oxidizer-lite 0.1.5

A distributed, event-driven DAG orchestration engine built on Redis Streams

Oxidizer-Lite

A distributed, event-driven DAG orchestration engine built on Redis Streams. Oxidizer-Lite coordinates data pipelines using a controller-worker pattern — configs define the topology, Redis handles dispatch, and workers scale horizontally.

Installation

pip install oxidizer-lite

Or install from source:

git clone https://github.com/moos-engineering/oxidizer-lite.git
cd oxidizer-lite
pip install -e .

Quick Start

1. Start infrastructure:

# Redis (with JSON module)
docker run -d --name redis -p 6379:6379 redis/redis-stack:latest

# MinIO (S3-compatible storage)
docker run -d --name minio -p 9000:9000 -p 9001:9001 \
  -e MINIO_ROOT_USER=minioadmin -e MINIO_ROOT_PASSWORD=minioadmin \
  minio/minio server /data --console-address ":9001"

2. Create a worker:

from oxidizer_lite import Reagent, CatalystConnection

catalyst = CatalystConnection(host="localhost", port=6379, db=0)
reagent = Reagent(catalyst)

@reagent.react()
def process(data: dict, context: dict):
    # Your processing logic here
    return data.get("input", [])

3. Run the controller:

from oxidizer_lite import Oxidizer, CatalystConnection, CrucibleConnection

catalyst = CatalystConnection(host="localhost", port=6379, db=0)
crucible = CrucibleConnection(
    s3_bucket="oxidizer-configs",
    s3_url="http://localhost:9000",
    access_key="minioadmin",
    secret_key="minioadmin"
)

oxidizer = Oxidizer(catalyst, crucible)
oxidizer.oxidize()

4. Start the API server:

from oxidizer_lite import Microscope, Catalyst, Crucible, CatalystConnection, CrucibleConnection

crucible = Crucible(CrucibleConnection(...))
catalyst = Catalyst(CatalystConnection(...))
microscope = Microscope(crucible=crucible, catalyst=catalyst)
microscope.run(host="0.0.0.0", port=8000)

Architecture

YAML Config (S3)
      │
      ▼
┌──────────────┐   Redis Streams    ┌─────────────┐
│  Controller  │◄──────────────────►│  Worker(s)  │
│  (Oxidizer)  │   task dispatch /  │  (Reagent)  │
└──────┬───────┘   checkpoints      └─────┬───────┘
       │                                  │
       ▼                                  ▼
┌──────────────┐                    ┌─────────────┐
│    Redis     │                    │ SQL / API / │
│  State Cache │                    │  Streams    │
└──────────────┘                    └─────────────┘

Controller reads invocations, compiles the DAG, dispatches ready nodes, and processes worker updates. Workers fetch inputs, execute user functions, write outputs, and report checkpoints — all through Redis Streams.

Project Structure

oxidizer-lite/
├── oxidizer_lite/          # Core package
│   ├── oxidizer.py         # DAG controller
│   ├── reagent.py          # Worker decorator
│   ├── catalyst.py         # Redis client
│   ├── anvil.py            # SQL/API engines
│   ├── crucible.py         # S3 storage
│   ├── microscope.py       # REST API + MCP
│   ├── lattice.py          # Config parser
│   ├── topology.py         # DAG compiler
│   ├── phase.py            # Data models
│   ├── incubation.py       # Scheduler
│   └── residue.py          # Logging
├── examples/
│   ├── skeleton/           # Minimal starter
│   └── scd2/               # SCD Type 2 pipeline
├── tests/
├── pyproject.toml
└── LICENSE

Components

Component	Module	Role
Oxidizer	oxidizer.py	Core orchestrator — topology lifecycle, node dispatch, checkpoint processing
Reagent	reagent.py	Distributed worker — input fetching, user function execution, output handling
Catalyst	catalyst.py	Redis cache and streams — consumer groups, JSON state, TTL management
Anvil	anvil.py	SQL engine (DuckDB, Iceberg) and REST API engine for data I/O
Crucible	crucible.py	S3-compatible object storage — configs, artifacts, multi-auth (IAM, SSO, keys)
Lattice	lattice.py	YAML config parsing, validation, and S3 persistence
Topology	topology.py	DAG compiler — multi-layer nodes, edges, dependency resolution
Phase	phase.py	Dataclass models — task messages, I/O methods, checkpoints, errors
Microscope	microscope.py	REST API + MCP server — invoke topologies, query logs, manage configs
Incubation	incubation.py	Cron-based scheduler for scheduled nodes
Residue	residue.py	Structured logging with Redis persistence

Features

I/O Methods

Method	Input	Output
Streams	Redis streams with batching, windowing, consumer groups	Write records to downstream streams
SQL	SELECT queries with pagination, SCD Type 2 support	INSERT, MERGE, UPDATE with auto-schema creation
API	HTTP GET with auth (bearer token, API key)	HTTP POST with JSON payload

DAG Orchestration

• YAML-driven topology — multi-layer DAG with dependency resolution
• 15 node lifecycle states — PENDING, READY, DISPATCHED, RUNNING, LIVE, PAUSED, SUCCESS, FAILED, RETRYING, SKIPPED, etc.
• Control signals — pause, resume, and shutdown topologies during execution
• Live streaming nodes — run indefinitely until explicitly stopped

Batch Processing

• Stateful checkpoints — cursor tracking, batch index, accumulated runtimes and memory per stage
• Resumable execution — workers resume from the last checkpoint on failure or restart
• Auto-pagination — SQL and stream inputs paginate automatically based on batch size

Monitoring & Control

• REST API — health checks, topology invocation, log queries
• MCP Server (FastMCP) — AI-friendly tools for topology management
• Structured logging — per-component logs persisted to Redis with TTL
• Performance profiling — runtime and memory tracking per processing stage

Storage

• Redis — topology state, stream coordination, JSON cache, log persistence
• S3-compatible — configs and artifacts via MinIO or AWS S3
• Iceberg — via AWS Glue Data Catalog with DuckDB

Examples

See the examples/ folder for complete working pipelines:

• skeleton/ — Minimal starter template (API → Streams → SQL)
• scd2/ — SCD Type 2 pipeline with Open-Meteo weather API (18 US cities)

API Usage

Invoke topologies via the MCP client:

import asyncio
from fastmcp import Client

async def main():
    async with Client("http://localhost:8000/mcp") as client:
        # List available operations
        tools = await client.list_tools()
        
        # Invoke a topology
        result = await client.call_tool("invoke_topology", {"lattice_id": "scd2"})
        print(result)

asyncio.run(main())

TODO:

[ ] Update Controller to Not Exit On Redis Removal
[ ] Add Pause / Resume Functionality
[ ] Scheduling - This will come with Rust Integration
[ ] Node Retry Logic (How to Test) [ ] Data Retreival Fallback Updates
[ ] Update DuckLake to Use Catalog (Remove USE ___)
[ ] Improve Memory Metric Capture (Maybe Switch to Rust? Maybe update CheckpointMetadata?) [ ] Add Default Override to Config - If Defaults in Config global, layer, node level - override the in code defaults [ ] Ensure TTL on All Necessary keys / streams / etc.

Ideas:

• Ack Node Logs (Reduce Existing TTL)
• Add Lineage View to Lattice Page in UI

License

MIT License — see LICENSE for details.