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

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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 in regards to the prospect of approximate question processing in analytic clusters for a few time, and this paper defines its usage at scale in production. Microsoft’s data that are big have actually 10s of thousands of devices, as they are employed by huge number of … Continue reading Experiences with approximating inquiries in Microsoft’s manufacturing big-data groups

DDSketch: an easy and fully-mergeable sketch that is quantile relative-error guarantees

DDSketch: a quick and fully-mergeable quantile sketch with relative-error guarantees Masson et al., VLDB’19 Datadog handles a huge amount of metrics – some clients have actually endpoints producing take a look at this website over 10M points per second! For reaction times (latencies) reporting an easy metric such as for example ‘average’ is close to worthless. Alternatively you want to understand what’s happening at various … Continue reading DDSketch: an easy and fully-mergeable quantile design with 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! Final we week looked over automating checks for invariant confluence, and extending the pair of cases where we are able to show that an item is certainly invariant confluent. I’m maybe maybe perhaps not planning to re-cover that back ground in this write-up, so … keep reading IPA: invariant-preserving applications for weakly constant replicated databases

selecting a cloud DBMS: architectures and tradeoffs

Picking a cloud DBMS: architectures and tradeoffs Tan et al., VLDB’19 you go with if you’re moving an OLAP workload to the cloud (AWS in the context of this paper), what DBMS setup should? There’s a diverse group of alternatives including in which you shop the information, whether you operate your very own DBMS nodes or use … Continue reading selecting a cloud DBMS: architectures and tradeoffs

Interactive checks for coordination avoidance

Snuba: automating supervision that is weak label training information

Snuba: automating supervision that is weak label training information Varma & Re, VLDB 2019 This week we’re moving forward from ICML to start out taking a look at a few of the documents from VLDB 2019. VLDB is really a huge seminar, as soon as once more i’ve an issue because my shortlist of “that looks actually interesting, I’d like to read … read on Snuba: automating poor direction to label training information

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