Source: thenewstack.io
What We Learned Benchmarking Petabyte Workloads with ScyllaDB
With the rise of real-time applications reading and writing petabytes of data daily, it’s not surprising that database speed at scale is gaining increased attention. Benchmarks performed at a petabyte scale can help you understand how a particular database handles the extremely large workloads that your company expects (or at least hopes) to encounter.
Spoiler alert: ScyllaDB stored a 1 PB data set using only 20 large machines running two mixed workloads at 7.5 million operations per second and single-digit millisecond latency.
We chose two concurrent workloads: Application data (OLTP): Data for transaction processing — for example, real-time ad bidding or real-time location data in delivery/Uber/IoT applications.
The ScyllaDB cluster achieved single-digit millisecond P99 latency with 7 million TPS
Spoiler alert: ScyllaDB stored a 1 PB data set using only 20 large machines running two mixed workloads at 7.5 million operations per second and single-digit millisecond latency.
We chose two concurrent workloads: Application data (OLTP): Data for transaction processing — for example, real-time ad bidding or real-time location data in delivery/Uber/IoT applications.
The ScyllaDB cluster achieved single-digit millisecond P99 latency with 7 million TPS
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