a modern, high-performance NoSQL database that retains Cassandra's compatibility while delivering major improvements in scalability, performance, and total cost of ownership (TCO).
This blog explores in depth why migrating from Cassandra to ScyllaDB is a compelling strategy, the performance benefits of ScyllaDB, and a step-by-step migration plan with real-world tips for distributed system engineers and backend developers.
Apache Cassandra has long been the go-to choice for horizontally scalable NoSQL deployments. Its decentralized, peer-to-peer architecture makes it resilient and relatively easy to scale out. However, Cassandra’s architecture was built around the Java Virtual Machine (JVM), which introduces performance ceilings due to garbage collection, memory management constraints, and thread contention at scale.
ScyllaDB was designed as a drop-in replacement for Cassandra, but with a radically different implementation. It is written in C++ (using the Seastar framework), operates without the JVM, and uses shared-nothing, shard-per-core design to maximize CPU and hardware utilization. These differences allow ScyllaDB to consistently outperform Cassandra, particularly under demanding workloads.
ScyllaDB provides 2x to 10x higher throughput than Cassandra on the same hardware. This means that workloads that previously required 100 Cassandra nodes may only need 10 to 15 ScyllaDB nodes. This reduction in cluster size not only lowers hardware and cloud instance costs but also simplifies operations, reduces failure domains, and improves overall system manageability.
This performance gain comes from ScyllaDB’s internal architecture, which eliminates thread contention and uses asynchronous I/O to scale linearly with the number of cores. Developers working with high-throughput services, such as real-time analytics, recommendation systems, or session stores, benefit immediately from ScyllaDB’s high efficiency and low latency under pressure.
Latency spikes in Cassandra, especially P99 and P999 latency, are often caused by JVM garbage collection, compaction storms, or over-threaded workloads. ScyllaDB avoids these entirely by eliminating the JVM and instead using user-space, per-core scheduling to isolate workloads and maintain consistent low-latency behavior, even under heavy load.
For latency-sensitive applications like fraud detection, user messaging, or IoT telemetry pipelines, ScyllaDB can deliver P99 latencies under 1 millisecond consistently, whereas Cassandra may struggle to remain within a few tens of milliseconds under similar load.
Because ScyllaDB avoids the JVM, it doesn't suffer from garbage collection pauses or heap size tuning problems. Each CPU core handles its own shard of data independently and communicates only when necessary. This means there are no locks, no context switching, and virtually no I/O blocking, resulting in near-linear scaling with hardware upgrades.
For developers managing cloud infrastructure, this translates to better cost per transaction, lower TCO, and higher predictability across deployments. It also enables developers to fine-tune performance on a per-core basis when necessary, a luxury not easily achievable in JVM-based systems.
Managing a large Cassandra cluster typically involves tuning compaction strategies, managing tombstone build-ups, coordinating repairs, and dealing with varying GC behaviors. ScyllaDB simplifies this with:
For platform teams managing petabytes of data, these tools allow them to operate large-scale deployments with fewer engineers and greater confidence.
ScyllaDB bundles many enterprise-grade features that require third-party tooling in Cassandra:
These features make ScyllaDB a compelling multi-purpose NoSQL database for a wide variety of use cases ranging from real-time analytics to system-of-record workloads.
Comcast replaced over 900 Cassandra nodes with just 78 ScyllaDB nodes. The result? A dramatic reduction in latency, 10x higher efficiency, and significantly lower cloud costs. Their main motivation was to eliminate unpredictable latency that impacted customer-facing services during garbage collection in Cassandra.
Discord transitioned their messaging infrastructure to ScyllaDB and reported P99 write latencies dropping from 70ms to just 5ms. They also improved read latencies and scaled back their cluster size while handling over 4 million messages per second, crucial for maintaining real-time chat performance at scale.
Rakuten migrated their loyalty program backend to ScyllaDB and reduced their cluster from 24 Cassandra nodes to just 6 ScyllaDB nodes, gaining performance and simplifying management in the process.
These case studies highlight one central theme: ScyllaDB improves efficiency while delivering higher performance at a lower cost.
ScyllaDB is fully compatible with Cassandra’s CQL and data models, meaning you can often reuse your existing table definitions, partition keys, and indexes. However, some internal differences may require changes if you’ve used non-standard configurations, so thorough schema validation in staging is essential.
Scylla Migrator is a powerful tool built on Apache Spark that allows you to move data from a running Cassandra cluster to ScyllaDB with parallelism, fault tolerance, and resume capability.
It supports:
For developers who need to maintain live systems during migration, this tool is a game-changer.
Depending on your use case, you can choose:
ScyllaDB supports dual-write patterns and CDC streaming to ensure data consistency and minimal disruption.
ScyllaDB provides tools like:
Don’t skip this step, ensure data integrity and latency objectives are met before committing production workloads.
Before any migration, create a mirror staging environment that reflects your production schema and workload. Use synthetic or sampled real data to benchmark latency, throughput, and compaction performance.
Use Scylla Monitoring Stack, which provides Prometheus-based dashboards for:
This gives you visibility into how the system behaves under load and lets you catch anomalies before they reach production.
Instead of flipping the switch instantly, use phased traffic migration:
ScyllaDB excels under pressure, but testing at peak load ensures smooth performance. Simulate bursts using traffic replay tools or load generators to validate system behavior during Black Friday–like events.
If you’re hitting scaling limits, experiencing unpredictable tail latencies, or tired of JVM tuning, ScyllaDB is built for you.
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