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Experiences with approximating questions in Microsoft’s manufacturing big-data groups

Experiences with approximating questions in Microsoft’s manufacturing big-data groups

Arandom stroll through Computer Science research, by Adrian Colyer

Experiences with approximating questions in Microsoft’s manufacturing big-data clusters Kandula et al., VLDB’19 I’ve been excited about the possibility of approximate question processing in analytic groups for many time, and also this paper defines its usage at scale in production. Microsoft’s big information groups have 10s of thousands of devices, and tend to be employed by numerous of … Continue reading Experiences with approximating questions in Microsoft’s manufacturing big-data groups

DDSketch: an easy and fully-mergeable quantile design with relative-error guarantees

DDSketch: a quick and fully-mergeable quantile sketch with relative-error guarantees Masson et al., VLDB’19 Datadog handles a lot of metrics – some clients have endpoints creating over 10M points per second! For response times (latencies) reporting an easy metric such as for example ‘average’ is close to worthless. Rather you want to understand what’s happening at various … Continue reading DDSketch: a quick and fully-mergeable sketch that is quantile relative-error guarantees

SLOG: serializable, low-latency, geo-replicated deals

IPA: invariant-preserving applications for weakly constant replicated databases

IPA: invariant-preserving applications for weakly consistent replicated databases Balegas et al., VLDB’19 IPA for developers, delighted times! Continue Reading

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