How Alphaus saves on costs by ‘stitching storage’


One of Alphaus’ data processing pipelines ingests around 10TB of client financial data per day. The processing engine is running on GKE with around 80-100 (depending on what week in the month) pods sharing the total workload. Each pod has around 10GB of memory and 30GB of attached storage. The consistency of this load allowed us to purchase enough Committed Use Discounts (CUDs) for the underlying VMs to save on compute costs.

Infra · Infra · System-Design

3 minutes

Revisiting latency numbers


“Back-of-the-envelop calculations”, “napkin-math”, “latency numbers every programmer should know” - yes, those numbers that usually come up during system design interview questions. This came into my periphery again while looking at RDMA latency checks and benchmarks with P4d instances in AWS (using SoftRoCE). As an old-timer with (most likely) outdated ideas about system design-related latency numbers, although I’m quite familiar with Jeff Dean’s “Numbers every one should know” approximations, I noticed that in a jiffy, I’m still (unconsciously) subscribed to the idea that disk access is most definitely faster than network.

Infra · Infra · Latency · System-Design

3 minutes