Key Value Databases Resources

ScyllaDB includes an integrated, highly efficient caching layer as part of its core design. This internal cache is engineered to provide predictable, low-latency performance even at massive scales. Because of this, many organizations have successfully replaced external caching tiers like Redis, ElastiCache, or DAX with ScyllaDB. For those using DynamoDB, ScyllaDB can act as both the primary database and the cache (replacing the need for DAX) through its DynamoDB-compatible API, leading to reduced architectural complexity and lower infrastructure costs.

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ScyllaDB delivers lower latencies at 50% of the cost of DynamoDB. Its close-to-the-metal architecture squeezes every ounce of power from the hardware for impressive price-performance. With ScyllaDB Alternator, a native DynamoDB-compatible API, users can run their DynamoDB workloads anywhere – without code changes. Some other aspects to highlight include: - ScyllaDB was benchmarked at 40.9X (provisioned) to 282.5X (on-demand) lower costs - ScyllaDB demonstrated up to 4X better P99 latency - ScyllaDB achieves single-digital ms latencies without an external cache - ScyllaDB does not throttle or imposes any partition size restrictions on your data by default

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ScyllaDB was built from the ground up to achieve predictable low latency and operational simplicity at scale. Its close-to-the-metal architecture squeezes every ounce of power from the hardware – so users can achieve better performance on fewer nodes. With a similar architecture, data format, and query language as Cassandra, users eliminate Cassandra pains without major refactoring. Some other key aspects include: (1) ScyllaDB has 2X-5X better throughput and much better latencies than Cassandra. (2) ScyllaDB adds and replaces nodes far faster than Cassandra. (3) ScyllaDB finishes compaction 32X faster than Cassandra. (4) A ScyllaDB cluster can be 10X smaller and 2.5X less expensive, yet still outperform Cassandra by 42%. (5) ScyllaDB scales 7.2X faster than Cassandra (9× with cleanup), sustaining ~3.5X higher throughput with fewer errors.

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