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Author: addshore (Page 2 of 12)

Automatic cleanup of old gcloud container images

I have been using Google Cloud Build for a budget project for roughly a year now. Cloud Build stores built images in a storage bucket which you are of course billed for. Within the first weeks of using it I realized that I needed some automated way to cleanup unused and old images that were built there.

At the time I had a quick search around on the web for something already implemented that I could copy, but I came up blank, and decided putting my problem off would be the best solution. I filed issue number 6 for my project and left it for future me.

Now it’s time to finally close that issue, and I hope others might also find the small bash script useful.

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Add Exif data back to Facebook images – 0.10

Screenshot of the Facebook Exif tool version 0.10

In 2019 I wrote a post introducing a tool that I created to add Exif data back to images downloaded as part of a Facebook information download. The tool allowed me to download and delete my uploaded Facebook images while keeping some of the useful data such as date taken. After some Twitter pressure I have finally released an updated and slightly fixed version, and it’s time that I wrote a updated guide to go with it!

What is Exif data?

Exchangeable image file format (officially Exif) is a standard that specifies the formats for images and tags used by digital cameras and other systems handling image files.

Snipped from WIkipedia

Common Exif data for an image includes the time that it was taken, the camera make and model and the coordinate data for the location of the image.

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Covid-19 Wikipedia pageviews, a first look

World events often have a dramatic impact on online services. A past example would be the death of Michael Jackson which brought down Twitter and Wikipedia and made Google believe that they were under attack according to the BBC.

Events like the COVID-19 (Coronavirus) pandemic have less instantaneous affect but trends can still be seen to change. Cloudflare recently posted about some of the internet wide traffic changes due to the pandemic and various government announcements, quarantines and lockdowns.

Currently the main English Wikipedia article for the COVID-19 pandemic is receiving roughly 1.2 million page views per day (14 per second). This article has already gone through 4 different names over the past months, and the pageview rate continues to climb.

Wikipedia pageviews tool showing English Wikipedia COVID-19 pandemic article views up to 21 March 2020 (source)
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WBStack Infrastructure

WBStack currently runs on a Google Cloud Kubernetes cluster made up of 2 virtual machines, one e2-medium and one e2-standard-2. This adds up to a current total of 4 vCPUs and 12GB of memory. No Google specific services make up any part of the core platform at this stage meaning WBStack can run wherever there is a Kubernetes cluster with little to no modification.

A simplified overview of the internals can be seen in the diagram below where blue represents the Google provided services, with green representing everything running within the kubernetes cluster.

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WBStack – November review

It’s been roughly 1 month since WBStack appeared online, and it’s time for a quick review of what has been happening in the first month. If you don’t already know what WBStack is, then head to my introduction post.

The number of users and wikis has slowly been increasing. In my last post I stated ” 20 users on the project with 30 Wikibase installs”. 3 weeks after that post WBStack now sits at roughly 38 users with roughly 65 wikibases. Many of these wikibases are primarily users test wikis, but that’s great, the barrier to trying out Wikibase is definitely lowered.

If you would like an invite code to try WBStack, or have any related thoughts of ideas, then please get in touch.

What’s changed

As WBStack is a shared platform, all changes mentioned in this blog post are immediately visible on all hosted Wikibases. In the future there will be various options to turn things on and off, but at this early stage things are being kept simple.

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An introduction to WBStack

WBStack is a project that I have been working on for a couple of years that finally saw the light of day at Wikidatacon 2019. It has gone through a couple of different names along the way, MWaas, WBaas, WikWiki, OpenCura and finally WBStack.

The idea behind the project is to provide Wikibase and surrounding services, such as a blazegraph query service, query service ui, quick statements, and others on a shared platform where installs, upgrades and maintenance are handeled centrally.

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Changing the concept URI of an existing Wikibase with data

Many users of Wikibase find themselves in a position where they need to change the concept URI of an existing Wikibase for one or more reasons, such as a domain name update or desire to have https concept URIs instead of HTTP.

Below I walk through a minimal example of how this can be done using a small amount of data and the Wikibase Docker images. If you are not using the Docker images the steps should still work, but you do not need to worry about copying files into and out of containers or running commands inside containers.

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Your own Wikidata Query Service, with no limits (part 1)

The Wikidata Query Service allows anyone to use SPARQL to query the continuously evolving data contained within the Wikidata project, currently standing at nearly 65 millions data items (concepts) and over 7000 properties, which translates to roughly 8.4 billion triples.

Screenshot of the Wikidata Query Service home page including the example query which returns all Cats on Wikidata.

You can find a great write up introducing SPARQL, Wikidata, the query service and what it can do here. But this post will assume that you already know all of that.

Guide

Here we will focus on creating a copy of the query service using data from one of the regular TTL data dumps and the query service docker image provided by the wikibase-docker git repo supported by WMDE.

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Wikidata Map July 2019

It’s been another 9 months since my last blog post covering the Wikidata generated geo location maps that I have been tending to for a few years now. Writing this from a hammock, lets see what has noticeably changed in the last 9 months using a visual diff and my pretty reasonable eyes.

Wikidata “Huge” map generated on the 13th May 2019
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