request / response

a blog about the web, Go, and building things

(by Matt Silverlock)


Diving Into FiveThirtyEight's "Russian Troll Tweets" Dataset with BigQuery

•••

FiveThityEight recently released a dataset of what is believed to be ~3 million tweets associated with “Russian trolls”. These tweets are designed to spread misinformation (let’s not mince words: lies), and ultimately influence voters. If you haven’t read the linked article, I highly suggest you do that before continuing on.

Exploring a ~700MB+ CSV file isn’t hugely practical (it’s since been sharded into < 100MB chunks), and so I’ve made the tweets available as a public dataset via Google’s BigQuery analytics engine. BigQuery has a sizeable free tier of 1TB per month, which should allow a fair bit of exploration, even if you’re a student or if paid services present a challenge for you.

Note: This isn’t a BigQuery & SQL tutorial: for that, take a look at the documentation.

If you’re already familiar with BigQuery & accessing public datasets, then you can simply run the below to start exploring the data:

#standardSQL
SELECT
  author,
  COUNT(*) AS tweets,
  followers
FROM
  `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets`
GROUP BY
  author,
  followers
ORDER BY
  tweets DESC,
  followers DESC

For everyone else: read on.

Accessing the Dataset

We’re going to use the BigQuery web UI, so navigate to the BigQuery interface and select the project you want to access it from. You’ll see the fivethirtyeight_russian_troll_tweets table appear on the left-hand-side, in the Resource tab. From there, you can inspect the table russian_troll_tweets, look at the schema (also pasted below), and see a preview of the data.

name type mode
external_author_id FLOAT NULLABLE
author STRING NULLABLE
content STRING NULLABLE
region STRING NULLABLE
language STRING NULLABLE
publish_date TIMESTAMP NULLABLE
harvested_date TIMESTAMP NULLABLE
following INTEGER NULLABLE
followers INTEGER NULLABLE
updates INTEGER NULLABLE
post_type STRING NULLABLE
account_type STRING NULLABLE
new_june_2018 INTEGER NULLABLE
retweet INTEGER NULLABLE
account_category STRING NULLABLE

So with the data above, what can we do? We can look at how these tweets were amplified (updates), what language the tweet was posted in (what audience was it for?), and the direct audience of the account (followers). We don’t get details on the followers themselves however, which makes it hard to know how impactful the reach was: is it trolls/bots followed by other trolls, or members of the general Twitter populace?

Analyzing It

OK, let’s take a quick look at the data to get you thinking about it. We’ll answer:

  • Was there a specific account with a non-negligible fraction of tweets?
  • Which months saw the most activity?
  • Which tweets were the most amplified in each language?
-- Was there a specific account with a non-negligible fraction of tweets?
SELECT
  author,
  COUNT(*) AS count,
  FORMAT("%.2f", COUNT(*) / (
    SELECT
      COUNT(*)
    FROM
      `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets`) * 100) AS percent
FROM
  `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets`
GROUP BY
  author
ORDER BY
  percent DESC
LIMIT
  10

The EXQUOTE account was definitely a sizeable contributor, although there’s not an order-of-magnitude difference across the top 10.

author count percent
EXQUOTE 59652 2.01
SCREAMYMONKEY 44041 1.48
WORLDNEWSPOLI 36974 1.24
AMELIEBALDWIN 35371 1.19
TODAYPITTSBURGH 33602 1.13
SPECIALAFFAIR 32588 1.10
SEATTLE_POST 30800 1.04
FINDDIET 29038 0.98
KANSASDAILYNEWS 28890 0.97
ROOMOFRUMOR 28360 0.95
-- Which months saw the most activity?
SELECT
  FORMAT("%d-%d", EXTRACT(month
    FROM
      publish_date), EXTRACT(year
    FROM
      publish_date) ) AS date,
  COUNT(*) AS count
FROM
  `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets`
GROUP BY
  date
ORDER BY
  count DESC
LIMIT
    10

Unsuprisingly here, we see October 2016 (just prior to the election on Nov 8th) feature prominently, as well August 2017, in which the North Korean conversation escalated immensely.

date count
8-2017 191528
12-2016 155560
10-2016 152115
7-2015 145504
4-2017 136013
1-2017 135811
11-2015 132306
3-2017 128483
11-2016 123374
8-2015 119454
-- Which tweets were the most amplified (likes, retweets) by language?
SELECT
  language,
  content,
  updates
FROM (
  SELECT
    language,
    content,
    updates,
    RANK() OVER (PARTITION BY language ORDER BY updates DESC) AS tweet_rank
  FROM
    `silverlock-bigquery.public_datasets.fivethirtyeight_troll_tweets`
  GROUP BY
    language,
    updates,
    content ) troll_tweets
WHERE
  tweet_rank = 1
GROUP BY
  language,
  content,
  updates
ORDER BY
  updates DESC
LIMIT
  10

I’ll leave analyzing these tweets as an exercise to the reader, but they certainly appear to prey on the hot button issues in a few places. Also note that I’ve truncated the output here, for brevity. Also be mindful of any links you follow here: I have not vetted them.

language truncated_content updates
English ‘@JustinTrudeau Mr. Trudeau, Canadian citizens dem 166113
Turkish KARMA, KARMA, KARMA!!! https://t.co/Eh5XUyILeJ 165833
Catalan ‘@HCDotNet Excellent! 🇺🇸👠🠻😆’ 165751
Farsi (Persian) Shameful https://t.co/rll2JrUzRI 165468
Dutch Trump’s tweets. #ThingsITrustMoreThanCNN https:/ 165407
Norwegian #2018PredictionsIn5Words Pro-Trump landslide 165371
Vietnamese So sad. @TitosVodka rocks!! https://t.co/sWtLlZxL5 164288
Lithuanian Stump for Trump @Stump4TrumpPac https://t.co/S0NS9 164082
Estonian #QAnon @Q #FOLLOWTHEWHITERABBIT 🠇 #FLYSIDFLY# 163448
Croatian ‘@FoxNews @rayann2320 @POTUS Bravo Mr President!!’ 163126

Wrap

There’s a lot of data to explore here, but it’s also worth keeping in mind that three (3) million tweets is only a small fraction of tweets associated with this kind of content, and this kind of bounded data collection may have some subjectivity to it.

If you have any questions about the dataset itself, you should open an issue on FiveThirtyEight’s GitHub repository. As for questions about exploring it via BigQuery: feel free to tweet @elithrar with your questions or explorations!


From Firestore to BigQuery with Firebase Functions

•••

In building my sentiment analysis service, I needed a way to get data into BigQuery + Data Studio so I could analyze trends against pricing data. My service (on App Engine) uses Firestore as its primary data store as an append-only log of all analysis runs to date.

The flexible schema (especially during development), solid Go client library & performance story were major draws, but one of the clear attractions was being able to trigger an external Firebase Function (Cloud Function) on Firestore events. Specifically, I wanted to get the results of each analysis run into BigQuery so I could run queries & set up Data Studio visualizations as-needed.

I wrote a quick function that:

  • Triggers on each onCreate event to Firestore
  • Pulls out the relevant fields I wanted to analyze in BigQuery: counts, aggregates and the search query used
  • Inserts them into the configured BigQuery dataset & table.

With that data in BigQuery, I’m able pull it into Data Studio, generate charts & analyze trends over time.

Creating the Function

If you haven’t created a Firebase Function before, there’s a great Getting Started guide that steps you through installing the SDK, logging in, and creating the scaffolding for your Function.

Note: Firebase Functions initially need to be created & deployed via the Firebase CLI, although it sounds like Google will support the Firebase-specific event types within Cloud Functions & the gcloud SDK (CLI) in the not-too-distant future.

Within index.js, we’ll require the necessary libraries, and export our sentimentsToBQ function. This function has a Firestore trigger: specifically, it triggers when any document that matches /sentiment/{sentimentID} is created (onCreate). The {sentimentID} part is effectively a wildcard: it means “any document under this path”.

const functions = require("firebase-functions")
const BigQuery = require("@google-cloud/bigquery")

exports.sentimentsToBQ = functions.firestore
  .document("/sentiments/{sentimentID}")
  .onCreate(event => {
    console.log(`new create event for document ID: ${event.data.id}`)

    // Set via: firebase functions:config:set centiment.{dataset,table}
    let config = functions.config()
    let datasetName = config.centiment.dataset || "centiment"
    let tableName = config.centiment.table || "sentiments"
    let bigquery = new BigQuery()

We can use the Firebase CLI to override the config variables that define our dataset & table names as needed via firebase functions:config:set centiment.dataset "centiment"- useful if we want to change the destination table during a migration/copy.

let dataset = bigquery.dataset(datasetName)
dataset.exists().catch(err => {
  console.error(
    `dataset.exists: dataset ${datasetName} does not exist: ${JSON.stringify(
      err
    )}`
  )
  return err
})

let table = dataset.table(tableName)
table.exists().catch(err => {
  console.error(
    `table.exists: table ${tableName} does not exist: ${JSON.stringify(err)}`
  )
  return err
})

We check that the destination dataset & table exist - if they don’t, we return an error. In some cases you may want to create them on-the-fly, but here we expect that they exist with a specific schema.

let document = event.data.data()
document.id = event.data.id

let row = {
  insertId: event.data.id,
  json: {
    id: event.data.id,
    count: document.count,
    fetchedAt: document.fetchedAt,
    lastSeenID: document.lastSeenID,
    score: document.score,
    variance: document.variance,
    stdDev: document.stdDev,
    searchTerm: document.searchTerm,
    query: document.query,
    topic: document.topic,
  },
}

The event.data.data() method returns the current state of the Firestore document, which is what we want to insert. The previous state of the document can also be accessed via event.data.previous.data(), which could be useful if we were logging specific deltas (say, a field changes by >= 10%) or otherwise tracking per-field changes within a document.

Note that we define an insertId to prevent duplicate rows in the event the function fails to stream the data and has to retry. The insertId is simply the auto-generated ID that Firestore provides, which is exactly what we want to de-duplicate a record on should it potentially be inserted twice, as our application treats Firestore as an append-only log. If we were expecting multiple writes to a record every minute, and wanted to stream those to BigQuery as distinct documents, we would need to use a different approach.

Beyond that, we compose an object with explicit columnName <=> fieldName mappings, based on our BigQuery schema. We don’t need every possible field from Firestore - only the ones we want to run analyses on. Further, since Firestore has a flexible schema, new fields added to our Firestore documents may not exist in our BigQuery schema.

The last part of our function is responsible for actually inserting the row into BigQuery: we call table.insert and set raw: true in the options, since we’re passing a row directly:

return table.insert(row, { raw: true }).catch(err => {
  console.error(`table.insert: ${JSON.stringify(err)}`)
  return err
})

As table.insert is a Promise, we should return the Promise itself, which will either resolve (success) or reject (failure). Because we don’t need to do any post-processing in the success case, we only explicitly handle the rejection, logging the error and returning it to signal completion. Not returning the Promise would cause the function to return early, and potentially prevent execution or error handling of our table.insert. Not good!

Deploying

Deploying our function is straightforward:

# Deploys our function by name
$ firebase deploy --only functions:sentimentsToBQ

=== Deploying to 'project-name'...
i  deploying functions
i  functions: ensuring necessary APIs are enabled...
✔  functions: all necessary APIs are enabled
i  functions: preparing _functions directory for uploading...
i  functions: packaged _functions (41.74 KB) for uploading
✔  functions: _functions folder uploaded successfully
i  functions: current functions in project: sentimentsToBQ
i  functions: uploading functions in project: sentimentsToBQ
i  functions: updating function sentimentsToBQ...
✔  functions[sentimentsToBQ]: Successful update operation.

Deployment takes about 10 - 15 seconds, but I’d recommend using the local emulator to ensure the functions behaves as expected.

Querying in BigQuery

So how do we query our data? We use the BigQuery console or the bq CLI. We’ll use the command line tool here, but the query is still the same:

bq query --nouse_legacy_sql 'SELECT * FROM `centiment.sentiments` ORDER BY fetchedAt LIMIT 5;'
Waiting on bqjob_r1af4578a67b94241_000001618c40385c_1 ... (1s)
Current status: DONE

+----------------------+---------+---------------------+-------+
|          id          |  topic  |        score        | count |
+----------------------+---------+---------------------+-------+
| PSux4gwOsHyUGqqdsdEI | bitcoin | 0.10515464281605692 |    97 |
| ug8Zm5sSZ2dtJXPIQWKj | bitcoin |  0.0653061231180113 |    98 |
| 63Qo2gRgsG7Cz2zywKOO | bitcoin | 0.09264705932753926 |    68 |
| Y5sraBzPrhBzsmOyHcm3 | bitcoin | 0.06601942062956613 |   103 |
| r3XApKXJ6feglUcyG1db | bitcoin | 0.13238095435358221 |   105 |
+----------------------+---------+---------------------+-------+
# Note that I've reduced the number of columns returned so it fits in the blog post

We can now see the results that we originally wrote to Firestore, and run aggregations, analyses and/or export them to other formats as needed.

sentiment-analysis-in-data-studio

The Code

For the record, here’s the full function as it is in production at the time of writing:

const functions = require("firebase-functions")
const BigQuery = require("@google-cloud/bigquery")

exports.sentimentsToBQ = functions.firestore
  .document("/sentiments/{sentimentID}")
  .onCreate(event => {
    console.log(`new create event for document ID: ${event.data.id}`)

    // Set via: firebase functions:config:set centiment.{dataset,table}
    let config = functions.config()
    let datasetName = config.centiment.dataset || "centiment"
    let tableName = config.centiment.table || "sentiments"
    let bigquery = new BigQuery()

    let dataset = bigquery.dataset(datasetName)
    dataset.exists().catch(err => {
      console.error(
        `dataset.exists: dataset ${datasetName} does not exist: ${JSON.stringify(
          err
        )}`
      )
      return err
    })

    let table = dataset.table(tableName)
    table.exists().catch(err => {
      console.error(
        `table.exists: table ${tableName} does not exist: ${JSON.stringify(
          err
        )}`
      )
      return err
    })

    let document = event.data.data()
    document.id = event.data.id

    let row = {
      insertId: event.data.id,
      json: {
        id: event.data.id,
        count: document.count,
        fetchedAt: document.fetchedAt,
        lastSeenID: document.lastSeenID,
        score: document.score,
        variance: document.variance,
        stdDev: document.stdDev,
        searchTerm: document.searchTerm,
        query: document.query,
        topic: document.topic,
      },
    }

    return table.insert(row, { raw: true }).catch(err => {
      console.error(`table.insert: ${JSON.stringify(err)}`)
      return err
    })
  })

Windows Subsystem for Linux w/ zsh, tmux & Docker

•••

I recently put together a Windows machine for gaming, and although I still do most of my development on macOS due to a great third-party ecosystem, BSD underpinnings & better programming language support, I decided to see what development life was like on Windows in 2018.

As a spoiler: it’s not perfect, but it’s definitely usable day-to-day. If you’re developing applications that don’t rely on OS-level differences (e.g. not systems programming), you can certainly use a Windows + Windows Subsystem for Linux (WSL) as your only setup. If you’re working with container-based applications, then it becomes even more usable.

I’m going to walk through a setup that gets you up & running with a few staples, namely:

First Things First

You’ll need to enable and install the Windows for Linux Subsystem. Basic familarity with the Linux CLI is also useful here: although this is a step-by-step guide, knowing how to edit text files with vim or nano is going to be helpful.

Hyper (your terminal)

Hyper is a fairly new terminal application, and although it’s not as polished as the venerable iTerm on macOS, it gets the job done. It uses the same underpinnings as the integrated terminal in VSCode (xterm.js), which means it sees regular releases and bug-fixes.

Out of the box, Hyper will use the Windows command prompt (cmd.exe) or Powershell (powershell.exe). In order to have it use your WSL shell, you’ll need to make a quick adjustment.

In Hyper, head to Edit > Preferences and modify the following keys:

    shell: 'wsl.exe',

    // for setting shell arguments (i.e. for using interactive shellArgs: ['-i'])
    // by default ['--login'] will be used
    shellArgs: [],

Note that if you have multiple Linux distributions installed via WSL, and you don’t want Hyper to use your default, you can set the value for shell to (e.g.) 'ubuntu.exe'.

Hyper is extremely configurable, and the awesome-hyper repository over on GitHub includes a long list of themes, plugins and tweaks.

zsh + ohmyzsh (your shell)

We’re also going to set up zsh as our default shell, alongside Oh My Zsh for it’s built-ins, themes and plugins.

First, confirm that zsh is available and installed (it should be, by default):

~ which zsh
/usr/bin/zsh

And then change your default shell to zsh:

~ chsh -s /usr/bin/zsh
# Enter your password, and hit enter
# Confirm the change
~ echo $SHELL
/usr/bin/zsh

We can now install oh-my-zsh -

# As per the instructions here: https://github.com/robbyrussell/oh-my-zsh#basic-installation
# NOTE: Don't just install any old program by piping a file into sh. Anything your user can do, the script can do. Make sure you at least trust the source of the script.
~ sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Once complete, you can begin tweaking things as per the README https://github.com/robbyrussell/oh-my-zsh#using-oh-my-zsh

tmux

tmux, if you’re not familiar, is a terminal multiplexer. Think of it as a way to run multiple shells quickly-and-easily, either in a grid-like fashion, or via a “tab” paradigm (or both). It’s extremely useful for multi-tasking: edit code or configs in one pane, watch results in another, and tail -f a log in a third.

The tmux version (2.1) available under Ubuntu 16.04 is getting on, and thus we’ll be building our version (2.6, at the time of writing) from source.

# Fetch the latest version of tmux from this page - e.g.
curl -so tmux-2.6.tar.gz https://github.com/tmux/tmux/releases/download/2.6/tmux-2.6.tar.gz
# Unpack it
~ tar xvf tmux-2.6.tar.gz
~ cd tmux-2.6.tar.gz
# Install the dependencies we need
~ sudo apt-get install build-essential libevent-dev libncurses-dev
# Configure, make & install tmux itself
~ ./configure && make
~ sudo make install
# Confirm it works
~ tmux

We’ll also want zsh to create (or use an existing) tmux session if available, so that we’re always in tmux. Let’s modify .zshrc to achieve that:

# open .zshrc in your preferred editor - e.g. vim
alias tmux="tmux -2 -u"if which tmux 2>&1 >/dev/null; thentest -z "$TMUX" && (tmux attach || tmux new-session)fi

We’ll now make sure zsh uses this updated config:

~ source .zshrc

Visual Studio Code

We have a standalone terminal w/ zsh + Oh My Zsh installed. Let’s make sure VSCode uses it for those times we’re using its integrated terminal. We’ll also want it to launch Hyper as our external terminal application, rather than cmd.exe or Powershell.

Open up VSCode’s preferences via File > Preferences > Settings (Ctrl+,) and update the following keys:

    "terminal.external.windowsExec": "%userprofile%\\AppData\\Local\\hyper\\Hyper.exe",
    "terminal.integrated.shell.windows": "wsl.exe"

Note: VSCode extensions that rely on background daemons or language servers to provide static analysis, formatting and other features will still use (require) the Windows-based version of these tools by default. There’s an open issue tracking this for Go, but it’s not a solved problem yet.

Docker

We’re also going to install Docker, via Docker for Windows (the daemon) and the Docker CLI (the client, effectively) within our WSL environment. This allows us to make use of Hyper-V and maintain good performance from our containerized applications, and avoid the minefield that is VirtualBox.

Once you’ve installed Docker for Windows—which may require rebooting to install Hyper-V, if not already enabled—you’ll also need to allow connections from legacy clients in the Docker settings. Check “Expose daemon on tcp://localhost:2375 without TLS”.

Note that this reduces the security of your setup slightly: other services already running on your machine could MitM connections between the Docker daemon. This does not expose the daemon to the local network, but there does not appear to be a way to retain TLS authentication between WSL and Docker for Windows yet.

# Install our dependencies
~ sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
# Add the Docker repository
~ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
~ sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) edge"
~ sudo apt-get update
# Install Docker Community Edition
~ sudo apt-get install -y docker-ce
# Add your user to the Docker group
~ sudo usermod -aG docker $USER

We’ll also need to tell our Docker client (inside WSL) how to connect to our Docker daemon (Docker on Windows).

# Persist this to shell config
~ echo "export DOCKER_HOST=tcp://0.0.0.0:2375" >> $HOME/.zshrc
~ source ~/.zshrc
# Check that Docker can connect to the daemon (should not get an error)
~ docker images

If you see any errors about not being able to find the Docker host, make sure that Docker for Windows is running, that you’ve allowed legacy connections in settings, and that echo $DOCKER_HOST correctly returns tcp://0.0.0.0:2375 in the same shell as you’re running the above commands in.

Now, let’s verify that you can run a container and connect to an exposed port:

~ docker run -d -p 8080:80 openresty/openresty:latest
4e0714050e8cc7feac0183a687840bdab67bbcc2dce21ae7170b52683a548de3
~ curl localhost:8080
<!DOCTYPE html>
<html>
<head>
<title>Welcome to OpenResty!</title>
...

Perfect!

Note: The guide by Nick Janetakis covers more of the details, including getting working mount points up-and-running.

What Else?

It’s worth noting that with Ubuntu 16.04.3 being an LTS release, software versions in the official repositories can be fairly out of date. If you’re relying on later versions of tools, you’ll need to either add their official package repositories (preferred; easier to track updates), install a binary build (good, but rarely self-updating), or build from source (slower, no automatic updates).

As additional tips:

  • Yarn (the JS package manager) provides an official package repository, making it easy to keep it up-to-date.
  • Ubuntu 16.04’s repositories only have Go 1.6 (3 versions behind as of Dec 2017), and thus you’ll need to download the binaries - keeping in mind you’ll need to manually manage updates to newer Go patch releases and major versions yourself.
  • Similarly with Redis, 3.0.6 is available in the official repository. Redis 4.0 included some big changes (inc. the module system, eviction changes, etc) and thus you’ll need to build from source

This is reflective of my experience setting up WSL on Windows 10, and I’ll aim to keep it up-to-date as WSL improves over time—esp. around running later versions of Ubuntu. If you have questions or feedback, ping me on Twitter @elithrar.


Automatically Build Go Binaries via TravisCI & GitHub

•••

Update: I’ve updated the travis.yml config to reflect Go 1.11.

GitHub has a great Releases feature that allows you surface—and users to download—tagged releases of your projects.

By default, Releases will provide links to a ZIP and a tarball of the source code for that tag. But for projects with binary releases, manually building and then uploading binaries (perhaps for multiple platforms!) is time-consuming and fragile. Making binary releases available automatically is great for the users of a project too: they can use it without having to deal with toolchains (e.g. installing Go) and environments. Making software usable by non-developer is an important goal for many projects.

We can use TravisCI + GitHub Releases to do all of the work for us with a fairly straightforward configuration, so let’s take a look at how to release Go binaries automatically.

Configuration

Here’s the full .travis.yml from a small utility library I wrote at my day job. This will:

  • Always build on the latest Go version - “go: 1.x” and sets an env variable. We’ll use this to only build binaries using the latest Go version.
  • Build as far back as 1.7
  • Builds, but doesn’t fail the entire run, on “tip” (e.g. Go’s master branch, which breaks from time-to-time)

It then runs a fairly straightforward build script using Go’s existing tooling: gofmt (style), go vet (correctness), and then any tests with the race detector enabled.

The final step—and the reason why you’re probably reading this post!—is invoking gox to build binaries for Linux, Darwin (macOS) & Windows, and setting the “Rev” variable to the git commit it was built from. The latter is super useful for debugging or supporting users when combined with a –version command-line flag. We also only release on tagged commits via tags: true so that we’re only releasing binaries with intent. Tests are otherwise automatically run on every branch (inc. Pull Requests).

language: go
sudo: false
matrix:
  include:
    # "1.x" always refers to the latest Go version, inc. the patch release.
    # e.g. "1.x" is 1.11 until 1.11.1 is available.
    - go: 1.x
      env: LATEST=true
    - go: 1.7.x
    - go: 1.8.x
    - go: 1.9.x
    - go: 1.10.x
    - go: 1.11.x
    - go: tip
  allow_failures:
    - go: tip

before_install:
  # gox simplifies building for multiple architectures
  - go get github.com/mitchellh/gox

install:
  - # skip

script:
  - go get -t -v ./...
  - diff -u <(echo -n) <(gofmt -d .)
  - go vet $(go list ./... | grep -v /vendor/)
  - go test -v -race ./...
  # Only build binaries from the latest Go release.
  - if [ "${LATEST}" = "true" ]; then gox -os="linux darwin windows" -arch="amd64" -output="logshare.." -ldflags "-X main.Rev=`git rev-parse --short HEAD`" -verbose ./...; fi

deploy:
  provider: releases
  skip_cleanup: true
  api_key:
    # Your *encrypted* GitHub key, as the output of the Travis CI CLI tool.
    secure: 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
  file:
  # The names of the binaries to output, based on the -output template passed to gox.
  - logshare.windows.amd64.exe
  - logshare.darwin.amd64
  - logshare.linux.amd64
  on:
    # What to repository to build
    repo: username/reponame
    # Only build binaries for tagged commits
    tags: true
    condition: $LATEST = true

Note: It’s critical that you follow TravisCI’s documentation on how to securely encrypt your API key—e.g. don’t paste your raw key into this file, ever. TravisCI’s documentation and CLI tool make this straightforward.

Wrap

Pretty easy, right? If you’re already using Travis CI to test your Go projects, extending your configuration to release binaries on tagged versions is only a few minutes of work.

Further Reading


A Node + TypeScript Starter

•••

TL;DR: If you want a simple template/boilerplate to get you started with TypeScript + Node.js, clone this repo.

As I started spending more time writing JavaScript, the more I missed a stricter type-system to lean on. TypeScript seemed like a great fit, but coming from Go, JavaScript/TypeScript projects require a ton of configuration to get started. Knowing what dependencies you need (typescript, tslint, node-ts), linter configuration (tslint.json), and putting together the right tsconfig.json for a Node app (vs. a browser app) wasn’t well documented. I wanted to compile to ES6, use CommonJS modules (what Node.js consumes), and generate type definitions alongside the .js files so that editors like VS Code (or other TypeScript authors) can benefit.

After doing this for a couple of small projects, I figured I’d had enough, and put together node-typescript-starter, a minimal-but-opinionated configuration for TypeScript + Node.js. It’s easy enough to change things, but it should provide a basis for writing code rather than configuration.

To get started, just clone the repo and write some TypeScript:

git clone https://github.com/elithrar/node-typescript-starter.git
# Then: replace what you need to in package.json, update the LICENSE file
yarn install # or npm install
# Start writing TypeScript!
open src/App.ts

… and then build it:

yarn build
# Outputs:
# yarn build v0.22.0
# $ tsc
# ✨  Done in 2.89s.

And that’s it. PR’s are welcome, keeping in mind the intentionally minimal approach.



© 2018 Matt Silverlock | His photo journal | Code snippets are MIT licensed | Built with Jekyll