[REF] Cloud Native Java – Josh Long

Published on Aug 19, 2016

Recorded at SpringOne Platform 2016.
Slides: http://www.slideshare.net/SpringCentr…

“It is not necessary to change. Survival is not mandatory.” -W. Edwards Deming

Work takes time to flow through an organization and ultimately be deployed to production where it captures value. It’s critical to reduce time-to-production. Software – for many organizations and industries – is a competitive advantage. Organizations break their larger software ambitions into smaller, independently deployable, feature -centric batches of work – microservices. In order to reduce the round-trip between stations of work, organizations collapse or consolidate as much of them as possible and automate the rest; developers and operations beget “devops,” cloud-based services and platforms (like Cloud Foundry) automate operations work and break down the need for ITIL tickets and change management boards. But velocity, for velocity’s sake, is dangerous. Microservices invite architectural complexity that few are prepared to address. In this talk, we’ll look at how high performance organizations like Ticketmaster, Alibaba, and Netflix make short work of that complexity with Spring Boot and Spring Cloud.


[REF] Everything You Need to Know About Sharding

This video covers the various approaches to sharding with MongoDB. We cover:

-What is sharding?
-When to shard?
-How to choose a shard key?
-Range Based Sharding
-Hash Based Sharding
-Tag Aware Sharding

Download Slides

[REF] SWIM: scalable weakly-consistent infection-style process group membership protocol


Several distributed peer-to-peer applications require weakly-consistent knowledge of process group membership information at all participating processes. SWIM is a generic software module that offers this service for large scale process groups. The SWIM effort is motivated by the unscalability of traditional heart-beating protocols, which either impose network loads that grow quadratically with group size, or compromise response times or false positive frequency w.r.t. detecting process crashes. This paper reports on the design, implementation and performance of the SWIM sub-system on a large cluster of commodity PCs. Unlike traditional heart beating protocols, SWIM separates the failure detection and membership update dissemination functionalities of the membership protocol. Processes are monitored through an efficient peer-to-peer periodic randomized probing protocol. Both the expected time to first detection of each process failure, and the expected message load per member do not vary with group size. Information about membership changes, such as process joins, drop-outs and failures, is propagated via piggybacking on ping messages and acknowledgments. This results in a robust and fast infection style (also epidemic or gossip-style) of dissemination. The rate of false failure detections in the SWIM system is reduced by modifying the protocol to allow group members to suspect a process before declaring it as failed – this allows the system to discover and rectify false failure detections. Finally, the protocol guarantees a deterministic time bound to detect failures. Experimental results from the SWIM prototype are presented. We discuss the extensibility of the design to a WAN-wide scale.
Date of Conference: 23-26 June 2002
Date Added to IEEE Xplore: 10 December 2002
ISBN Information:
Print ISBN: 0-7695-1597-5
Print ISBN: 0-7695-1101-5
INSPEC Accession Number: 7396105
Publisher: IEEE
Conference Location: Washington, DC, USA, USA
author={A. Das and I. Gupta and A. Motivala},
booktitle={Proceedings International Conference on Dependable Systems and Networks},
title={SWIM: scalable weakly-consistent infection-style process group membership protocol},
keywords={computer network reliability;protocols;wide area networks;workstation clusters;PC cluster;SWIM;WAN;deterministic time bound;distributed peer-to-peer applications;experimental results;failure detection;generic software module;heart-beating protocol;membership update;network loads;performance;periodic randomized probing protocol;piggybacking;ping messages;process group membership protocol;response times;scalable weakly-consistent infection-style protocol;Application software;Computer crashes;Condition monitoring;Delay;Frequency;Large-scale systems;Peer to peer computing;Personal communication networks;Protocols;Robustness},

Solving Service Discovery by Richard Roger, Nearform

Solving Service Discovery – Richard Roger, Nearform

The microservice architecture is a powerful way to structure large scale Node.js systems. Microservices bring many benefits, enabling rapid development, making continuous delivery easier, and making fine-grained scaling practical. But this architecture is a distributed system, and brings with it all the associated challenges of such systems, not least of which is the need for individual services to find each other. Deciding how to solving the service discovery problem is a key decision point for any architect building microservice systems.

A common approach is to use a service registry. Unfortunately service registries bring their own problems, such as ensuring consistency while scaling, or increasing deployment complexity with the need to run agents. All this could be forgiven, but for a greater sin – registries encourage strong coupling between microservices by providing a concept of serviceidentity. The very purpose of the registry, service discovery, is poisonous to one of the key benefits of microservices – that the system is dynamic and flexible.

There is a better way. Services can find each other dynamically, using gossip protocol algorithms. Highly efficient algorithms such SWIM (Scalable Weakly-consistent Infection-style Process Group Membership Protocol) mean that microservices can do away with service discovery altogether, relying instead on an constantly evolving, dynamic map of their local network. By pushing intelligence to the edges of the network, not only does this approach make failure detection orders of magnitude faster (no more waiting for heartbeats to timeout), but it also makes deployment much easier, as the network configures itself.

This talk will examine the use of the SWIM algorithm to build a Twitter clone in Node.js, one microservice at a time.

About Richard Rodger
Richard Rodger is co-CEO and co-founder of nearForm. He is an expert and thought leader in next-generation cloud and mobile technologies, with a current focus on Node.js and microservices. His book Mobile Application Development in the Cloud (Wiley, 2010) is one of the first major works on the subject.
Richard was previously CTO of FeedHenry, a mobile application platform provider that was acquired by RedHat for €63.5m in 2014.
Since Richard co-founded nearForm in 2011, the company has become the world’s largest Node.js and microservices consulting company.

Richard has long been an active member and influencer of the global open source software (OSS) community, specializing in OSS for enterprise. As CTO of nearForm, he has placed OSS at the heart of how the company works. Most recently, he created Seneca.js, a microservices tool kit for Node.js that is a key component of nearForm’s software development and delivery arsenal.
The Tao of Microservices (Manning), Richard’s new book, will be published in 2016.