În afara temei aiqbench
Această știre a fost preluată de la o sursă pe care o urmărim, dar nu ține de domeniul AI sau cuantic, așa că nu există o perspectivă aiqbench pentru ea.
Rewinding the exponential: How we keep hardware usage down

Our strategies to optimize our data pipeline throughput without compromising data quality.
More than 20% of the web sits behind Cloudflare’s network, and our team, the Data team, owns the data pipeline responsible for plumbing that data (and more) from our global network to applicable central data centers. The Cloudflare network is a globally distributed system supporting tens of thousands of servers. Data that power Cloudflare Logs, SQL API, observability, internal analytics, and more flow through this network and use our data pipeline to serve customers. In May of this year, we were providing data delivery at roughly two billion events per second. This includes customer traffic as well as data sent from internal teams. Year after year, we tackle the same problem — exponential traffic growth against linear hardware growth. We have to react to this increase in order to keep resource costs under control. Over the past five years, we’ve made various improvements to compensate for the data volume increase in comparison to hardware allocation, equivalent to deferring two and a half years of hardware expansion. This post details those changes: breaking up our monolithic data schema in 2022, adjusting compression in 2024, rearchitecting pipeline replication in spring 2026, decentralizing sampling in summer 2026, and introducing aggregations into the pipeline…
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