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 we week looked over automating checks for invariant confluence, and extending the collection of cases where we are able to show that the item is indeed invariant confluent. I’m 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

selecting 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 do my homework a diverse group of alternatives including where you shop the info, 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 poor direction to label training information

Snuba: automating supervision that is weak label training information Varma & Re, VLDB 2019 This week we’re shifting from ICML to begin taking a look at a number of the papers from VLDB 2019. VLDB is really a conference that is huge as soon as once again i’ve a issue because my shortlist of „that looks actually interesting, I’d like to read … Continue reading Snuba: automating poor guidance to label training information

Do My Homework0 comments

Leave a Reply