Stupid Rust Tricks

Enforcing deadlines with a macro

Rust has a really powerful macro system. You can use it to do great things safely… or you can have fun with it and quickly prototype coding productivity features. I chose the latter.

todo-macro is a procedural Rust macro that stops compiling after a user-specified deadline. It’s like TODO comments, but with teeth. It’s probably best explained with an example:

Using todo-macro

It’s January 1, 2020. I’m working on some Rust code that compiles, but it’s not quite ready to ship.

I want to take a break, but I know myself – I’ll probably forget about the deficiency. I could add a TODO comment, but that depends on me actively searching for TODO comments next time I open the project.

To save me from myself, I add a quick todo macro with a deadline of January 2 (in ISO 8601 format):

// Implement the timeout handling

Linux .NET Development in 2019

What you need to know

I’ve recently been building .NET Core back-end services that run on Linux. Linux .NET development is in an interesting place; it’s clearly the future of back-end .NET, but it’s still a little rough around the edges compared to our old friend .NET-on-Windows.

Let’s dive into what you (an experienced Windows .NET developer or a .NET-curious Linux developer) need to know to start building .NET services for Linux. I’ll cover IDEs, service hosting, Linux system calls and more.

Background & Motivation

.NET development for Linux has been possible via Mono since 2004, but it was always a bit… fringe compared to Windows .NET development. That all changed when Microsoft released .NET Core in 2016 as a cross-platform .NET implementation; first-party support from Microsoft is a big deal to most .NET developers.

We’re also in a world where Linux is the lingua franca for back-end development; if you want to do anything involving cloud services, distributed systems, or containerization, Windows is typically an afterthought (if it’s supported at all). I want to skate to where the puck is going (sorry!), and it’s headed toward Linux.

Every now and then, I wonder “Is low-level programming still unpleasant?” Call it a morbid fascination, but I really did enjoy some aspects of working in C and C++; in particular, reasoning about low-level behaviour becomes a lot easier with fewer layers of abstraction on top of the metal. On the other hand: memory management and interoperability are hard, the C/C++ ecosystems have accumulated decades of cruft, and both languages are missing a lot of features that are now par for the course.

It turns out that the short answer is “No, because Rust is great”, but it’s still useful to get a feel for modern C and C++. Here are some projects and tools that I’ve found particularly indispensable for that.

Preface: Why Not Book Learnin'?

There are a lot of books out there that will teach you how to use newer C++ features. Effective Modern C++ is a popular one, but personally, that’s not what I’m looking for. I know that I learn best from hands-on experience, and so what I really wanted was to play with some decent-sized example projects using modern tooling.

CMU’s Research/Educational Databases for C++

I can’t say enough good things about CMU’s Database Group, and specifically professor Andy Pavlo’s work. CMU has 3 (yes, 3!) open source relational databases written in modern C++ on GitHub:

  1. BusTub, an educational system written for the Database Systems course
  2. Terrier, CMU’s current research database
  3. Peloton, CMU’s older research database

BusTub and Terrier use C++17, but Peloton uses C++11. I’d recommend BusTub and Terrier since they’re both under active development. Terrier has excellent documentation for getting up and running on the wiki. They recommend using CLion, a modern C/C++ IDE from JetBrains.

CMU’s even provided some small pieces of work to dip your toes in; the projects for the Database Systems course involve implementing basic functionality in BusTub, and grading scripts for the projects will be available soon.

New Project: ETL Sheets

Experiments with spreadsheet-inspired UI

I recently started experimenting with building tooling for ETL systems. After many years of wrestling with ETL in industry, I had a few questions on my mind:

  1. Can we make common data issues quick to resolve?
  2. Can we make automated data transformations as easy to work with as spreadsheets?

I think the answer to both questions is yes, but don’t take my word for it – you can try the prototype at (double-click on an issue to get started), and view the source code.


Importing data at scale is painful. Your data providers will screw up the formatting, systems will experience connectivity issues, and your transformation logic will fail on cases you didn’t expect. What if our tools focused on helping with those failures, instead of assuming the happy path?

Speaking of transformations, how should we write them? Some systems take a code-first approach, which is great for coders and impenetrable for everyone else. Others take a GUI-driven approach, which usually becomes the stuff of nightmares. I think we can do better, by drawing inspiration from a tool that’s found in every office:


Here’s what building a new transformation might look like, and here’s what it might look like when that transformation fails to run successfully.

Managing Postgres with Pulumi and Terraform

Adventures with Infrastructure as Code

I’m a big fan of Pulumi, and I’ve previously used it for cloud infrastructure. That’s their main selling point; Pulumi’s slogan is literally “Program the cloud." Interestingly, Pulumi can also be used to manage on-premise infrastructure, including PostgreSQL databases. Let’s dive into the details.

Pulumi basics

Pulumi takes a declarative infrastructure definition and handles provisioning said infrastructure, just like Terraform, AWS CloudFormation, and Azure Resource Manager. The main difference is that in Pulumi you’re writing real code (Node, Python, or Go) to build that infrastructure definition instead of a YAML or JSON file, it’s great.

Pulumi/Terraform PostgreSQL Provider

After Pulumi has executed your code to build up an infrastructure definition, it needs to interact with external infrastructure resources (cloud providers, databases, etc.) to turn that definition into a reality. Some of Pulumi’s functionality for working with external resources is derived from Terraform providers. For example, Pulumi has a PostgreSQL provider which is derived from the Terraform PostgreSQL provider.

At a high level, the PostgreSQL provider lets you define databases, roles, schemas, permissions, and PostgreSQL extensions. It stops short of letting you manage DDL objects and data within the database (which is probably for the best given the complexities of schema+data migrations).

Defining a Postgres database with TypeScript

Pulumi can be used to instantiate entire Postgres clusters, but for this tutorial let’s assume you already have an existing cluster managed outside of Pulumi. It can be running anywhere: in the cloud, in a container, on your local machine, whatever.

First, install Pulumi then instantiate a new TypeScript project with pulumi new typescript. This scaffolds a bare-bones Node+TypeScript Pulumi project with the following files:

index.ts                package-lock.json       tsconfig.json
Pulumi.yaml             node_modules            package.json

Install the Pulumi PostgreSQL provider with npm i @pulumi/postgresql (or just edit your package.json directly).

Next, we need to tell Pulumi which Postgres cluster to connect to and how. The PostgreSQL provider’s configuration points are documented here. My Postgres cluster is running on a local VM with the hostname fedora-vm, so I set the postgresql:host variable like so:

pulumi config set postgresql:host fedora-vm

This creates a YAML configuration file for the current Pulumi stack, or modifies one if it already exists. For my dev stack, this creates a file named with the following contents:

  postgresql:host: fedora-vm

You’ll want to do the same for postgresql:username and postgresql:password, to tell Pulumi which credentials to use. Sensitive configuration values like passwords can be encrypted using the --secret flag.

Finally, we’re ready to work with the fun stuff: configuring a database in real TypeScript code. Let’s say we want to instantiate a Pulumi-managed database, create a role with login permissions, and grant the role SELECT permission on tables in the public schema in the new database. This can all be done in the index.ts file like so:

import * as pulumi from "@pulumi/pulumi";
import * as postgresql from "@pulumi/postgresql";

const config = new pulumi.Config();

const managedDatabase = new postgresql.Database("managedDatabase", {
  name: "pulumi"

const publicReaderRole = new postgresql.Role("publicReaderRole", {
  login: true,
  name: "public_reader",
  password: config.require("publicReaderPassword")

const publicReaderSelectTablesGrant = new postgresql.Grant(
    objectType: "table",
    privileges: ["SELECT"],
    schema: "public"

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