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What’s Behind Lyft’s Choices in Big Data Tech

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Lyft was a late entrant to the ride-sharing business model, at least compared to its competitor Uber, which pioneered the concept and remains the largest provider. That delay in starting out actually gave Lyft a bit of an advantage in terms of architecting its big data infrastructure in the cloud, as it was able to sidestep some of the challenges that Uber faced in building out its on-prem system. Lyft and Uber, like many of the young Silicon Valley companies shaking up established business models, aren’t shy about sharing information about their computer infrastructure. They both share an ethos of openness in regards to using and developing technology. That openness is also pervasive at Google, Facebook, Twitter, and other Valley outfits that created much of the big data ecosystem, most of which is, of course, open source. So when the folks at Lyft were blueprinting how to construct a system that could do all the things that a ride-sharing app has to do – tracking and connectin...

Actian VectorH architecture and Amazon Redshift benchmark papers

Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop  system  built  on  top  of  the  fast  Vectorwise  analytical database system.  VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting  the  HDFS  replication  policy  to  optimize  read  locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity.  Even though HDFS is an append-only filesystem, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a differential update structure that can be queried efficiently.  The paper describes the changes made to single-server Vectorwise to turn it into a Hadoop-based  MPP  system,  encompassing  workload  management, parallel  query  optimizat...