Go: Pointer to methods

Go: Pointer to methods

Sometimes I need a pointer to a method that is not bound to an explicit object.

Method pointers are just function pointers where the first argument is the type.

Imagine we have a Pet type with a Speak method:

type Pet interface {
    Speak() string
}

Here’s how we would declare, assign, and invoke the pointer

var fp func(Pet) string
fp = Pet.Speak

pet := GetPetFromSomewhere()
fmt.Sprintf("The pet says: %s\n", fp(pet))
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Getting timestamp from v1 uuid

Getting timestamp from v1 uuid

Translated from J.B. Langston’s Converting TimeUUID Strings to Dates Java version to Python:

import arrow
import fileinput
import uuid

def main():
    for line in fileinput.input():
        line = line.rstrip()
        value = uuid.UUID(line)
        epoch_millis = (value.time - 0x01b21dd213814000) / 10000
        epoch = epoch_millis / 1000
        ts = arrow.get(epoch)
        print(ts)

if __name__ == "__main__":
    main()
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Starting a new Minecraft mod

Starting a new Minecraft mod

My kids are really into Minecraft, and are interested in mods. Here’s a quickstart to getting an OS X dev environment set up with IntelliJ IDEA.

Prereqs

You should have the following already installed on your machine:

  • Minecraft (with purchased license)
  • JDK
  • IntelliJ IDEA

Get Forge Mod Development Kit (MDK)

  1. Go to the Minecraft Forge downloads page.
  2. In the “Download Recommended” section, click on the “Mdk” icon. You’ll be redirected to an ad-based download page.
  3. In a few seconds, you’ll see a red “SKIP” button in the top-right, click on
    it to begin the download.

As of this writing, the recommended file is forge–1.8–11.14.4.1563-mdk.zip

Set up a folder for development

I’m calling mine ~/dev/minecraft-potion-mod, since the initial mod I’m writing involves new potions. Here we extract it and create a new git repository:

mkdir ~/dev/minecraft-potion-mod
unzip ~/Downloads/forge-1.8-11.14.4.1563-mdk.zip -d ~/dev/minecraft-potion-mod
cd ~/dev/minecraft-potion-mod
git init
git add .
git commit -m 'initial commit'
./gradlew setupDecompWorkspace

IntelliJ

Generate the project files

./gradlew idea
  1. Open IntelliJ IDEA
  2. Click “Open”
  3. Open the generated .ipr file. In my case, it’s
    /Users/lorin/dev/minecraft-potion-mod/minecraft-potion-mod.ipr

IDEA will likely show you pop-ups:

  • Unregistered VCS root detected.
  • Unlinked Gradle project?

These are safe to ignore. Alternately, you can register your root git and link the gradle projects so IDEA stops nagging you.

Run Minecraft with the mod

  1. In the menu bar, to the left of the (likely grayed out) “Run” button that looks
    like a “play” triangle, click the drop-down, and choose “Minecraft Client”.
  2. Click the (now active) “Run” button.

This should launch Minecraft. If you click the “Mods” button, it should say
“Example Mod” as one of the mods.

You’re now ready for Minecraft mod development.

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Testing the Vert.x kinesis module

I needed a simple way to test queueing a Kinesis message with the Vert.x Kinesis module. This Jython script will do it:

import vertx
from core.event_bus import EventBus
from org.vertx.java.core.json import JsonObject
from org.python.core.util import StringUtil

module = "com.zanox.vertx.mods~mod-kinesis~1.4.13"

def handler(_, x):
    print "Deployed kinesis module: %s" % x
    data = JsonObject()
    data.putBinary("payload", StringUtil.toBytes("this is a test"))
    send_message(data)


def send_message(msg, address="kinesis.verticle"):
    print "sending a message"
    EventBus.send(address, msg, reply_handler) 

def reply_handler(message):
    print "I received a reply %s" % message.body


vertx.deploy_module(module, vertx.config(), 1, handler)

Assuming the script is named test.py, run it by doing:

vertx run test.py -conf conf.json

See the module’s README for the conf.json format, should look something like this:

{
    "address": "kinesis.verticle",
    "streamName": "test-stream",
    "partitionKey": "partitionKey",
    "region": "us-east-1"
}

You’ll also need to create a stream first. You can use the AWS CLI for this:

aws kinesis create-stream --stream-name test-stream --shard-count 1
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Using mitmproxy to log Docker API calls

The other day I needed to log the API calls made from my Docker client to debug a difference in behavior between the docker command-line tool and the docker-py Python client. Here’s how I did it.

Disable TLS on boot2docker

I’m using boot2docker on OS X. I disabled TLS to simplify things. To disable TLS:

boot2docker ssh
sudo -i
echo DOCKER_TLS="no" > /var/lib/boot2docker/profile
exit
exit
boot2docker start

You’ll also need to update environment variables:

unset DOCKER_TLS_VERIFY
unset DOCKER_CERT_PATH
export DOCKER_HOST=tcp://192.168.59.103:2375

The IP address of your boot2docker instance may be different, you can do boot2docker ip to find out what the IP is onyour system.

Run mitmproxy as a reverse proxy

Run mitmproxy as a reverse proxy:

mitmproxy -R http://192.168.59.103:2375

Tell your docker client to connect to mitmproxy

By default, mitmproxy listens on port 8000, so point your Docker client there:

export DOCKER_HOST=http://127.0.0.1:8080

Note that we’re hitting localhost (127.0.0.1) and not the IP address of the boot2docker instance.

Save request/response bodies

When you interact using the docker client, the requests and responses should be captured by mitmproxy. Hit “tab” to toggle between request and response, and hit “B” to save the request or response body that you’re currently viewing.

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Plotting a counting process in an IPython notebook

There are lots of interesting timeseries data in software systems: events that occur over time such as numbers of users signed up, or errors of a certain type.

I like visualizing these as a counting process, which is a cumulative count of the events over time.

Once upon a time, I would’ve done this in R, but I want to learn pandas so I can do more with IPython notebooks. However, I don’t want to give up R’s excellent ggplot2 library. Fortunately, there’s a Python port.

Here’s an IPython notebook that shows an example of a cumulative plot. The example data I used is the commit history of the OpenStack Neutron project. I suspect there’s a simpler way to do the data manipulation with pandas, but I’m just a beginner with the library.

The plot appears below:

OpenStack Neutron commits

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Start stopped instances that all have the same name

It’s worth learning the AWS CLI and jq in order to do one-off batch operations to EC2 instances. I needed to start a group of stopped instances that all had the same name. Here’s the one-liner:

aws ec2 describe-instances --filters \
Name=tag:Name,Values="name goes here" | \
jq ".Reservations[].Instances[].InstanceId" -r | \
xargs aws ec2 start-instances --instance-ids
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