Code

16 Ideas of Graph Visualization / Graph Databases Usages

I’m passionate about devtool and data visualization for a long time. I’m regularly giving trials to new tools in the mission to improve my productivity; I’m also creating some tools by myself (assh, depviz, wookie ADN solver, etc.)

There is a sub-topic where I’m more consuming external tools than producing ones: graph visualization; which contains itself some sub-topics: graph databases, graph optimizations, social graphs, real-time graph, graph UI, etc.

For about a week now, thinking (again) about ”how to represent a complex system”, and more precisely: ”how to make a collaborative tool that allows to defining, viewing and analyzing an unstructured, complex, evolutive, and living system.”

For now, I’m mostly reading articles and PoCing, from what I can see, there are a lot (maybe too much) of different existing solutions that handle every part I would need for the final solution.

Problems to address

• how to store the data
• how to programmatically inject and edit data
• how to manually inject and edit data
• how to generate code based on the model definitions
• how to visualize easily / navigate
• how to perform queries
• how to create real-time dashboards
• …

My plan is now to give a more in-depth look at my favorite options. In this intention, I listed 10 (actually 11) ideas of usages that are easier to implement than my target.

Usages for myself

1. Visualize GitHub issues relationships – dependencies, author, people working on, people commenting out, project & organization hierarchy, milestone grouping, labels tagging, etc.
2. Visualize Git code/PRs/Commits – similar to GitHub issues above, but based on code instead of issues: code, files, languages, author, PRs, commits, etc.
3. Define and visualize IT architecture (intermediary milestone to my target) – host, container, ports, cluster, dependencies, Datacenter, process, developers, product, etc.
4. Personal CRM – maintain a wiki about my relationships (people and company), visualize my social graph, etc.
5. Blog content relationship – analyze content based on multiple criteria (keywords, tags, labels, label’s metadata, etc.) to analyze what I talk most, and generate better “related posts” suggestions.
6. Cross-service social graph – create aggregators to visualize people, groups, the friends of friends, followers, followed, etc.
7. Real-time monitoring – define services hierarchically and then write probs that to monitor the health
8. Service/Application comparison based on features and other attributes
9. My GitHub stats; repos, organizations, languages, libraries, metadata, CI used, followers, custom flags (more than 1000 commits, edited < 1 year ago, has a Dockerfile, contributors, etc.).
10. Log parser to analyze user agent to endpoint (real-time dependency) – https://link.medium.com/rSLv1KGPnU
11. P2P network efficiency analysis

Bonus: usages for friends

1. PayFit: engine rules visualization
2. Doctrine: analysis of legal case relationships
3. Zenly: social graph, party recommendation
4. Sounds.am: social graph, friend/playlist/artist/song recommendations
5. Scaleway: visualize relationships between image / volume / volume layer / server entities

PS: about 1 year ago, I forced myself to list “10 something” every morning for a month. Writing this blog post motivated me to retry the experience and share some outputs in the form of small articles on this blog.

Dockerself - runtime self-dockerizer

Presentation

dockerself is a program that creates a new Docker container, injects itself, and finally runs itself from within Docker.

For the record, I was working on pathwar (a security learning platform) and was trying to design the less-constraining way of creating new levels.

My current best idea is to inject a custom entrypoint when the platform starts a new container, instead of requiring the level developers to adapt their Dockerfile.

Additionally, to the simplicity that is added for a level developer, this pattern also has the advantage of always putting the latest version of the entrypoint, even if the image was not updated for a very long time.

Additional resources

• The project: https://github.com/moul/dockerself
• Pathwar sources: https://github.com/pathwar/pathwar

Collaborative Dependencies

The idea

Create a tool that allows to colleboratively define, visualize (with graphs) and query dependencies.

a.k.a. “The perfect mix between Neo4J, Wikipedia/Wikidata, and LucidChart”

Needs

• definition/editing
  • can be done manually
  • can be done using scripts/APIs to aggregate external sources
• should be typed yet flexible

Solution proposal

Glue together:

• Wikibase, the engine behind https://wikidata.org, to store and manually manage the dependencies
• An existing or a custom solution to import/synchronize existing data
• Well-defined base entities to ensure a good overall quality
• A custom frontend to provide useful visualizations
• Some process/guidance to have collaboration

Jim Kang's Website

$> man fred - fred man page

A good friend is starting his “piscine” at 42 today; it’s only his first day but he already made me the good old joke about “there is no man fred”.

If this joke doesn’t make you smile, this is probably because you don’t know what is the man command for developers and how much useful it is.

Explanation: man is the short for manual, when you type man something in a terminal, you can get some information about the command/function/thing.

More info on Wikipedia: man page, RTFM (read the fucking manual)

Unfortunately, as the fred command does not exist by default, running man fred will just raise an error:

$ man fred
No manual entry for fred

As a workaround, I created a manpage for fred four years ago. Today, I just added some install instructions and an example in the README file.

As soon as you install this manpage on your computer, you will be able to type man fred and have usefulvital! information about me :)

Good luck William for your piscine!