Parallel Grid Search in H2O

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H2O is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, machine learning algorithms are implemented. At H2O, we design every operation, be it data transformation, training of machine learning models, or even parsing to utilize the distributed computation model. In order to work with big data fast, it’s necessary.

However, a single operation usually can not utilize clusters’ computational resources to the very maximum. Data needs to be distributed across the cluster, and many operations require sequential execution of tasks, which, even if implemented in a distributed manner, follow after each other and require data exchange. These and many other smaller factors, if summed up together, may introduce a significant overhead.

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Principles to Handle Thousands of Connections in Java Using Netty

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C10K problem is a term that stands for ten thousand concurrently handling connections. To achieve this, we often need to make changes in the settings of created network sockets and default settings of Linux kernel, monitor the usage of the TCP Send/Receive Buffers and Queues, and, in particular, adjust our application to be a good candidate for solving this problem.

In today’s article, I’m going to discuss common principles that need to be followed if we want to build a scalable application that can handle thousands of connections. I will refer to Netty Framework, TCP, and Socket internals and some useful tools if you want to get some insights from your application and underlying system.

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API Security Weekly: Issue #68

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This week, we take a look at where API security is at on Gartner Hype Cycle, what the threatscape for 2020 looks like according to McAfee, and a SANS Institute whitepaper on DevSecOps.

Analysts: API Security in Gartner Hype Cycle

Gartner published their Hype Cycle for Application Security, 2019 a few months ago. The Hype Cycle provides a graph on where we are in application security in terms of the maturity of technologies and their adoption; what is up and coming and what is already established.

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Organizing Your Eclipse Foundation Open-Source Project Team

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The Eclipse Foundation Development Process (AKA, the Eclipse Development Process, or EDP) says nothing about how teams should organize.

The EDP defines a committer role: committers are those developers who have the ability to make decisions for the project (e.g., push commits to project Git repositories and configure build servers). We often say that the committers are the ones with the real power: they’re the ones that hold all of the keys to all of the project resources.

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Build Your First Python Chatbot Project

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Introduction

Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells us that 71% of people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.

Today we are going to build an exciting project on Chatbot. We will implement a chatbot from scratch that will be able to understand what the user is talking about and give an appropriate response.

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What’s the Future of Cloud Databases?

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As more organizations look to migrate their databases to the cloud, what does this mean for developers? We at DZone believe there is no one better to ask than you! Tell us about your experiences with cloud databases by taking this 5 minute survey!

Over the next two weeks, we plan to survey hundreds of software developers about their experiences with cloud databases. The key findings from the survey will be found in our Cloud Database Trend Report to be released February 19. It is our hope that we can identify some of the key trends happening in the space to help our community stay ahead of the curve.

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SQL, Databases, and Hollywood Movies

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Hollywood sign close up

Policemen, doctors, lawyers, scientists, teachers: these are the professions that we often see in Hollywood movies. But are movies also trying to show what the work of data analysts looks like? Here is a list of six movies in which screenwriters, directors, and producers decided to cast SQL and databases in the main roles.

Data analysis is most often associated with business, reporting, and making key decisions for the company. Today it is part of many organizations’ daily life, so it’s no wonder that this topic often appears in movies.

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Durable Cloud Event Production From Knative Container Source With Azure Durable Task Framework

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Durable cloud event production with Knative

Knative is an excellent platform for building, deploying and managing serverless workloads on Kubernetes. The Serving resources of Knative extend Istio to support serverless applications. Another class of resources of Knative called Eventing extend Istio to support the production and consumption of Cloud Events.

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API Security Weekly: Issue #66

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This week, the OAuth 2.0 Token Exchange got its RFC, and there is an upcoming webinar on JWT. In addition, we take a look at where to start with securing your APIs, and how 2020 seems to be shaping up according to analysts.

Standard: OAuth 2.0 Token Exchange

IETF has published the RFC 8693 for OAuth 2.0 Token Exchange.

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Automate CRUD Operations and Focus on What Matters

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I have written about Magic here previously, but on the 5th of January we came out with a significant upgrade to the Magic wand — the ability to automatically scaffold up an entire Angular frontend. This means that you can now start out with only a database, click one button, and Magic creates your entire backend. The result is that every single database table becomes wrapped inside of CRUD HTTP REST endpoints. Click another button, and Magic gives you a ZIP file that contains an entire Angular frontend, tailored specifically to your backend. See the process in the video below, or download Magic and try it out on your own database if you wish.

According to an article I read here at DZone, a highly skilled software developer can produce roughly 750 lines of code per month. When I tested Magic on a database with 122 tables, Magic produced almost 100,000 lines of code for me automatically. That becomes the equivalent of 8.8 years of software development for a human being, and my computer created this code in less than 60 seconds. Add to that the fact that human beings will write an occasional error into their code, and a computer will never create an error — and you get the point.

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