scalo

There's plenty of sage advice about running services in production at scale -- config cascades, structured logging, secret masking, Prometheus, OpenTelemetry, health probes, backpressure, graceful shutdown -- but almost none of it as code you can just install and use.

This is that code.

scalo is an integrated, self-regulating runtime for hyperscale-grade data-plane services. Config, logging and metrics come as one pre-wired trinity -- global singletons you just use, no plumbing, no init dance. Everything else leans on that same integration: the config cascade flows straight into the CLI so run/version/config-check just work; the metrics and health wiring feed the K8s probe trinity; and the deployment contract generates your Helm, Dockerfile and Argo manifests from the config the app already declares.

Attach scalo to your service and a whole class of production pain -- the kind done wrong a hundred times elsewhere -- just goes away. Battle-tested, and almost no code on your side to do it properly. It's not a bag of utility functions you wire up yourself; it's the wiring, done right, for free.

scalo comes in two halves that share one set of conventions, idiomatic in each language. scalo-rs (this crate) is the data plane -- the Rust hot path where every microsecond and byte counts (cargo add scalo). scalo-py is the control plane -- orchestration, APIs and integration in Python (pip install scalo).

Opinionated about correctness -- backpressure, memory safety and the probe trinity are on by default. Unopinionated about your domain -- no web framework, no ORM, no enforced transport. Built as the foundation for PB/hr data services.

This module exists because of this -- https://www.youtube.com/watch?v=xE9W9Ghe4Jk -- but for the backend. And of course, no microservices.

Quick Start

[dependencies]
scalo = "2"

Default features: config, logger. Add the others you want explicitly.

use scalo::{config, logger, env};

fn main() -> anyhow::Result<()> {
    let environment = env::Environment::detect();
    logger::setup_default()?;
    config::setup(config::ConfigOptions {
        env_prefix: "MYAPP".into(),
        ..Default::default()
    })?;

    tracing::info!("Running in {environment:?}");
    Ok(())
}

Features

Pick the slice you need; pay only for what you use.

Native System Dependencies

This crate dynamically links against system C libraries for several features. Both build hosts and deployment targets need the appropriate packages.

Build Host (CI / Development)

For librdkafka-dev >= 2.12.1, add the Confluent APT repo:

curl -fsSL https://packages.confluent.io/clients/deb/archive.key \
  | sudo gpg --dearmor -o /usr/share/keyrings/confluent-clients.gpg
echo "deb [signed-by=/usr/share/keyrings/confluent-clients.gpg] \
  https://packages.confluent.io/clients/deb noble main" \
  | sudo tee /etc/apt/sources.list.d/confluent-clients.list
sudo apt-get update
sudo apt-get install -y librdkafka-dev libssl-dev libsasl2-dev pkg-config

Deployment Host (Runtime)

The compiled binary links against .so files at runtime. Install the runtime packages (not -dev) on deployment hosts or in Docker images.

Only install what you use. Check the features your binary enables to determine which runtime packages are needed.

Docker Example

# Build stage
FROM rust:1 AS builder
RUN apt-get update && apt-get install -y \
    pkg-config libssl-dev librdkafka-dev libgit2-dev libzstd-dev
COPY . .
RUN cargo build --release

# Runtime stage
FROM ubuntu:24.04
RUN apt-get update && apt-get install -y --no-install-recommends \
    librdkafka1 libssl3 libgit2-1.7 libzstd1 ca-certificates \
    && rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/target/release/myapp /usr/local/bin/

For librdkafka1, add the Confluent APT repo to both build and runtime stages.

Health Check Endpoints - The Probe Trinity

For services deployed to Kubernetes, the http-server feature provides the three K8s probe types:

Liveness MUST NEVER check downstream dependencies (a DB outage shouldn't restart your replicas). Readiness checks dependencies AND requires an explicit set_ready() call - cleared during graceful shutdown.

Self-regulation (default vertical scaling)

A scalo data-plane app regulates its own intake. Sized for steady state, a pod slows down or speeds up WITHIN itself first -- the default, fast, local response to a burst, a stalled upstream, or a transform that balloons memory. Only when that vertical headroom is exhausted does it escalate to horizontal scale (KEDA adding pods), driven by the same pressure signal. Memory is the hard, never-OOM authority; CPU is left to the kernel scheduler (CFS), which the byte-budget loop reads through longer process times. It is ON by default and opt-out via self_regulation.enabled = false. See docs/SELF-REGULATION.md.

Architecture

See docs/ for the full documentation index - docs/ARCHITECTURE.md for the module map and layering, and docs/core-pillars/CONFIG.md for the 7-layer config cascade reference.

License

Apache-2.0.

Related

• scalo-py -- sister library for Python services. Same opinions, same patterns, expressive Python ergonomics for control planes, APIs, and integration layers.