Omid reloaded: scalable and highly-available transaction processing

Omid, reloaded: scalable and highly-available transaction processing Shacham et al., FAST '17 Omid is a transaction processing service powering web-scale production systems at Yahoo that digest billions of events per day and push them into a real-time index. It's also been open-sourced and is currently incubating at Apache as the Apache Omid project. What's interesting … Continue reading Omid reloaded: scalable and highly-available transaction processing

Enlightening the I/O path: A holistic approach for application performance

Enlightening the I/O Path: A holistic approach for application performance Kim et al., FAST '17 Lots of applications contain a mix of foreground and background tasks. Since we're at the file system level here, for application, think Redis, MongoDB, PostgreSQL and so on. Typically user requests are considered foreground tasks, and tasks such as housekeeping, … Continue reading Enlightening the I/O path: A holistic approach for application performance

Explaining outputs in modern data analytics

Explaining outputs in modern data analytics Chothia et al. ETH Zurich Technical Report, 2016 Yesterday we touched on some of the difficulties of explanation in the context of machine learning, and last week we looked at some of the extensions to ExSPAN to track network provenance. Lest you be under any remaining misapprehension that explanation … Continue reading Explaining outputs in modern data analytics

How good are query optimizers, really?

How good are query optimizers, really? Leis et al., VLBD 2015 Last week we looked at cardinality estimation using index-based sampling, evaluated using the Join Order Benchmark. Today's choice is the paper that introduces the Join Order Benchmark (JOB) itself. It's a great evaluation paper, and along the way we'll learn a lot about mainstream … Continue reading How good are query optimizers, really?

Cardinality estimation done right: index-based join sampling

Cardinality estimation done right: Index-based join sampling Cardinality estimation done right: Index-based join sampling Leis et al., CIDR 2017 Let's finish up our brief look at CIDR 2017 with something closer to the core of database systems research - query optimisation. For good background on this topic a great place to start is Selinger's 1979 … Continue reading Cardinality estimation done right: index-based join sampling

SnappyData: A unified cluster for streaming, transactions, and interactive analytics

SnappyData: A unified cluster for streaming, transactions, and interactive analytics Mozafari et al., CIDR 2017 Update: fixed broken paper link, thanks Zteve. On Monday we looked at Weld which showed how to combine disparate data processing and analytic frameworks using a common underlying IR. Yesterday we looked at Peloton that adapts to mixed OLTP and … Continue reading SnappyData: A unified cluster for streaming, transactions, and interactive analytics

Self-driving database management systems

Self-driving database management systems Pavlo et al., CIDR 2017 We've previously seen many papers looking into how distributed and database systems technologies can support machine learning workloads. Today's paper choice explores what happens when you do it the other way round - i.e., embed machine learning into a DBMS in order to continuously optimise its … Continue reading Self-driving database management systems