zeeone

The company hired a new CTO. He's taken it upon himself to set the development stack for both back and front end development. Our application is migrating to micro services. Is it normal for a CTO to set a single technology to be used in all services?

My wife (28) developed an app to make fun of people who make the "duck face" in pictures. Any advice for her?

https://play.google.com/store/apps/details?id=com.duckface.nua.app

I thought it would be great to share code snippets without saving them on a server, so I created http://mem.pe

It allows you to paste code and save it in your browser's local storage. When you share the URL of the pasted code, other people will connect to it via web sockets. For example, this http://mem.pe/#sge/5 you will be pulling from my browser. I'll keep it open for a while.

If you close the web app, your code paste will not be available for anyone to see, until you open the web app again.

This is obviously, not suitable for using at forums and other places where the paste will have to be available for a long time. However, it is great for IRC and chats where you want to send a quick disposable link to a code snippet.

Available on Github at https://github.com/naturalist/mempe

Any comments would be greatly appreciated.

A recent post in HN claimed that MongoDB doesn't scale well, because the performance drops after the indexes are bigger than the available memory.

Scaling with MongoDB (or how Urban Airship abandoned it for PostgreSQL) - http://news.ycombinator.com/item?id=2684423

I decided to put this claim to the test, so I set up an Arch Linux virtual machine with 128M RAM and wrote a quick and dirty benchmark test. The test is a simulation of a simple blog with N user accounts and M posts per account. It creates all accounts first, then it creates the same number of posts for each account. The posts are not created in a consecutive order, and they are indexed by their "account_id" field. Once all accounts and posts are created, the script runs a query to find all post per account for all accounts.

So this script does two things: 1. Writes a lot of records. 2. Reads all records using an index.

You can download it from Github and use it to test in your own environment: https://github.com/naturalist/MongoDB-MySQL-compare

I used MongoDB v 1.8.2 and MySQL 5.5.13. My laptop is a Toshiba Satellite with Intel Core i5 @ 2.53GHz, and the virtual machine I set up was with 128M RAM, running Arch Linux.

Here are the results of my tests:

  MongoDB 
  5000 accounts
  50 posts each
  using MongoDB's native _id

  Create: 317 secs | 185.94 CPU
  Read: 595 secs | 480.64 CPU

  ---

  MySQL
  5000 accounts
  50 posts each

  Create: 322 secs | 99.77 CPU
  Read: 740 secs | 487.50 CP

  ---

  MongoDB
  5000 accounts
  50 posts each
  using an auto incrementing int for _id
  Create: 252 secs | 112.31 CPU
  Read: 520 secs | 218.29 CPU

So it looks like that MongoDB is significantly faster not only when writing but when reading too. The above test completely exhausted the VM's memory and kept using the swap partition.

Next, I decided to really push the limits of both and ran the MongoDB test with 10,000 accounts and 300 posts each. That's 3 million records. It created the records in 3643 secs using 1831.45 CPU, and read the records in ~8000 seconds using ~3000 CPU.

MySQL could not complete the above test. The MySQL server froze and I had to manually kill the process and restart the server.

Disclaimer: I do not work for 10gen, I don't even know them. I'm just a guy who's considering MongoDB for his next project.