distracteddev90
Joined 12 years ago
Joined 12 years ago
1. Wait timings for jobs.
2. Run timings for jobs.
3. Timeout occurrences and stdout/stderr logs of those runs
4. Retry metrics, and if there is a retry limit, then metrics on jobs that were abandoned.
One thing that is easy to overlook is giving users the ability to define a specific “urgency” for their jobs which would allow for different alerting thresholds on things like running time or waiting.
The transition was seamless.
The Github repo mentions a REST api that can be used to push arbitrary data into the system. Are there any docs around this yet?
These people are often labelled as the "pragmatic". They also lean towards having a more general scope of knowledge and thus often lack a "speciality".
There are also those that simply wish to solve the problem in the "best" way possible, and will use whichever stack gets them closer to that ideal solution.
disclaimer: purely based on my experience/opinion/perspective.
If you do get this visa though, be very careful what you say to the border guards. You need to ensure that when speaking about your work, you always frame it as "work in support of scientific research/innovation".
The comment above (https://news.ycombinator.com/item?id=11626762) further expands on why these kinds of benchmarks, although interesting, have no real value.
Each implementation does something wildly different and responds to different inputs with completely different outputs.
To put it metaphorically, if you put a car engine in two completely different chassis and then race them on a track, you aren't gaining any real insight into relative performance of the engine in the two vehicles.
Also, just to be clear, my qualms are with the benchmarks alone, I think the library is great! Thanks for all the hard work :)
Specifically, the http server example(1), doesn't even bother using the standard library provided Cluster module(2). Cluster is specifically designed for distributing server workloads across multiple cores.
All node.js services/applications I've worked on in the past 3 years (that are concerned with scale) utilize a multi-process node architecture.
The current benchmark can only claim that a single python process that spawns multiple threads is 2x faster than a single node.js process that spawns only one thread.
This fact may be interesting to some, but is irrelevant to real world performance.
[1]: https://github.com/MagicStack/vmbench/blob/master/servers/no...
(Not to mention half of ES6 existed in CoffeeScript first, but that's a gripe for another day)
If we want to get really specific, Its also common to see the "DB" image split up between the image of the disk where the data is actually persisted, and the image of the actual DB process. This makes it easy to play around with your data under different versions of your DB.
(edit: this was a reply to the post below.. not sure how I messed that up.. gah.. such a noob)
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