paukiatwee

I was researching k8s store offering and found https://rook.io and rook support edgefs with stable status https://github.com/rook/rook#project-status

This post make me never use egdefs in any form.

Rule 1: Never shutdown customer services at the first machine/AI detected potential issue. Always contact customer to verify, always.

DO never suitable for business use case.

Important features:

Native Table Partitioning - https://www.keithf4.com/postgresql-10-built-in-partitioning/ Logical Replication - https://blog.2ndquadrant.com/logical-replication-postgresql-...

or what is new in PG 10 https://wiki.postgresql.org/wiki/New_in_postgres_10

Postgresql really awesome!

Not specific to DO but here are some checklist might helpful to you.

1.1. Elastic IP - Allow you to remap IP to another instance, this is very important for HA where you can bring up another server and remap IP to new instance.

1.2. Alternative to elastic IP is load balancer

2. Bock storage - Allow you to use persistent storage with redundancy. Important to keep data safe when some instances down. You can bring up new instance and start using existing block storage.

DO support all of the above so DO is good enough for production usage. If you need latency sensitive services like RDS, maybe you can consider AWS. I know some people use RDS with compute on other provider like DO.

Really great resources! I was working on my own version of k8s setup scripts using Ansible, and I will definitely use this guide to improve mine.

I think this is different from other company in the sense that they dont own Kubernetes.

It is unlike, for example, Influxdata that they own InfluxDB and dont integrate clustering capability into open source edition.

> 25000+ COMPANIES AROUND THE WORLD USE FLOCK

A lot of Show HN apps/websites using big companies logo, not sure why it is legal to do so, when I believe all is for marketing.

And I hope they will response to your comment.

Anyone looking for self-hosted data exploration platform (only SQL), Airbnb's Superset[0] (previously Caravel) is good alternative.

[0]: https://github.com/airbnb/superset

Machine/deep learning based github/gitlab/bitbucket issue bot that auto label issue with "bug", "enhancement", or "question" label. Currently implemented machine learning(no tuning yet) part but no time implement bot part.

MongoDB Server come to my mind [1].

Not fan of MongoDB.

[1]: https://github.com/mongodb/mongo

The keywords for AGPL are: modify, packaging, distribute.

If someone modify AGPL code, and repackage it, then distribute to clients (AGPL include via network), then they must disclose the source code to clients only.

RethinkDB drivers are in Apache 2.0, which most companies package it in their software and distribute to clients. In this case, the proprietary software need not in AGPL. This is why drivers are in Apache 2.0.

You only concern AGPL when you want modify, repackage it, then distribute it (include via network) to clients WITHOUT disclose source code. If yes, you are in 0.1% of minority that want compete directly with RethinkDB using RethinkDB.

Java AOT is compile JVM bytecode to native code during startup of JVM, which is different from Go's compile source to native and distribute platform specific binaries. So in this case, Java binary size remain same as before, which is .jar or .war binaries.

For Go, .go -> native

For Java, .java -> .class -> package .jar -> AOT native

For Go part I might be wrong, not working on Go professionally.

I believe previously is "alpha" release that required signup invitation form. Now "beta" release is open to anyone with Google Account.

For anyone who interested in ML/DL on cloud.

Google Cloud Platform just released Cloud ML beta with different pricing model, see https://cloud.google.com/ml/

Cloud ML costing $0.49/hour to $36.75/hour, compared to AWS $0.900/hour to $14.400/hour

The huge different of $36.75/hour (Google) compared to $14.400/hour (AWS) make me wonder what Cloud ML are using, they mentioned GPU (TPU?) but not exact GPU model.

Just to confirm, 1000 microservices in this case is 1000 different apps (e.g.different docker images) running simultaneously? 1000 microservices in this case not 1000 microservice instances (e.g. docker instances)?

If it is 1000 microservices as in different apps, then they must have at least 2000 running apps (at least 2 instances per app for HA).

Maybe uber only have 200 "active" microservice app running at the same time where each microservices have N running instances.

I just cant imagine running 1000 different microservices (e.g different docker images, not docker instances) at the same time.

You can get started using https://pgexercises.com/ , which focus on Postgres

Time serie DB for storing: Monitoring data, IoT data, etc.

See example supported inputs by Telegraf https://github.com/influxdata/telegraf/tree/master/plugins/i... from InfluxData

One of the way is can we develop a open source FB client that does not have FB ads? That way we can also plug in Machine Learning algorithm to remove spam etc.

We talking about DW here, so honest question, do you ever develop using DW before?

I learnt to use DW by building an open source personal budgeting app[0]. DW really good and awesome. No whatsoever bytecode manipulation problem.

Everything work great. If you learn real work DW app, see my open source project.

Stack:

1. AngularJS 1.x as frontend

2. DW as REST backend

3. Hibernate as Data Access Layer

4. PostgreSQL as DB

[0]: https://github.com/paukiatwee/budgetapp

Out of topic, I think the way DRF project funded is very interesting, i.e. public funded fulltime developer working on OSS. How many other similar successful OSS with such funding?

I personally built open source personal budget app [1] for fun and experience. I built the app not for profit so there is not failed project.

Instead of assume failed, I suggest make it as your portfolio for future employment.

[1]: https://github.com/paukiatwee/budgetapp

I believe OVH offering will be cheaper, and hourly billing but only available in Europe. While compose.io's offering have more locations (US, Europe, Asia, etc).

It is good to see more and more PostgreSQL as Service offering.

In me experience, employers does not care whether it is free or paid, they care more about your passion to learn.

I took:

1. Functional Programming Principles in Scala[1]

2. Machine Learning Specialization [2]

[1]: https://www.coursera.org/course/progfun [2]: https://www.coursera.org/specializations/machine-learning

I personally paid one course and one specialization courses.

There are the reasons why I paid:

1. The course is awesome and I want to support the authors.

2. The course I paid is new learning area I never use professionally during job but I wish to use at my job (or new job). Instead of say "I interested in XYZ", I said "I interested in XYZ so I took online course to learn" at my interview. This is to partially solve chicken and egg problem.

3. For serious courses, I pay so I can have pressure and motivation to complete it.

Just curious, where (AWS, DO, GCE or other) do you run your Kubernetes cluster ?

If I read correctly, docker for Mac is run on top on another visualization (xhyve, not VirtualBox) and docker for windows run on top of Hyper-V, which mean that it is not for production workload (at least for Windows).

So you can only use it for development. And it is close sourced. hmmm...

All of these "You should use X instead of Y" articles is really not much can debate because there is no perfect languages/tools where in this case, the author think TypeScript is better than JavaScript.

I stop find perfect language because there is none.

Original article is from http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Remind me of https://news.ycombinator.com/item?id=11133950