• Posted on

    Atlas: Declarative schemas for SQL databases

    Database migrations are often one of the most dreaded parts of working with SQL databases. In many cases, teams end up keeping a bunch of timestamped .sql files and automatically running them in order. While this work, it doesn’t scale well: the more developers you have working on your schema, the more you increase the risk of conflicts. Atlas solves this problem by offering you to define your SQL schemas in a declarative manner, without requiring you to use a specific ORM.

    A map and glasses
    Image by Dariusz Sankowski

    Imperative vs Declarative

    The traditional way of managing database schemas is through imperative changes. It consists of describing the steps required to achieve the state you want. This is consistent with the tools given to us by SQL: once you have created a table with CREATE TABLE, you’re not supposed to re-use CREATE TABLE but to append changes with ALTER TABLE.

    Declarative programming works by describing the state you expect and letting your tools figure out how to get there. This is similar to what can be found in languages like Prolog or LISP, or tools like terraform. Declarative solutions are a great match for infrastructure problems because they allow you to keep track of the expected state of your systems, and let you use git for versioning and merging changes. This removes toil from the ops team while increasing visibility on the system’s state.

    Getting started with Atlas

    To create a table with Atlas, you just have to write a CREATE TABLE SQL statement. Like the following, which I will write in a schema.sql:

    CREATE TABLE `users` (
        `id` int NOT NULL,
        `name` varchar(100) NULL,
        `password` char(88) NOT NULL,
        PRIMARY KEY (`id`)

    To run this in my database, you can use atlas schema apply, which takes the URL of your database using -u, the file path using --to, and the address of a test database (for linting) using --dev-url (you can use docker:// to have Atlas launch an ephemeral docker container).

    atlas schema apply \
    	-u "mysql://root:pass@localhost:3306/db" \
    	--to "file://schema.sql" \
    	--dev-url "docker://mysql/8/db

    Now if you want to modify the table you created, you can modify the CREATE TABLE statement in schema.sql. For example, I turn the name columns into NOT NULL:

    CREATE TABLE `users` (
        `id` int NOT NULL,
        `name` varchar(100) NOT NULL,
        `password` char(88) NOT NULL,
        PRIMARY KEY (`id`)

    Rerunning the same atlas schema apply command will modify the database appropriately. In the output, we can see that Atlas generated the following SQL statement to achieve that:

    ALTER TABLE `users` MODIFY COLUMN `name` varchar(100) NOT NULL;

    Using HCL

    Atlas also allows you to use HCL to define schemas. You might already be familiar with HCL if you have been using Terraform, but if not, the main benefit is that it is easy to read and modify for both humans and computers. This is interesting if you ever need your CI to interact with your database schemas.

    The users table from the previous example would look like this in HCL:

    table "users" {
      schema = schema.db
      column "id" {
        null = false
        type = int
      column "name" {
        null = false
        type = varchar(100)
      column "password" {
        null = false
        type = char(88)
      primary_key {
        columns = [column.id]

    As a bonus, HCL doesn’t require a database for linting, so if you choose to use this, you can skip the --dev-url parameter when applying.

    Image by Michael Gaida

    Migrating an existing system

    With an already running database, you can use atlas schema inspect to generate the schema files. It also requires a -u parameter for the database URL and --format for the output format (SQL/HCL/JSON).

    atlas schema inspect \
      -u "mysql://root:pass@localhost:3306/db" \
      --format '{{ sql . }}'

    If you are already using an imperative migration system, and do not want to throw it away right now, you can also use Atlas to generate migration files. This is done using the atlas migrate diff [migration_name] command, which takes a --dir parameter for the migration folder, a --to parameter for the schema file, and a --dev-url parameter like the apply command.

    atlas migrate diff my_migration \
      --dir "file://migrations" \
      --to "file://schema.sql" \
      --dev-url "docker://mysql/8/test"

    Use atlas migrate apply to run the migrations:

    atlas migrate apply \
      -u "mysql://root:pass@localhost:3306/db" \
      --dir "file://migrations"

    Integrating Atlas to your Go codebase

    After using Atlas for a bit, you might need to write a wrapper for it or want to use it as part of your custom infrastructure tooling. This is made simple with their Go SDK. The packages ariga.io/atlas-go-sdk/atlasexec will expose all the high-level functions that you can use for that.

    The example below behaves like atlas schema apply:

    func main() {
        client, err := atlasexec.NewClient(".", "atlas")
        if err != nil {
            log.Fatalf("failed to initialize client: %v", err)
        params := atlasexec.SchemaApplyParams{
            URL: "mysql://root:pass@localhost:3306/db",
            To: "file://schema.hcl",
        res, err := client.SchemaApply(context.Background(), &params)
        if err != nil {
            log.Fatalf("Failed to apply schema: %v", err)
        log.Printf("Applied %d changes (%d pending)\n", len(res.Changes.Applied), len(res.Changes.Pending))
  • Posted on

    5 kubectl plugins to make your life easier

    I have been using Kubernetes for five years, but only very recently started using plugins to enhance my kubectl commands. I will show you five plugins that help me avoid repetitive tasks, make cluster administration simpler, and incident response less stressful. All the plugins presented in this article are installable using Krew.

    Blue lego bricks
    Photo by Iker Urteaga

    Note for Mac users

    If you’re using an ARM Mac, most of the plugins I mention will appear uninstallable when using Krew. It is generally because the plugin authors didn’t release a mac-arm64 build. But you can install the mac-amd64 builds, which work as well, by overriding the KREW_ARCH environment variable. For example:

    KREW_ARCH=amd64 kubectl krew install janitor


    Logging pods through kubectl logs -f is always a good way to know what a running pod is doing. Sadly, I never manage to remember how to make it log multiple pods at once. The tail plugins solve that, by giving us a set of helper functions to easily stream the logs of a group of pods. For example, it can retrieve logs from all the pods created by a Job, or all the pods attached to a Service:

    ❯ k tail --job=logging-job
    default/logging-job-xtx4s[busybox-container]: My log
    ❯ k tail --svc=mikochi
    default/mikochi-69d47757f6-9nds7[mikochi]: [GIN] 2023/07/27 - 12:31:16 | 200 |     496.098µs | | GET      "/api/refresh"
    default/mikochi-69d47757f6-9nds7[mikochi]: [GIN] 2023/07/27 - 12:31:16 | 200 |   10.347273ms | | GET      "/api/browse/"
    default/mikochi-69d47757f6-9nds7[mikochi]: [GIN] 2023/07/27 - 12:31:16 | 200 |    9.598031ms | | GET      "/api/browse/"
    default/mikochi-69d47757f6-9nds7[mikochi]: [GIN] 2023/07/27 - 12:31:19 | 200 |     193.686µs | | GET      "/ready"


    Janitor is a kubectl plugin that allows you to list resources in a problematic state. Instead of battling with grep, it gives you access to commands to automatically list unhealthy, unready, or unscheduled Pods, failed Jobs, pending PVCs, and or unclaimed PVs. This is helpful when examining a cluster during an incident, as it can directly point you toward ongoing issues.

    ❯ k janitor pods status
    STATUS             COUNT
    Running            4
    Error              6
    ImagePullBackOff   1
    ❯ k janitor pods unhealthy
    NAME                 STATUS             AGE
    failing-job-ln7rf    Error              4m40s
    failing-job-vbfqd    Error              4m33s
    failing-job2-kmxqm   Error              4m30s
    failing-job-cjbt6    Error              4m27s
    failing-job2-grwcn   Error              4m23s
    failing-job2-s842x   Error              4m17s
    my-container         ImagePullBackOff   17m
    ❯ k janitor jobs failed
    NAME           REASON                 MESSAGE                                       AGE
    failing-job    BackoffLimitExceeded   Job has reached the specified backoff limit   4m46s
    failing-job2   BackoffLimitExceeded   Job has reached the specified backoff limit   4m36s


    Neat is a simple utility to remove generated fields from the command output. You can use it by simply piping the output of kubectl get into kubectl neat. This makes for a more readable output and is very convenient if you want to save the yaml to create a new resource.

    ❯ k get pod -o yaml mikochi-69d47757f6-9nds7
    apiVersion: v1
    kind: Pod
      creationTimestamp: "2023-07-21T12:30:58Z"
      generateName: mikochi-69d47757f6-
        app.kubernetes.io/instance: mikochi
        app.kubernetes.io/name: mikochi
        pod-template-hash: 69d47757f6
      name: mikochi-69d47757f6-9nds7
      namespace: default
    ❯ k get pod -o yaml mikochi-69d47757f6-9nds7 | k neat
    apiVersion: v1
    kind: Pod
        app.kubernetes.io/instance: mikochi
        app.kubernetes.io/name: mikochi
        pod-template-hash: 69d47757f6
      name: mikochi-69d47757f6-9nds7
      namespace: default


    Since the data inside Secrets is base64 encoded, reading them often results in a mix of kubectl get, jq, and base64 -d. The view-secret plugin aims at simplifying that, by allowing you to directly read and decrypt values from secrets.

    ❯ k view-secret mikochi username
    ❯ k view-secret mikochi password


    If you want to directly access a node, finding the node IP, using SSH with the right RSA key, etc… can make you lose precious time during an incident. But it is possible to obtain a root shell from a (privileged) container using nsenter. The node-shell plugin leverages this to give you access to the nodes in a single kubectl command:

    ❯ k node-shell my-node
    spawning "nsenter-qco8qi" on "my-node"
    If you don't see a command prompt, try pressing enter.
    root@my-node:/# cat /etc/rancher/k3s/k3s.yaml
    apiVersion: v1
    - cluster:
  • Posted on

    Is technical analysis just stock market astrology?

    Technical analysis is a part of finance that studies price moves to guide investment decisions. A lot of investors seem skeptical of the use of past price data, which leads to technical analysis often being perceived as similar to astrology. In this article, I will try to see if it can provide an edge to long-term investors and if it beats reading the horoscope.

    A screen showing multiple stock prices
    Photo by Chris Liverani

    Moving Average Crossover

    There are hundreds of different technical analysis strategies and indicators out there. For this article, I decided to pick what seemed to be one of the simplest: SMA crossovers.

    Simple Moving Averages (SMA) are, as the name suggests, just an average of past closing prices. The SMA Crossover strategy uses two moving averages, a “fast” one (50 days) and a “slow” one (200 days), and compares them to decide on buying or selling an asset. If the fast SMA is above the slow one, we should buy and hold the stock, and if this condition inverts, we should sell. It is a heuristic around momentum, which is the idea that if an asset price starts rising, we can jump on the bandwagon and hope it will continue its trajectory.

    The strategy would have for example indicated to buy and hold between June 2009 and June 2010

    I have backtested an SMA crossover strategy on the SPY ETF, one of the most traded passive investment funds in the world. My backtest makes the following assumptions:

    • we do not pay any transaction fees
    • we will obtain exactly the close price when buying or selling
    • dividends received while holding stocks are immediately reinvested
    • cash yields no interests
    The result of both backtests, showing similar performance for the SMA and buy and hold strategies

    While someone who would have bought 10.000$ of SPY in July 1998 and continuously held the stock would have ended up with 52.030$ in June 2023 (6.78% annualized), an investor using the SMA crossover strategy would have made ended up with 49.127$. (6.54%). Even without accounting for transaction costs, the SMA crossover strategy doesn’t provide a significant over-performance to simply buying and holding over a long period.

    Does this mean that this strategy is completely useless?

    Actually no, a closer look at the data will show that the SMA crossover strategy allows investors to avoid extended drawdowns, like the 2008 crisis. This significantly reduces the risk taken by investors: the yearly standard deviation of the SMA crossover strategy is only 13%, against 19% for the buy and hold strategy. Since returns didn’t decrease as much as risk, this leads to high risk-adjusted returns, as measured by the Sharpe ratio: 0.43 for passive investing vs 0.53 for SMA crossovers.

    But what about astrology?

    Of course, the subject of this article wasn’t to compare technical analysis to buy and hold, but to astrology. I was surprised to learn that financial astrology was actually a thing and that there were also a lot of astrology-related strategies out there. I decided to implement a strategy based on lunar cycles since it was one of the clearest about when to buy and when to sell.

    The strategy goes as follows: we purchase SPY on a new moon, and re-sell it on the next full moon. And repeat that every lunar month.

    The result of both backtests, showing the moon phases strategy failing to make any money

    Clearly, it fails at beating SMA crossovers. Or at doing basically anything, an investor using the moon phase strategy starting with 10.000$ would end up with only 11.110$ and a Sharpe ratio of only 0.09.


    Similarly to diversification, simple technical analysis strategies can be used to minimize investment risk, without necessarily reducing profits by the same amount. There’s however no guarantee that a strategy that worked in the past will continue working in the future. It is also likely that there exist smarter momentum indicators than moving averages, but you’ll have to do your own backtests for those.

  • Posted on

    Introducing Mikochi: a minimalist remote file browser

    Like many people working in DevOps, I have taken the bad habit to keep playing with servers and containers in my free time. One of the things I have running is a Media Server, which I use to access my collection of movies and shows (that I evidently own and ripped myself). To make my life easier with this, I have built a web application that allows me to browse, manage, and stream/download files. It is called Mikochi and received its first stable release last week.

    The mikochi UI

    Problem statement

    My media server was initially running Jellyfin. It is a pretty nice piece of software that probably fits the need of many people. Sadly for me, it focuses a lot on areas I didn’t care about (metadata, transcoding, etc) while being lackluster on classic file management.

    The features I need is to have basic FTP-like management from a browser. This means it needs to list the content of folders and allow navigation between them while allowing to download, rename, delete, and upload files.

    In addition to that, I also wanted a search function that could lead me to any file/directory in the server.

    Since it’s replacing a media server, the last requirement was streaming. I do not use streaming in the browser much (since it doesn’t always support fancy codecs like HEVC), so I just needed to be able to read it from a media player like VLC or MPV, which is easier.


    One of my aims in this project was to get back into frontend development since I didn’t touch a line of JavaScript in a while. For this project, I decided to use Preact, a React alternative weighing only 3kb.

    Preact was a great surprise. I expected a framework that small to be too good to be true, but it works well. I didn’t experience any trouble learning it since it is almost the same API as React and didn’t encounter any performance issues or unexplainable crashes. I will definitely try to use it again for future projects.

    The complete JS bundle size ends up being ~36kb, barely more than the icon pack that I use.

    Two small anime characters surrounded by grass
    The character who gave this software its name


    The backend was made using Go, which has been one of my main languages for the past 5 years. I used the Gin framework to handle the regular HTTP boilerplate, which worked admirably.

    The only pain point I had was re-implementing JWT authentication. I had decided to not use a library for that because I felt that, it might not handle an edge case well: I need tokens passed in GET params for streaming requests, since VLC isn’t going to write a Authorization header. It’s not particularly complex but it is a lot of code.

    I had the good surprise that streaming files “just works” in a single line of code:


    Running it

    If you’re interested in trying out Mikochi, it can be launched with just a Docker image:

    docker run \
    -p 8080:8080 -v $(PWD)/data:/data \
    -e DATA_DIR="/data" -e USERNAME=alicegg \
    -e PASSWORD=horsebatterysomething zer0tonin/mikochi:latest

    Compiled binaries are also available on GitHub. And for those who love fighting with Ingresses and PersistentVolumeClaims, there’s a helm chart available.

  • Posted on

    Specialization considered harmful

    It is sometimes recommended that software engineers should learn “depth-first”, and seek to specialize early in their careers. I think his advice is misguided. In my opinion, having a wide range of knowledge is in many cases more important than being extremely good at a very specialized task. I will use this article to make the case for avoiding specialization as a software engineer.

    A set of tools
    Photo by Kenny Eliason

    It’s not just about practice hours

    A common misconception when learning a new skill is that, since it might take 10,000 hours to master it, the best thing to do is to start practicing as early as possible and with as much focus as possible. Reality is however not as simple.

    It may be true that just putting in a lot of focused practice hours will lead to amazing results in problems that are very constrained in scope (like chess). However, for subjects that have a very broad, and frequently evolving set of problems, experience working on very diverse subjects will often perform better than intense specialization.

    One of the reasons behind that is that many problems that are at first sight unrelated will have similar patterns. Being exposed to a wide variety of problems allows you to see a lot of potential patterns between problems.

    This is why history has many records of people achieving breakthroughs in many fields. For example, Benoit Mandelbrot first noticed the concept of fractal by studying probability distributions in financial markets, he managed to find an application of the concept to many patterns that appear in nature, such as coastlines, clouds, and blood vessels.

    Tech changes, fast

    Many people underestimate how fast the world of software engineering can change. Some extremely popular concepts like “DevOps” were pretty much not a thing 10 years ago. 20 years ago, I doubt anyone would have known what differentiated a “frontend developer” from a “backend developer”. Even if you zoom on very specific technologies, things are changing every year: React code written in 2023 doesn’t have much in common with React code written in 2015.

    A retro-looking computer
    Photo by Lorenzo Herrera

    Being a generalist, allows you to adapt much faster to change, it can be seen as a way of “learning to learn”. Once you have been exposed to many problems and solutions, picking up new tools and adapting to changes in the field becomes easier and easier.

    There’s more to software engineering than code

    Most importantly, learning the ins and out of a programming language and tech stack is not what brings value. Software engineering is the art of using computers to solve problems. Those problems are generally not about computers, but involve businesses and people. In my experience, this is a point that is easy to miss when over-focusing on the depth of a specific technology.

    This is also where a lot of people who make a career change and transition late into the tech industry have an edge. They can compensate for their late start by being more aware of the reality of business and the needs of organizations.

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