Almond is a Scala kernel for Jupyter. Some features: Ammonite, a Scala REPL implementation. Coursier, an artefact manager. You can deploy Almond on Kubernetes with the following manifest: apiVersion: apps/v1 kind: Deployment metadata: name: almond labels: app: almond spec: replicas: 1 selector: matchLabels: app: almond template: metadata: labels: app: almond spec: containers: - name: almond image: almondsh/almond:0.13.11 resources: requests: memory: 384Mi limits: memory: 384Mi ports: - containerPort: 8888 --- kind: Service apiVersion: v1 metadata: name: almond spec: type: ClusterIP selector: app: almond ports: - protocol: TCP port: 8888 targetPort: 8888 --- kind: Service apiVersion: v1 metadata: name: almond-headless spec: clusterIP: None selector: app: almond Port forward:...
Some hatch mechanics 📐🔨
I am often a DIY’er and I have a really heavy hatch (32 kg) at home that I want to open automatically with an electric actuator. Something like the hatch in the image below. You can buy these actuators for 50 to 100 euros online: but which one do I need? or: What is the minimal and maximal length (and stroke) that I need? What is the force the actuator should be able to push?...
Collecting stickers 📷
My local supermarket runs a campaign where you can collect stickers for a photo album 📔 with historic pictures of our pitoresk town. For every 10 euros you get a package of 4 stickers and in total there are 229 unique stickers. My question is: How many stickers including doubles do you need to get a full album? 💁 This is known as the Coupon collector’s problem (a variant of the birthday problem)....
Skiing in the Alps
While skiing in the Swiss Alps last winter, I got into a discussion about speed. Following my engineering knowledge, your skiing speed does not depend on your own mass. Without the intention to offend anyone, skiing is basically a form of falling at an angle. We can build a model based on Newton’s second law where acceleration times mass is equal to the resultant force. We can see that mass ($m$) will drop out of the equation:...
Overview of Spark configurations
Find myself looking for an overview too often. So let’s create a rough overview of common used config for Spark. As a start, create a Spark Session with default config: from pyspark.sql import SparkSession spark = SparkSession.builder \ .master(SPARK_MASTER) \ .appname("app name") \ .getOrCreate() The Spark Context represents the connection to the cluster; communicaties with lower-level API’s and RDDs. Some resource settings on the driver: ... .config("spark.driver.memory", "8g") ... .config("spark.cores.max", "4") ....