Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

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Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

Eugene Melnikov

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The Ruby ecosystem is constantly evolving. There have been many changes in the engineering world since our comparison of Ruby frameworks in 2014. During the two years, we received a few requests from fellow engineers asking for an updated benchmark. So, here is the 2016 edition with more things tested.

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Using Predix UI Components: Page Layout and Login

Nick Herman

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Predix provides several UI components that help you to build web apps faster and easier. The components are implemented with Polymer, a lightweight library built on top of WebComponents, and can be integrated with other Predix services, such as Time Series.

In this post, we explain the basics of using Predix UI, with focus on page layout requirements and the px-login component.

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Using Spark Streaming, Apache Kafka, and Object Storage for Stream Processing on Bluemix

Ilya Drabenia

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One of the key points in the Industrial Internet is stream data processing. Equipment fault monitoring, predictive maintenance, or real-time diagnostics are only a few of the possible use cases. Some of the services provided by IBM Bluemix enable you to significantly speed up the implementation of such use cases. With Bluemix, you are not required to deploy and configure Hadoop, Apache Kafka, or other big data tools. It allows you to launch service instances in a few clicks.

In this article, we explain how to integrate and use the most popular open-source tools for stream processing. We explore IBM Message Hub (for collecting streams), the Apache Spark service (for processing events), and IBM Object Storage (for storing results).

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Using Predix Analytics Services from a Node.js App

Nick Herman

ge-predix-analytics-services-v1

Predix Analytics is a set of services that enable developers to use analytics solutions implemented by data scientists from their own apps. In this post, we explain how to get started with the three services required for running Predix analytics: Analytics Catalog, Analytics Runtime, and Analytics User Interface.

With the provided instructions, you will be able to set up the services and call the Univariate Anomaly Detection analytic service from a Node.js application.

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Using the PostgreSQL Service in an ASP.NET App on GE Predix

Eugene Lahansky

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PostgreSQL is one of the data management services available for applications running on the Predix platform. This article shows how to work with a Cloud Foundry-hosted instance of the Postgres service from an ASP.NET application.

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How to Deploy a React.js App to GE Predix (a Tic Tac Toe Example)

Dmitry Kozhukh

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React is a JavaScript library for building user interfaces; the technology is backed by Facebook and Instagram—among others. This article shows how to create a simple Tic Tac Toe game with React and then deploy the app to the Predix platform. The post also explains some of the React basics.

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An Outstanding Cloud Foundry Feature: App Placement and Host Affinity

Aliaksandr Prysmakou

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Out of the box, Cloud Foundry doesn’t give us the ability to control application placement or to specify a target host(s) for an application. This may be critical when one needs to separate apps between different environments or host types—or for any other type of app segregation. The good news is that this can be resolved very easily.

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Tuning Configuration of the Cloud Foundry Java Buildpack to Avoid Out-of-Memory Errors

Andrei Bushyk

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Java apps created in accordance with 12-factors are supposed to run in Cloud Foundry without modifications. However, sometimes the defaults provided by the public buildpacks don’t work. In this post, we will look at two ways to resolve out-of-memory errors.

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Using Time Series in a Spring Boot App on Predix

Pavel Orda

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User Account and Authentication (UAA) is an identity management service in Cloud Foundry, used as an OAuth2 provider by the GE Predix platform. Being responsible for managing timed measurements, Time Series, in its turn, enables to efficiently and concurrently store data, as well as quickly retrieve it.

This post explains how to configure both services and to start using them in a sample Spring Boot app having two controllers (for querying and ingesting data).

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Experimenting with Deep Neural Networks for X-ray Image Segmentation

Sergey Kovalev

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Deep neural networks present a great interest for the field of medical image segmentation. This article shares the results of the exploratory phase of the research aimed at examining the potential of deep learning methods and encoder-decoder convolutional neural networks for lung image segmentation. The study was conducted by our partners at the Biomedical Image Analysis Department of the United Institute of Informatics Problems, National Academy of Sciences of Belarus.

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