Blog

Setting Forgejo and Forgejo actions with `Docker Compose`, with `Nix` based actions

Docker Compose Configuration

traefik:
  # ...
  command:
    # ...
    - "--entrypoints.ssh.address=:222"
  ports:
    # ...
    - "222:222"
forgejo:
    container_name: forgejo
    image: codeberg.org/forgejo/forgejo:11
    environment:
        - USER_UID=1000
        - USER_GID=1000
        - FORGEJO__database__DB_TYPE=postgres
        - FORGEJO__database__HOST=pgforgejo:5432
        - FORGEJO__database__NAME=forgejo
        - FORGEJO__database__USER=forgejo
        - FORGEJO__database__PASSWD=forgejo
    restart: always
    networks:
        - forgejo
        - <network name>
    volumes:
        - ./forgejo:/data
        - /etc/timezone:/etc/timezone:ro
        - /etc/localtime:/etc/localtime:ro
    # ports:
    #     - "3000:3000"
    #     - "222:22"
    depends_on:
        - pgforgejo
    labels:
        - "traefik.enable=true"
        - "traefik.http.routers.forgejo.rule=Host(`git.example.com`)"
        - "traefik.http.routers.forgejo.entrypoints=websecure"
        - "traefik.http.routers.forgejo.tls.certresolver=myhttpchallenge"
        - "traefik.http.routers.forgejo.service=forgejo"

        - "traefik.http.routers.forgejo-http.rule=Host(`git.example.com`)"
        - "traefik.http.routers.forgejo-http.entrypoints=web"
        - "traefik.http.routers.forgejo-http.middlewares=forgejo-redirect"
        - "traefik.http.middlewares.forgejo-redirect.redirectscheme.scheme=https"
        - "traefik.http.middlewares.forgejo-redirect.redirectscheme.permanent=true"

        - "traefik.http.services.forgejo.loadbalancer.server.port=3000"
        - "traefik.docker.network=<network name>"
        - "traefik.tcp.routers.forgejo-ssh.entrypoints=ssh"
        - "traefik.tcp.routers.forgejo-ssh.rule=HostSNI(`*`)"
        - "traefik.tcp.routers.forgejo-ssh.service=forgejo-ssh"
        - "traefik.tcp.services.forgejo-ssh.loadbalancer.server.port=22"

pgforgejo:
    container_name: pgforgejo
    image: postgres:17.6-alpine
    restart: always
    environment:
        - POSTGRES_USER=forgejo
        - POSTGRES_PASSWORD=forgejo
        - POSTGRES_DB=forgejo
    networks:
        - forgejo
    volumes:
        - ./pgforgejo:/var/lib/postgresql/data

docker-in-docker:
    container_name: docker-dind
    image: docker:dind
    privileged: "true"
    command: ["dockerd", "-H", "tcp://0.0.0.0:2375", "--tls=false"]
    restart: "unless-stopped"
    networks:
        - forgejo

forgejo-action:
    container_name: "forgejo-action"
    image: "data.forgejo.org/forgejo/runner:9"
    links:
        - docker-in-docker
    depends_on:
        docker-in-docker:
            condition: service_started
    environment:
        DOCKER_HOST: tcp://docker-in-docker:2375
    networks:
        - forgejo
    # User without root privileges, but with access to `./data`.
    user: 1001:1001
    volumes:
        - ./forgejo-data:/data
    restart: "unless-stopped"
    # command: '/bin/sh -c "while : ; do sleep 1 ; done ;"'
    command: '/bin/sh -c "sleep 5; forgejo-runner daemon"'

Forgejo Actions

# .runner
{
  "WARNING": "This file is automatically generated by act-runner. Do not edit it manually unless you know what you are doing. Removing this file will cause act runner to re-register as a new runner.",
  "id": 1,
  "uuid": "****",
  "name": "<runner name>",
  "token": "****",
  "address": "https://git.example.com",
  "labels": [
    "bookworm:docker://node:24-bookworm",
    "nix-base:docker://docker.nix-community.org/nixpkgs/nix-unstable:latest",
    "nix:docker://git.nexveridian.com/nexveridian/action-attic:latest"
  ]
}

Available runner images

UT Austin Class Schedule

SemesterCourse NameCategorie
2025 FallCase Studies in Machine LearningElective
2025 FallDeep LearningApplication
2026 FallParallel SystemsSystems

Using `serde_json` or `serde` data, in `datafusion`

Getting data into datafusion is not well documented, especially using serde_json or serde data.

This example shows how to convert a serde_json::Value::Array into a datafusion DataFrame, manipulate the dataframe in datafusion, then convert it back to serde_json.

# Cargo.toml
datafusion = "47.0.0"
serde_arrow = { version = "0.13.3", features = ["arrow-55"] }
// `serde_json::Value`
let json = serde_json::json!([{
    "date": "2025-06-05",
    "test": "test",
    "price": 1.01,
}]);

let ctx = SessionContext::new();

let serde_json::Value::Array(json_array) = &json else {
    return Err(anyhow::anyhow!("Expected JSON array, got different type"));
};

if json_array.is_empty() {
    return Ok(Vec::new());
}

// Configure `TracingOptions` to allow null fields and coerce numbers
let tracing_options = TracingOptions::default()
    .allow_null_fields(true)
    .coerce_numbers(true);

// Get the schema from actual data, using samples, with `TracingOptions`
let fields = Vec::<FieldRef>::from_samples(json_array, tracing_options)?;

// Convert `serde_json::Value::Array` to `RecordBatch` using `serde_arrow`
let record_batch = serde_arrow::to_record_batch(&fields, &json_array)?;

// Create a DataFrame from the `RecordBatch`
let mut df = ctx.read_batch(record_batch)?;

// Add a new column `new_col` using DataFrame API
df = df.with_column("new_col", lit("test".to_string()))?;

// Execute the DataFrame query
let result_batches = df.collect().await?;

// Convert back to `serde_json` using `serde_arrow`
let all_json_values = result_batches
    .into_iter()
    .flat_map(|batch| {
        serde_arrow::from_record_batch(&batch).unwrap_or_else(|_| Vec::new())
    })
    .collect::<Vec<serde_json::Value>>();

#[derive(Default, Debug, Clone, Deserialize, Serialize)]
pub struct TestData {
    date: String,
    test: String,
    price: f64,
    new_col: String,
}

// Convert the `serde_json::Value` to Vec<TestData>
let test_data: Vec<TestData> =
    serde_json::from_value(serde_json::Value::Array(all_json_values))?;

assert_eq!(
    test_data,
    Vec![
        TestData {
            date: "2025-06-05".to_string(),
            test: "test".to_string(),
            price: 1.01,
            new_col: "test".to_string(),
        },
    ]
);

Or you use can use this datafusion_ext

// src/utils/datafusion_ext.rs
use anyhow::Error;
use datafusion::{arrow::datatypes::FieldRef, dataframe::DataFrame, prelude::*};
use serde_arrow::schema::{SchemaLike, TracingOptions};

pub trait JsonValueExt {
    /// Converts a `serde_json::Value::Array` into a `datafusion::dataframe`
    fn to_df(&self) -> Result<DataFrame, Error>;
}

impl JsonValueExt for serde_json::Value {
    fn to_df(&self) -> Result<DataFrame, Error> {
        let ctx = SessionContext::new();

        let Self::Array(json_array) = self else {
            return Err(anyhow::anyhow!(
                "Expected `serde_json::Value::Array`, got different type"
            ));
        };

        if json_array.is_empty() {
            return Err(anyhow::anyhow!("Empty `serde_json::Value::Array` provided"));
        }

        let tracing_options = TracingOptions::default()
            .allow_null_fields(true)
            .coerce_numbers(true);

        let fields = Vec::<FieldRef>::from_samples(json_array, tracing_options)?;
        let record_batch = serde_arrow::to_record_batch(&fields, &json_array)?;

        let df = ctx.read_batch(record_batch)?;

        Ok(df)
    }
}

#[async_trait::async_trait]
pub trait DataFrameExt {
    /// Collects a `datafusion::dataframe` and deserializes it to a Vec of the
    /// specified type
    async fn to_vec<T>(&self) -> Result<Vec<T>, Error>
    where
        T: serde::de::DeserializeOwned;
}

#[async_trait::async_trait]
impl DataFrameExt for DataFrame {
    async fn to_vec<T>(&self) -> Result<Vec<T>, Error>
    where
        T: serde::de::DeserializeOwned,
    {
        let result_batches = self.clone().collect().await?;

        let all_json_values = result_batches
            .into_iter()
            .flat_map(|batch| serde_arrow::from_record_batch(&batch).unwrap_or_else(|_| Vec::new()))
            .collect::<Vec<serde_json::Value>>();

        let typed_result: Vec<T> =
            serde_json::from_value(serde_json::Value::Array(all_json_values))?;

        Ok(typed_result)
    }
}
use utils::datafusion_ext::{DataFrameExt, JsonValueExt};

let json = serde_json::json!([{
    "date": "2025-06-05",
    "test": "test",
    "price": 1.01,
}]);

let mut df = json.to_df()?;

df = df.with_column("new_col", lit("test".to_string()))?;

#[derive(Default, Debug, Clone, Deserialize, Serialize)]
pub struct TestData {
    date: String,
    test: String,
    price: f64,
    new_col: String,
}

let etfs = df.to_vec::<TestData>().await?;

assert_eq!(
    test_data,
    Vec![
        TestData {
            date: "2025-06-05".to_string(),
            test: "test".to_string(),
            price: 1.01,
            new_col: "test".to_string(),
        },
    ]
);