BT

Your opinion matters! Please fill in the InfoQ Survey!

JavaOne 2016 - Day 1 Highlights

| by Monica Beckwith Follow 261 Followers on Sep 26, 2016. Estimated reading time: 1 minute |

A note to our readers: As per your request we have developed a set of features that allow you to reduce the noise, while not losing sight of anything that is important. Get email and web notifications by choosing the topics you are interested in.

This year JavaOne live streamed sessions from four rooms for the entire duration of the five-day conference, and those presentations were also made available right after the broadcast.

Learn Java 8: Lambdas and Functional Programming by Henri Tremblay from Terracotta's EHCache team recapped the evolutoon of Java since Java 5 and generics, through Java 7 syntax simplifications, and Java 8 with Lambdas and Nashorn's JavaScript support. Keeping up with his live coding tradition, Tremblay provided many of his answers using live-coding.

Arun Gupta of Couchbase covered Docker for Java Developers. He started with the Docker mission of build, ship and run and then contrasted Docker based containers with typical virtual machines (VMs) on a Hypervisor, as shown in this image from docs.docker.com. Gupta then spoke in detail about the Docker toolbox and also provided information on swarm mode and rolling updates.

There were a several presentations on open source. One was by James Ward of Salesforce who spoke about Managing Open Source Contributions in Large Organizations. Ward talked about the why of open source and the concerns around open-sourcing, as well as strategies to mitigate those concerns, the most common being – do nothing!

There was a presentation on Automated Tuning of the JVM with Bayesian Optimization by Twitter engineers Ramki Ramakrishna, Alex Wiltschko and Jianqiao Liu. Ramakrishna first introduced the JVM tuning problem, explaining that there are some 800 tunable switches, many of which are dependent on the hardware or on each other. Out of those 800, about 250 influence performance. Ramakrishna talked about performance tuning and how it needs to be contiguous, requiring a “black-box tuning assistant” that could provide suggestions. Wiltschko then spoke about Bayesian Optimization, a machine learning approach to black-box optimization. He also provided a one dimensional tuning example shown below:

Liu then spoke on tuning the JVM performance. And finally Ramakrishna summarized the findings:

Rate this Article

Adoption Stage
Style

Hello stranger!

You need to Register an InfoQ account or or login to post comments. But there's so much more behind being registered.

Get the most out of the InfoQ experience.

Tell us what you think

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread
Community comments

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread

Discuss

Login to InfoQ to interact with what matters most to you.


Recover your password...

Follow

Follow your favorite topics and editors

Quick overview of most important highlights in the industry and on the site.

Like

More signal, less noise

Build your own feed by choosing topics you want to read about and editors you want to hear from.

Notifications

Stay up-to-date

Set up your notifications and don't miss out on content that matters to you

BT