We’re integrating dietitian-built meal plans with chef-crafted menus to create a new dining experience.
Join our founding team of hackers in building a modern tech stack that (literally) serves 5,000 athletes across the NFL, Team USA, USSOCOM, NBA, NHL and MLB, with plans to (soon) disrupt the consumer dining experience.
Come work at Notemeal. We have an office with a private gym in Boston and can get tested for COVID once per week as a perk. https://notemeal.io jokes aside (kind of), we were remote for the first year, and it wears on you pretty fast to eat, sleep and work in the same room.
We did this with the New England Patriots, only we moved to an on-prem PostgreSQL instance. Specifically, we were on Oracle 8, so just finding the compatible drivers was half of the battle. I wrote a Python utility that carries these (nasty) migrations out from end-to-end. It works well, probably doesn't scale well, and needs tests. IT also doesn't solve the problems of Oracle-specific features, we just dropped those, which is probably not suitable for most folks, though many of these can/should be done at the application level.
Anyway, I made a promise to myself that when the project hits 300 stars I'd write tests and refactor for scalability. I need 3 more. Probably will be looking for some help if anyone's interested... https://www.github.com/seanharr11/etlalchemy
This page suggest that I am logged into "VK", which apparently is the Russian-equivalent of Facebook, but I never heard of it before this. Any reason why that is?
I'm trying to give some consult to a company who is looking to store JSON data in MySQL, and they have a Rails app.
Given a Rails web app (i.e. ActiveRecord models), what is the best choice if the JSON column type is essential?
Initial research shows that PostgreSQL stores JSONB more efficiently, and provides faster query performance with indexes, while MySQL exposes and JSON query interface w/o much optimization. Thoughts?
Just a quick interjection: I wrote a library that migrates between any 2 relational databases. In my case, I migrated off of Oracle and onto MySQL, but the tool supports any RDBMS supported by SQLAlchemy.
The best way to know is to try! It takes 4 lines of python to get you migrated.
PostgreSQL is a bit more feature rich in my findings, but for the majority of web development there really aren't huge differences. In my experience, MySQL performs a bit better for lots of simple READ queries, while PostgreSQL can handle larger, more complex queries and very frequent WRITEs.
This includes automatic migration of the schema, constraints, indexes and obviously data. It leverages fast bulk loading tools like mysqlimport, and PostgreSQL's COPY FROM. It doesn't need any config. Just plug and play.
The tool uses SQLAlchemy to abstract column types, and handles various column type conversions between any flavor of SQL.
The one thing it needs are tests, and scalability support for tables bigger than a few GB!
A few of these steps could be solved with this tool, including schema/index migrations, and even initial (fast) data transfer:
https://github.com/seanharr11/etlalchemy
> As to the motivation for the correct step: can you point me to a resource that explains this? Not sure I follow...
You write an equation involving division by the gradient. This is an illegal operation (one cannot divide by a vector), and your final recipe doesn't do it. As far as I can tell, you are writing down the incorrect, illegally-vector-inverting formula as motivation for the correct formula involving the (inverse of the) Hessian. All I am suggesting is that you say explicitly something like "Of course, this formula as written is not literally correct; one cannot actually divide by a vector. The correct procedure is explained below."
(Incidentally, speaking of inverses, another poster (https://news.ycombinator.com/item?id=14881265) has mentioned that it may be a bit confusing to speak of the inverse of a matrix rather than the reciprocal, since (as I interpret that other poster's point) the reciprocal of a matrix is just its inverse. I might prefer to say something like "We write $H_{\ell(\theta)}^{-1}\nabla\ell(\theta)$ rather than $\frac{\nabla\ell(\theta)}{H_\ell(\theta)}$ to emphasise that we are inverting a matrix, not a scalar, so that the order of multiplication matters.")
Bishop has a nice treatment of Newton's method in "Pattern recognition and machine learning". Good book to have on your shelf of you are learning this stuff.
Typescript, Apollo GraphQL, React, Ionic
We’re integrating dietitian-built meal plans with chef-crafted menus to create a new dining experience.
Join our founding team of hackers in building a modern tech stack that (literally) serves 5,000 athletes across the NFL, Team USA, USSOCOM, NBA, NHL and MLB, with plans to (soon) disrupt the consumer dining experience.