Matt Ranney, Chief Systems Architect at Uber, gave an overview of their dispatch system, responsible for matching Uber's drivers and riders. Ranney explained the driving forces that led to a rewrite of this system. He described the architectural principles that underpin it, several of the algorithms implemented and why Uber decided to design and implement their own RPC protocol.
After living with microservices for three years at Gilt we can see advantages in team ownership, boundaries defined by APIs and complex problems broken down. Challenges still exists in tooling, integration environments and monitoring, Yoni Goldberg explained in a presentation at the QCon London conference describing the challenges they encountered moving to a microservices architecture.
Microservices are conceptually too big; they conflate optimizing for organisational and technical factors, but solutions to problems of each type may not fit together very well, Phil Wills, senior architect at The Guardian, explained in a presentation at the QCon London conference promoting thinking about independent services and single responsibility applications, rather than microservices.
The goal of software is to sustainably minimize lead time to positive business impact, everything else is detail, Dan North claimed in a presentation at the QCon London conference describing ways of reasoning about code and how this leads him into an architecture style that may fit microservices.
When designing and building Halo 4, the next version in a video game series, a new solution was created based on the Actor model implemented by the Orleans framework. Caitie McCaffrey told in a presentation at the QCon London conference talking about the work designing and building the services supporting the new game.
Pivotal recently released Spring XD 1.1 GA with new features including stream processing with Reactor, RxJava, Spark Streaming and Python. Additionally support for Kafka, batching and compression with RabbitMQ, and support for container group management when running on YARN are now featured.
The assumption that a large system must have a single environment, often with a one-to-one mapping between a project’s scope and the system built are challenged today Stefan Tilkov explains when looking into ways to split a large system into smaller parts and comparing the characteristics of systems, applications and microservices.
Google announced last week the release of open source MapReduce framework for C, called MR4C, that allows developers to run native code in Hadoop framework. MR4C framework brings together the performance and flexibility of natively developed algorithms with the scalability and throughput provided by Hadoop execution framework.
Pivotal has decided to open source core components of their Big Data Suite and has announced the Open Data Platform, an initiative promoting open source and standardization for Big Data.
Project Pachyderm Aims to Build "Modern" Hadoop using Docker and CoreOS.
An article by Jin Scott - A tale of two clusters: Mesos and YARN – describes hardware silos created by using different resource managers on different hardware clusters, most popular being Mesos and Yarn and introduces Myriad – a solution allowing to run a YARN cluster on Mesos.
Apache Hive has released version 1.0 of their project on February 6th, 2015. Originally planned as version 0.14.1, the community voted to change the version numbering to 1.0.0 to reflect the amount of maturity the project has reached.
CoreOS announced the availability of etcd 2.0, the first stable version of the open source distributed key-value store.
When adopting a microservices architecture, using an external architect to create the design of a service instead of helping a team make their own decisions about design and implementation is one of several traps or bad practices that Vladimir Khorikov has experienced in his work.
Amazon recently announced EMRFS, an implementation of HDFS that allows EMR clusters to use S3 with a stronger consistency model. When enabled, this new feature keeps track of operations performed on S3 and provides list consistency, delete consistency and read-after-write-consistency, for any cluster created with Amazon Machine Image (AMI) version 3.2.1 or greater.