A Short Spot on Coding up Go Types Sizing Limits

In an effort, finally, to get around to putting more content together I’ve created this video. It’s the first of many that I’ll put together. This one is a quick coding example of getting the maximum size of particular types in Go. At just about a minute, it’s a quick way to pick up a way to do something coding. If you like this video, do leave a comment, if you thought it wasn’t useful leave a comment. Either way I’d like to read the over under on material like this and if it is or isn’t useful (or entertaining) for you. It’ll mostly be just little tips n’ tricks on how to get things done.

The code gist.

 

Machine Learning, Protocols, Classification, and Clustering

Today Suz Hinton @noopkat and Amanda Moran @AmandaDataStax are presenting, “Alternative Protocols – how offline machines can still talk to each other” and “Classification and Clustering Algorithms paired with Wine and Chocolate” respectively. The aim is to stream these talks tonight too on my Thrashing Code Twitch Channel. If you can attend in person, we’re almost at capacity so make sure you snag one of the remaining RSVP’s.

Here’s some more details on the speakers for tonight.

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More on ML4ALL, Learning About “Using Your Data to Give Your Customers Superpowers”

This year’s ML4ALL is coming up real soon, more details over on the site, but here I’m providing some retrospective talks from last year to get you ready for this year’s ML4ALL!

In this talk Kirsten Westeinde (@kmkwesteinde) provides us insights into how your customers can gain superpowers through effective data use. Last year, I introduced Kirsten.

This year’s event is coming up, if you want to get deeper into, pr present on machine learning, learn ways to improve your use of, and eliminate biases like this then ML4ALL is going to be a great conference for you. Check it out, we have an open CFP, tickets are available (early bird still!)

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Eliminating Machine Bias

I introduced Mary Ann Brennan last year for ML4ALL and I really enjoyed the talk Mary gave and wanted to share it again with everyone.

The more I learn myself about machine bias, which really is just manifestation of people’s cognitive biases showing through in models because we’ve poorly identified we have a problem, the more frustrated I get with humanity’s inability to fix this problem. But to the rescue come those people who aim to find, identify, and fix these problems and eliminate the inherent cognitive biases by eliminating machine bias! Enter, Mary Ann Brennan and this talk she gave last year at ML4ALL. This year’s event is coming up, if you want to get deeper into, pr present on machine learning, learn ways to improve your use of, and eliminate biases like this then ML4ALL is going to be a great conference for you. Check it out, we have an open CFP, tickets are available (early bird still!)

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Meetups Last Week: Serverless Identity and Security, Advanced XSS, RAFT Algorithms & Events, and Event Modeling.

Tuesday: Matthew Henderson, Serverless Identity and Security, then Naomi Bornemann, Advanced XSS Techniques.

Wednesday: James Nugent, RAFT Algorithm and Events, then Adam Dymitruk on Event Modeling.

ML Spends A Year In Burgundy with Jon Oropeza at ML4ALL

We’re building up to ML4ALL 2019, and in the meanwhile I want to re-introduce some of the past speakers and show you their talks. This first, of the many, is Jon Oropeza. I introduced him last year here, so check out his talk and work, he’s got a lot of good stuff he’s put together!.

The Talk

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Distributed Database Things to Know: Gossip

Some of the names used can seem to conflate the actual purpose of a feature’s functionality in distributed databases. However gossip is pretty spot on. Within a group of people gossiping the purpose is to find out each other’s business. What’s going on with Frank, who’s he seeing, and Sally started a business, say what! In the end, all gossippers get into the business and understand what Frank, Sally, and the whole crew are up to. This is a good analogy for what gossip does in a distributed database, or distributed systems in general.

The way gossip works in node, is on a peer-to-peer basis. It’s a communication protocol with the purpose of minding the other nodes business so the singular node gossiping can go about its business. The process runs every second and exchanges state messages between the nodes, which then can update their respective state and keep all nodes informed.

Preventing over-communication and mixed messages, the list is derived from seed nodes for all nodes in the cluster. When a node boots up it initiates its gossip from this seed node, which we usually have a few of, and then continues with that gossip list. Note, that seed nodes aren’t a single point of failure, as other nodes in the cluster will take their place if need be, they’re just kind of designated as the lead to initiate a gossip list from.

It is important in Apache Cassandra to also designate a single seed node per replication group (i.e. datacenter) for the seed list. This is recommended for fault tolerance, else gossip has to communicate across higher latency to hit each datacenter, which can eat at response time and performance of the gossip. Think of sending a snail mail USPS letter to a friend to get gossip news! That would take months just to find out what’s going on, kind of the same version of that for computer nodes going across datacenters to talk to the seed node.