When you install GraalVM, one of the things you get is a Node runtime environment (GraalVM 19.2.1 is based on Node 10.16.3 – with support for the core Node libraries and un understanding of NPM modules – and has a JavaScript engine that is ECAMScript 2019 compliant). Instead of V8, […]
Lucas Jellema
Lucas Jellema, active in IT (and with Oracle) since 1994. Oracle ACE Director and Oracle Developer Champion. Solution architect and developer on diverse areas including SQL, JavaScript, Kubernetes & Docker, Machine Learning, Java, SOA and microservices, events in various shapes and forms and many other things. Author of the Oracle Press book Oracle SOA Suite 12c Handbook. Frequent presenter on user groups and community events and conferences such as JavaOne, Oracle Code, CodeOne, NLJUG JFall and Oracle OpenWorld.
Switch off Screen Lock on Ubuntu Bionic Desktop
I am running an Ubuntu VM on Virtual Box. A guest running inside a host system. I am getting quite annoyed with Ubuntu locking the screen quite aggressively. Since the host OS probably already runs screensaver and screen lock, I want to switch off the screen lock in the Ubuntu […]
Python application running on GraalVM and Polyglotting with JavaScript, R, Ruby and Java
GraalVM is among other things a polyglot language runtime. It can run applications written in many languages – JVM languages like Java, Scala, Groovy and Kotlin as well as non-JVM language such as Python, R, Ruby, JavaScript and LLVM. GraalVM also allows applications in any of these languages to execute […]
Create a Native Image Binary Executable for a Polyglot Java Application using GraalVM – using build time class initializing
GraalVM provides a runtime component that enhances the JVM in several ways. It makes the JIT compilation better. It also allows the JVM to run non-JVM languages such as JavaScript, R, Python, Ruby and LLVM. And it makes it possible for languages running on the JVM to interact – through […]
Leverage NPM JavaScript Module from Java application using GraalVM
Interoperability from Java to JavaScript has been an objective for the Java community for quite a while. With GraalVM, there is great way to run JavaScript code from within Java applications. The interaction itself is faster, more robust and more ‘native’ (rather than bolt-on) than earlier mechanisms. For developers, the […]
Calling out from Java to JavaScript (with call back) – leveraging interoperability support of GraalVM
Interoperability from Java to JavaScript has been an objective for the Java community for quite a while. With Rhino and later Nashorn, two valiant attempts were made to add scripting interaction to the JDK and JVM. Now, with GraalVM, there is a better alternative for running JavaScript code from within […]
Getting started with Windows Subsystem for Linux, Ubuntu and Docker
Starting with a vanilla Windows 10 environment, it took just a few simple steps to get going with Linux on my Windows machine in the Windows Subsystem for Linux (WSL). Note: this is not yet Version 2 of wsl which is currently in (limited) preview install Ubuntu App from Windows […]
Ordering rows in Pandas Data Frame and Bars in Plotly Bar Chart (by day of the week or any other user defined order)
I have time series data in my Pandas Data Frame. And I want to present an aggregation of the data by day of the way in an orderly fashion – sorted by day of the week. Not alphabetically, but sorted the way humans would order the days – starting from […]
Introduction to Oracle Machine Learning – SQL Notebooks on top of Oracle Cloud Always Free Autonomous Data Warehouse
One of the relatively new features available with Oracle Autonomous Data Warehouse is Oracle Machine Learning Notebook. The description on Oracle’s tutorial site states: “An Oracle Machine Learning notebook is a web-based interface for data analysis, data discovery, and data visualization.” If you are familiar with Jupyter Notebooks (often Python […]
Convert Groupby Result on Pandas Data Frame into a Data Frame using …. to_frame()
It is such a small thing. That you can look for in the docs, no Stackoverflow and in many blog articles. After I have used groupby on a Data Frame, instead of getting a Series result, I would like to turn the result into a new Data Frame [to continue […]
Dissecting Dutch Death Statistics with Python, Pandas and Plotly in a Jupyter Notebook
The CBS (the Dutch Centraal Bureau Statistiek) keeps track of many thing in The Netherlands. And shares many of its data sets as open data, typically in the form of JSON, CSV or XML files. One of the data sets is publishes is the one on the number of births […]
Loading Data into Always Free Oracle Autonomous Data Warehouse Cloud – from JSON and CSV to Database Table
In a number of recent articles, I have described how to provision an instance of Oracle Data Warehouse Cloud in Oracle Cloud’s Always Free tier. I have also described how to connect both SQL Developer and Data Visualization Desktop to this ADW instance. In this article, we take this one […]
Oracle Data Visualization Desktop Connecting to Oracle Cloud Always Free Autonomous Database
Oracle Cloud now offers the Always Free Tier that comes with an always free Autonomous Data Warehouse (up to 20 GB data storage) as well as an free Autonomous Database for Transaction Processing. In an earlier article, I described how to provision your own Free Autonomous Data Warehouse in Oracle […]
Connect local SQL Developer to Oracle Cloud Autonomous Database (Always Free Tier)
In a recent article I described how to provision an instance of Oracle Cloud Autonomous Data Warehouse in the recently launched Always Free Tier of OCI. This article shows how to connect from SQL Developer (desktop tool) to this instance. Note: connecting from SQL Developer is the same [of course] […]
Oracle Cloud Always Free Autonomous Data Warehouse – steps to get going
Last month, Oracle announced it’s Cloud Free Tier on Oracle Cloud Infrastructure (OCI). This free tier offers several services (compute, storage, network, monitoring & notifications, serverless functions and Autonomous Database. Two autonomous database instance can be provisioned and leveraged in this “forever free tier” – each with up to 20 […]
Highlights from Oracle OpenWorld 2019 – Larry Ellison’s Key-Notes
This article gives some brief and key insights in Larry Ellison’s keynote presentation on Oracle Cloud Infrastructure at Oracle OpenWorld 2019. Note the new mission statement for Oracle: Our mission is to help people see data in new ways, discover insights, unlock endless possibilities. Autonomous was the key word of […]
Downsizing the Data Set – Resampling and Binning of Time Series and other Data Sets
Data Sets are often too small. We do not have all data that we need in order to interpret, explain, visualize or use for training a meaningful model. However, quite often our data sets are too large. Or, more specifically, they have higher resolution than is necessary or even than […]
Prepare Jupyter Notebook Workshop Environment through Docker container image and Bootstrap Notebook
Earlier this week, I presented a workshop on Data Analytics. I wanted to provide each of the participants with a fully prepared environment, right on everyone’s own laptop (and optionally in a cloud environment such as Katacoda). The environment consisted of Python 3.7, Jupyter Labs (for Notebooks), many additional Python […]
Determine the Language of a Document from the Letter Frequency – using Levenshtein Distance between sequences
Even though many languages share the same or a very similar alphabet, the use of letters in documents written in these languages is quite distinct. The letter ” e” is quite popular, but not the most used letter in every language. In fact, the letter frequency is very specific to […]
Tour de France Data Analysis using Strava data in Jupyter Notebook with Python, Pandas and Plotly – Step 2: combining and aligning multi rider data for analyzing and visualizing the Race
In this article, I analyze the race that took place in stage 14 of the 2019 Tour de France in a Jupyter Notebook using Python, Pandas and Plotly and based on the Strava performance data published by Steven Kruijswijk, Thomas de Gendt, Thibaut Pinot and Marco Haller. In this previous […]
Tour de France Data Analysis using Strava data in Jupyter Notebook with Python, Pandas and Plotly – Step 1: single rider loading, exploration, wrangling, visualization
In this article, I will show how to analyze the performance of Steven Kruijswijk during stage 14 of the 2019 Tour de France in a Jupyter Notebook using Python, Pandas and Plotly. Strava collects data from athletes regarding their activities – such as running, cycling, walking and hiking. Members can […]
Analyzing the 2019 Tour de France in depth using Strava performance data from Race Riders
This year’s Tour de France was quite a spectacle. Great performances, exciting stages, unexpected events: it had it all. Analyzing the race events as they unfolded during the stages of this year’s Tour is something I am keen to attempt. Using Jupyter Notebooks, Python and Pandas and Plotly for visualization, […]
Monitoring Oracle Database using Prometheus
Prometheus is a very popular framework for gathering metrics from a plethora of runtime components, recording, analyzing, visualizing them and making them available for companion technologies such as Grafana. Prometheus harvests information from ‘exporters’ – processes that expose an HTTP endpoint where Prometheus can scrape metrics in a format that […]
Collective Data Set and Equal Data Position between B2B Partners in Data Sharing Ecosystems – enter: Blockchain
Many organizations collaborate in one or more ecosystems: groups of organizations that work together in vertical chain or share common interests sometimes even despite being competitors. Examples are research environments (health, climate, agriculture, environment), supply chains & logistics, insurance industry, government agencies, pension funds, traffic management. Note that within large, […]
Correlation calls for Common Cause Consideration
Correlation is a powerful thing. When two metrics rise and fall in a similar way, surely that cannot be just coincidence. It has to be meaningful in some way. In our minds correlation is easily turned into causality. Our minds are wired to think like that: find the narrative in […]
Custom Log Parser in OMC Log Analytics to deal with Extended Logging in WebLogic Access Log
We recently enabled Extended HTTP Access Logging on our WebLogic Managed Servers. This adds the average response times and potentially several other valuable values to the WebLogic Access Log files. Unfortunately, the WebLogic Access Log file parser in Oracle Management Cloud Log Analytics does not know how to handle the […]
Smoke Escaping from a Black Box – how to tackle major production issues on a system that is not very transparent
This story is based on true events. It happened fairly recently. I was called into an organization that was experiencing performance and stability issues that affected mission critical processes. Every now and again, and increasingly frequently, their service bus stopped working. It appeared as if only a restart could get […]
The Pipelined Pancake Party – demonstrating the Power of Parallelization and Pipelining in JavaScript with Promises and Asynchronous Generators
This article is an attempt to demonstrate the performance gains – and programming elegance – that is at our disposal with the advent of asynchronous generators in ES 2018 (JavaScript), for example in Node 10 and later. With asynchronous generators and Promises, we can implement parallel, asynchronous and pipelined processing […]
JavaScript Pipelining using Asynchronous Generators to implement Running Aggregates
As of ES 2018 (recent browsers or Node 10), JavaScript support asynchronous generators. Generators are functions that return a set of values, one value at a time. These values can be processed inside the code that invokes the generator immediately, as soon as they become available. There is no need […]
Jupyter Notebook for retrieving JSON data from REST APIs
If data is available from REST APIs, Jupyter Notebooks are a fine vehicle for retrieving that data and storing it in a meaningful, processable format. This article introduces an example of a such a dataset: Oracle OpenWorld 2018 was a conference that took place in October 2018 in San Francisco. […]
The Full Oracle OpenWorld and CodeOne 2018 Conference Session Catalog as JSON data set (for data science purposes)
Oracle OpenWorld and CodeOne 2018 are two co-located conferences that took place in October 2018. Some 2000 sessions presented by over 2500 presenters form the core of these conferences. Many details are known about each of the sessions and the speakers – from title, abstract, room (size), date and time, […]
JavaScript Generators – How functions return a stream of results, yielding one result at a time
It was through inspecting some Python code that relied quite heavily that I suddenly realized the beauty of the ES6 concept of generators and the yield keyword. A generator function does not return its result all at once but instead an iterator that can be read from, one value at […]
Newly released: Oracle Exadata X8-2 – bigger disks for saving money, expanding capacity
Earlier this month, Oracle announced the availability of the latest Exadata machine, the X8-2. As well as the option to swap in big 14 TB disks in older versions of Exadata that were shipped with far smaller disks, allowing these older systems to substantially increase there capacity. Our AMIS resident […]
Extremely convenient way to run free Oracle Database 18c on your laptop – or anywhere else – using Vagrant & Virtual Box
Oracle Database 18c XE (eXpress Edition) is the free community edition of the enterprise edition of Oracle Database. Oracle Database XE has almost all features of Oracle Database; however, it is limited in non-functional terms (2 CPUs, 2 GB memory, 12 GB data volume). Oracle is generous in the terms […]
Updating running Kubernetes resources from the kubectl commandline using the patch command
All too often I found myself editing Kubernetes resource definitions in the Kubernetes Dashboard. Modifying for example the type of a service. Typically I would git clone yaml files, run them in a single statement and then manually change a few details. After just the tiniest of search efforts, I […]
Running Apache Kafka on Minikube
I frequently want to demonstrate the working of Apache Kafka and/or provide colleagues and other audiences with an environment for a workshop that involves Apache Kafka. I have been struggling to find a good way to get Kafka to function properly on Minikube – the standalone, single node Kubernetes cluster […]
Serverless Node Function on Oracle Functions runs periodically to produce CSV report on Oracle ObjectStorage from data in ElasticSearch
This article highlights a use case implementation I recently implemented (for a demo session) leveraging Oracle Functions as well as OKE (Oracle Kubernetes Engine), Object Storage and Elastic Search. I will briefly touch upon some of the interesting aspects of implementing this case. The code for the function and […]
Invoke an Oracle Function on Oracle Cloud Infrastructure from a Node application
Oracle Functions is currently in limited preview and is expected to soon reach wider availability. Rolando Carrasco did this write up on Medium about his first experiences with Oracle Functions. Oracle Functions is a managed PaaS service, based on Project Fn. It supports a serverless architecture with light weight functions […]
Save File to Oracle Cloud Infrastructure Object Storage from Node through REST API
Oracle Cloud Infrastructure Object Storage is a public cloud service for storing data to which you need fast, immediate, and frequent access. As Oracle states: “The Oracle Cloud Infrastructure Object Storage service is an internet-scale, high-performance storage platform that offers reliable and cost-efficient data durability. The Object Storage service can […]
Exposing Kubernetes Services to the internet using Traefik Ingress Controller
Three services have been deployed to my Kubernetes cluster running on a public cloud environment. These services expose port for three underlying Pods and six or so containers. The service make the Pods accessible within the cluster – but not to the outside world, the public internet. For a […]