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ClickHouse achieved good (great) results. However, it's a bad comparison. Clickhouse is an analytics DB while Scylla is a realtime, random access one. /me am Sc

ClickHouse achieved good (great) results. However, it's a bad comparison. Clickhouse is an analytics DB while Scylla is a realtime, random access one. /me am Scylla co-founder. We could stack 100k rows in a single partition and be 1000x faster in this use case than the performance we demonstrate but we wanted to keep it real. Actually the use case we wanted to show is a single row per partition which would require more machines but surprisingly we couldn't provision that many on AWS. The presented usecase by ClickHouse is 100x slower on writes (8M row/s) as they report. It doesn't matter since it's just a completely different use case. Use Clickhouse for analytics (I wonder why stop in SSE and not to go all the way to the GPU like SqreamDB) and use Scylla for OLTP

jayleeg 7y ago
The ScyllaDB one is a bit funny anyway as it doesn't really target analytical workloads. On SSE/GPUs - the ClickHouse guys don't use GPUs today (GPUs are on the roadmap for next year) as their workloads target volumes greater than GPU memory. If your hot dataset sits totally in GPU memory then it makes sense for some things otherwise they found the cost/performance ratio doesn't add up after you paginate in/out. I don't doubt GPU based DB perf numbers but cost is the main factor. Now just to clarify - you're saying Scylla writes are 100x faster on the same hardware as ClickHouse (so 800M row/s on a NUC). Using the same code that Altinity used I manage around 25M rows/s on my home PC (8 cores/16HT) and elsewhere in this thread the guys from VictoriaMetrics pulled in 53M rows/s on a single node with 28 cores/56 threads (probably doable with ClickHouse on similar hardware I'd suspect). I'm going to test this with Scylla on my home PC to validate your 800M row/s claim and I'll post about it - I should be able to hit around 2.5 billion rows/s with Scylla if what you've said is true. I've had CH write 300M row/s on my 8 core box using memory buffered tables but that was only at burst.

jayleeg 7y ago

The ScyllaDB one is a bit funny anyway as it doesn't really target analytical workloads. On SSE/GPUs - the ClickHouse guys don't use GPUs today (GPUs are on the roadmap for next year) as their workloads target volumes greater than GPU memory. If your hot dataset sits totally in GPU memory then it makes sense for some things otherwise they found the cost/performance ratio doesn't add up after you paginate in/out. I don't doubt GPU based DB perf numbers but cost is the main factor. Now just to clarify - you're saying Scylla writes are 100x faster on the same hardware as ClickHouse (so 800M row/s on a NUC). Using the same code that Altinity used I manage around 25M rows/s on my home PC (8 cores/16HT) and elsewhere in this thread the guys from VictoriaMetrics pulled in 53M rows/s on a single node with 28 cores/56 threads (probably doable with ClickHouse on similar hardware I'd suspect). I'm going to test this with Scylla on my home PC to validate your 800M row/s claim and I'll post about it - I should be able to hit around 2.5 billion rows/s with Scylla if what you've said is true. I've had CH write 300M row/s on my 8 core box using memory buffered tables but that was only at burst.