UDP 1, 2, 3: netcat vs. socat

TCP is handy for simple, reliable communications like this tiny toy logger. I run the server and clients in separate consoles on the same machine:
# TCP log server
nc -kl 8000 > server-log.txt
# TCP logging from netcat client
date | nc 8000
# TCP logging from socat client
date | socat STDIN TCP:localhost:8000
# TCP logging from Bash client
date > /dev/tcp/
The only bummer about TCP is that–in my example–other clients have to wait in line. We are logging so I want fast, one-way communication from any number of clients to the server, and reliability of every log message is probably not critical. Let’s try UDP! I could just add -u to the netcat server args to use UDP datagrams, but a netcat UDP server gets a little wonky. The easy workaround is to use socat as the server instead. socat happily accepts any datagram from multiple clients, simultaneously.
# UDP log server
socat UDP-RECV:8000 STDOUT > server-log.txt
# UDP logging from netcat client
date | nc -q1 -4 -u 8000
# UDP logging from socat client
date | socat STDIN UDP-DATAGRAM:localhost:8000
# UDP logging from Bash client
date > /dev/udp/
Use at your own risk. The TCP version is surely simplest, safest (ahem, still no auth – this is just a toy) and reliable. I don’t know much about what’s going on under the hood here. Insight welcome! Messages from different clients might get mangled together, too. Tested on Ubuntu 14.04.

Group chat me crazy

Group chat (IRC, Rocket.Chat, Let’s ChatMattermost, Zulip, Slack, etc) rocks! Definitely use it. But, fair warning:

My thoughts on group chat:

  1. Be available sometimes, especially when your coworkers are. Aim for healthy overlap.
  2. Be unavailable sometimes. Focus on your work and get stuff done.
  3. Managers: support your team doing both #1 and #2 above.
  4. Discuss and curate tribal knowledge in group chat, but distill often into other permanent, public, shared resources for your “knowledge base”, such as mailing lists, wikis, and (gasp) formal documentation.

Updating multiple git repositories at once

1. myrepos

http://myrepos.branchable.com – manage multiple repos (source)

2. One-liner

Assuming repo1, repo2 and repo3 are subdirs of the current dir, try:

parallel --tag -j0 git --git-dir={}/.git pull --ff-only ::: repo1 repo2 repo3

Note this assumes you’re using GNU Parallel. On Ubuntu 14.04, I had to do sudo apt-get install parallel. This uninstalled moreutils, which was a minor bummer.

Survey for my SeaGL 2015 command-line talk

I want to give my best talk ever, and I need your help. Knowing my audience will help me produce the most relevant, fun, and insightful content. Please spare a minute and take this survey. Submission does not guarantee admission. All questions are optional. Here’s more info on my talk (including date, time, and location).

Alternative to the Google Form above: all survey questions are repeated below. Email your response to haircut@gmail.com.

  1. Will you attend my talk?
    • Yes/No/Maybe
  2. How experienced are you on the command line?
    • 1=Beginner,2,3,4,5,6,7,8,9,10=Expert
  3. What’s something you love or hate about the command line?
  4. What shell(s) do you use?
    • bash, zsh, ksh, fish, tcsh, other
  5. What operating system(s) do you use?
    • FreeBSD, GNU/Linux, Mac OS X, Windows, other
  6. Is there something in particular you want to learn or improve related to the command line?
    • Examples: basic stuff, piping multiple commands, find/grep/xargs/ssh/cut/paste/col/parallel/at/sed/awk/vim/git, job control (running multiple programs at once), fun/silly stuff, customizing my shell, programmable completion, shell programming, advanced/experimental stuff (like what?)
  7. Anything else you’d like to tell me?

Free Software Claus is Coming to Town

I help organize a conference for Free Software enthusiasts called SeaGL. This year I’m proud to report that Shauna Gordon McKeon and Richard Stallman (aka “RMS”) are keynote speakers.

I first invited RMS to Seattle 13 years ago, and finally in 2015 it all came together. In his words:

My talks are not technical. The topics of free software, copyright vs community, and digital inclusion deal with ethical/political issues that concern all users of computers.

So please do come on down to Seattle Central College on October 23rd and 24th, 2015 for SeaGL!

Sandstorm – personal cloud, self-organzing cluster

I’ve heard a lot of Meteor news lately, but somehow I missed Sandstorm. Your own personal cloud. Install services easier than installing apps on your phone. Add machines and they self-organize into a cluster. This sounds just way too awesome. Looks like they use Meteor heavily. Jade Wang (formerly of the Meteor Development Group) is a co-founder.

Apps must be packaged for Sandstorm (made into “grains”). The list of ported apps is pretty inspiring. Included are: draw.io, LibreBoard, HackerSlides, Let’s Chat, Paperwork… All were new to me, several are written in Meteor, and I was able to check out all of these in seconds. I’m hooked.

how to upgrade MongoDB 2.6 to 3.x on Ubuntu

sudo mv /etc/apt/sources.list.d/mongodb* /tmp/
echo "deb http://repo.mongodb.org/apt/ubuntu "$(lsb_release -sc)"/mongodb-org/3.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-3.0.list
sudo apt-get update && sudo apt-get install -y mongodb-org

And I also had to do fix my replica set in the MongoDB shell (necessary for Meteor oplog tailing):

var a = {"_id" : "rs0", "version" : 1,"members" : [{"_id" : 1, "host" : "localhost:27017"}]};
rs.reconfig(a, {force:true});

UPDATE 2015-10-01: Alas, one of my coworkers found even all the above wasn’t enough–he had to blow away his old MongoDB install.

sudo mv /var/lib/mongodb /tmp
sudo apt-get purge mongodb-org-server
sudo apt-get install -y mongodb-org-server

We also use one-member replica sets in dev (Meteor uses the oplog), so edit /etc/mongodb.conf and include something like replSet=rs0, then restart mongo (sudo service mongodb restart). Finally, initialize the replica set:

var a = {"_id" : "rs0", "version" : 1,"members" : [{"_id" : 1, "host" : "localhost:27017"}]};

It appears that collections can be restored by simply copying files like blah.0, blah.1, blah.2 and blah.ns from /tmp/mongodb to /var/lib/mongodb while the MongoDB server is stopped.

Oplog: a tail of wonder and woe

First, your TL;DR:

  1. Stress test your Meteor app.
  2. Oplog tailing may be less efficient for particular workloads.


My work involves using crowdsourcing to assess and improve technical skill. We’re focusing on improving basic technical skills of surgeons first because—no surprise here—it matters. A more skilled surgeon means patients with less complications. Being healthy, not dying. Good stuff.

One way we gather data is a survey app where crowdworkers watch a short video of real human surgery and answer simple questions about what they saw. For example:

  • Do both hands work well together?
  • Is the surgeon efficient?
  • Are they rough or gentle?

Turns out the crowd nails this! Think of it this way: most anyone can recognize standout performers on the basketball court or a playing a piano, even if they’re not an expert at either. Minimal training and this “gut feel” are all we need to objectively measure basic technical skill.


So, a survey app. Watch a video, answer a few questions. Pretty straightforward. We built one in-house. Meteor was a great choice here. Rapid development, easy deployment, JavaScript everywhere, decent Node.js stack out of the box, all that.

And of course we used oplog tailing right from the start because much of what read about oplog tailing made it sound like it was the only way to go. Sure, you’ll want oplog tailing for realtime (<10sec delayed) data when you have multiple apps connecting to the same MongoDB database. But if you don’t need that, you may not need it at all, and you may not want it.

Traffic pattern

Our traffic is very bursty. We publish a HIT on Amazon Mechanical Turk. Within minutes, the crowd is upon our survey app. Our app generally does fine, but folks complained of very slow survey completion times when we started hitting somewhere around 80 DDP(?) sessions in Kadira. Each DDP session in our survey app should equate to one simultaneous active user (hereafter “user”).

Here’s what we want to know:

  1. Why does our app slow down when it does?
  2. Can it scale [linearly]?
  3. Are there any small code or configuration changes we could do to get a lot more performance out of the existing hardware?


  1. Meteor pegs the CPU when oplog tailing is enabled.
  2. Yes, if we disable oplog tailing.
  3. Yes, disabling oplog tailing and clustering our app.

Stress test

We created a stress test to get a better feel for the performance characteristics of our app.

The test uses nightwatch to emulate a turker completing a survey. Load the survey app, click radio buttons, enter comments, and throw in a few random waits. Many threads of the nightwatch test are spawned and charge on in parallel. The machine running nightwatch needs to be pretty beefy. I preferred a browser-based stress test because I noticed client-server interactions amplified the amount and frequency of DDP traffic (hello Mr. Reactivity). It was also easier to write and run nightwatch then pick the exact DDP traffic to send.

Notes on our app:

  • We use mup to deploy to Ubuntu EC2 servers on AWS.
  • Tested configuration uses one mup-deployed Meteor app.
  • The app connects to a local MongoDB server running a standalone one-member replica set (just to get the oplog).
    • I also tested with Modulus, scaled to one 512mb servo in us-east-1a. Non-enterprise Modulus runs with oplog tailing disabled, and the app connects to MongoDB on a host other than localhost.
  • Our app uses iron:router.
  • Our app doesn’t need to be reactive. Surveyees work in isolation. But this is how we wrote the app, so that’s what I tested.


I ran a series of stress tests. Ramp up traffic, capture metrics, change code and/or server configuration, repeat. Here are the results.


  • Each row in the spreadsheet represents one test.
  • Every test ran for 5 minutes.
  • When one “user” completes a survey, another one begins (so the number of users is kept more or less constant during evey 5-minute test).
  • There are lots of notes and Kadira screenshots in the results spreadsheet. For the Kadira screenshots, the relevant data is on the rightmost side of the graphs.
  • I think Kadira session counts are high. Maybe it isn’t counting disconnects, maybe DDP timeouts take a while, or maybe the nightwatch test disconnects slowly.
  • Row 3. At 40 users, the CPU is pegged. Add any more users and it takes too long for them to complete a survey.
  • Row 5. Notice how doubling the cores does not double the number of test passes (less than linear scaling along this dimension).
  • Row 6. Ouch, we’re really not scaling! Might need to investigate the efficiency of meteorhacks:cluster.
  • Row 7. Oplog tailing is disabled for this and all future tests. MongoDB CPU load is roughly doubled from the 40-user, 1-core, oplog-tailing-enabled test.
  • Row 9. Too much for one core: 6.5% of the tests failed.
  • Row 11. This is what we want to see! 2x cores, 2x users, 2x passes. In other words, with oplog tailing disabled and double the number of cores, we supported double the number of users and doubled test passes.
  • I should have also tested 160 users, 4 cores, oplog disabled. I didn’t. Live with it.
  • Disabling oplog tailing seemed to allow the processing load to shift more to MongoDB. MongoDB appeared to be able to handle same more… gracefully.
  • I didn’t get very far with Modulus. I’m very interested in their offering, but I just couldn’t get users (test runs) through our app fast enough to make further testing worthwhile.
  • A DNS issue prevented capturing Kadira status while running on Modulus.
  • cluster lives up to its promise—adding cores and spreading load.
  • I don’t think we’re moving much data, but any reactivity comes at a price at scale (even our so far little bitty scale).
  • Our survey app could and should be modified to use much less reactivity since, as I mentioned earlier, it is unnecessary.

Server-side profiles

This is somewhat of an addendum, but I figured it might be useful.

Here’s what the Meteor Node.js process does when 10 users hitting our survey app running on one core.

Oplog tailing enabled:

Pie chart server profile with oplog<br /><br />

Oplog tailing disabled:

Pie chart server profile without oplog<br /><br />


  • Note that these pie charts only show %CPU usage. CPU and network are the primary resources our app uses, so this is fine.
  • The profile data for each slice (when you drill down) are very low-level. It’s hard to make any quick conclusions (or I just need more practice reading these).
  • When oplog tailing is enabled, the Cursor.fetch slice is about twice as big, and none of the methods causing that CPU load are ours. Perhaps this is the oplog “tailing” in action?
  • When oplog taling is disabled, drilling into Cursor.fetch shows us exactly what specific methods of ours are causing CPU load. Even if oplog tailing is more efficient, this level of introspection was priceless. We need this until we learn to better debug patterns in our code that lead to more CPU when oplog tailing is enabled.
  • The giant ~30% slice of “Other” is a bit of a bummer. What’s going on in there? Low-level/native Node.js operations like the MongoDB driver doing its thing? Sending/receiving network traffic?
  • Kadira monitoring isn’t free CPU-wise, but it is worth it.
  • What should these pie charts look like in a well-optimized application under load? Perhaps the biggest slice should belong to “Other”?

Further reading:

Feedback/questions/comments/corrections welcome! I’d espeically love to hear about your experiences scaling Meteor.