Scientist
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Scientist | Blazer | |
---|---|---|
18 | 17 | |
7,331 | 4,375 | |
0.3% | - | |
2.5 | 7.2 | |
about 1 month ago | about 1 month ago | |
Ruby | Ruby | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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Scientist
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Crates that run multiple versions of a function and ensures the return value is the same?
For some google-fu, the ruby / .NET equivalent of this is https://github.com/github/scientist / https://github.com/scientistproject/Scientist.net
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Scientist: A Ruby library for carefully refactoring critical paths
The readme (here https://github.com/github/scientist#alternatives) doesn't mention, but here is one for Rust: https://crates.io/crates/scientisto
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Test Against Reality
Something I've learned in Ruby land (prob standard in other places, forgive my ignorance) that seems a bit different than what the article advocates for (fake services):
- Write your service wrapper (eg your logic to interact with Twilio)
- Call the service and record API outputs, save those as fixtures that will be returned as responses in your tests without hitting the real thing (eg VCR, WebMock)
- You can now run your tests against old responses (this runs your logic except for getting a real response from the 3rd party; this approach leaves you exposed to API changes or you have edge cases not handled)
For the last part, two approaches to overcome this:
- Wrap any new logic in try/catch and report to Sentry: you avoid breaking prod and get info on new edge cases you didn't cover (this may not be feasible if the path where you're inserting new logic into does not work at all without the new feature; address this with thoughtful design/rollout of new features)
- Run new logic side by side to see what happens to the new logic when running in production (https://github.com/github/scientist)
I use the first approach bc small startup.
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Real-World Engineering Challenges: Migrations
Check out GitHub scientist if you are doing a migration with a ruby based system: https://github.com/github/scientist
Great support and functionality for testing differences between two systems of record.
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Rethinking Testing
As far as this idea, I have seen this before in a few different forms. The closest thing that I've personally witnessed being used is the scientist gem for Ruby applications. You have to do it manually, but you can instrument your code to compare old and new versions of some code. It also does some fancy stuff like randomly choosing which version gets run, almost like an A/B test. I wonder if there's a similar library for Python?
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axum-strangler initial release
Not sure what OP had in mind, but for my dream strangler (that's a phrase I never expected to use), I'd love functionality like github's scientist library; basically, the ability to implement a route, continue to serve most requests through the original service, but duplicate a small percentage to the new implementation, compare the outputs of the two services, and log wherever the responses differ, so you get live production tests to exercise the new service without impacting users.
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Using Scientist to Refactor Critical Ruby on Rails Code
However, the good news is that it’s easy and safe to do so in Ruby and Rails using the Scientist gem. Scientist's name is based on the scientific method of conducting experiments to verify a given hypothesis. In this case, our hypothesis is that the new code does the job.
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Book notes: Turn the Ship Around!
Github scientist.
Blazer
- Blazer: Business Intelligence Made Simple
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Is Tableau Dead?
I try to avoid these tools wherever possible, given the choice I'd always go for tools like Blazer.
https://github.com/ankane/blazer
No such luck in my current role, Looker and PowerBI are both in use by different bits of the org and nobody has the ability to delve into the underlying figures.
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BI vs custom queries in app
As u/jaxn said you could use Blazer for this kind of thing. I would also look into materialized views or custom tables and a scheduled job that calculates the metrics they care about. That will take you a long way. Eventually you can use something like Metabase but I would put that off for as long as possible as it's really expensive and pretty involved.
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Evidence – Business Intelligence as Code
And it's Open Source: https://github.com/evidence-dev/evidence
I'd also highly recommend Blazer https://github.com/ankane/blazer if you are into the Ruby on Rails world. It's super solid, and it's been an indispensable tool integrated to all my projects.
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Italian watchdog bans use of Google Analytics
I use Ahoy too, but I don't have very good visibility into the data. I should spend more time building queries and creating charts. I should probably set up blazer as well: https://github.com/ankane/blazer
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My project: railstart app
blazer
- dashboard framework
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Using Scientist to Refactor Critical Ruby on Rails Code
The Blazer gem provides a nice way to analyze the results easily. It is simple to install and allows SQL queries to run against tables. The query here shows that the candidate implementation is significantly faster than the original.
- A Ruby-Powered Business Intelligence Tool
- Out of the Box CRUD Management Framework
What are some alternatives?
Rubocop - A Ruby static code analyzer and formatter, based on the community Ruby style guide. [Moved to: https://github.com/rubocop/rubocop]
Rails DB - Rails Database Viewer and SQL Query Runner
Coverband - Ruby production code coverage collection and reporting (line of code usage)
PgHero - A performance dashboard for Postgres
SimpleCov - Code coverage for Ruby with a powerful configuration library and automatic merging of coverage across test suites
Redis Dashboard - Sinatra app to monitor Redis servers.
Rubycritic - A Ruby code quality reporter
SchemaPlus - SchemaPlus provides a collection of enhancements and extensions to ActiveRecord
Traceroute - A Rake task gem that helps you find the unused routes and controller actions for your Rails 3+ app
SecondBase - Seamless second database integration for Rails.
Flog - Flog reports the most tortured code in an easy to read pain report. The higher the score, the more pain the code is in.
Upsert - Upsert on MySQL, PostgreSQL, and SQLite3. Transparently creates functions (UDF) for MySQL and PostgreSQL; on SQLite3, uses INSERT OR IGNORE.