# Nuclio — High-Performance Serverless Framework for Real-Time Data

> Nuclio is a serverless framework optimized for real-time and data-intensive workloads, delivering sub-millisecond cold starts and high throughput on Kubernetes, Docker, or bare metal.

## Install

Save as a script file and run:

# Nuclio — High-Performance Serverless Framework for Real-Time Data

## Quick Use
```bash
# Install the CLI
# Deploy a function from a YAML spec
nuctl deploy my-func 
  --path ./handler.py 
  --runtime python 
  --handler handler:handler 
  --platform local

# Invoke it
nuctl invoke my-func --body '{"key": "value"}'
```

## Introduction
Nuclio is built for workloads where latency and throughput matter — event processing, real-time inference, IoT data pipelines. Unlike general-purpose FaaS platforms, it keeps function instances warm and processes events in parallel threads within a single container, eliminating per-invocation cold starts.

## What Nuclio Does
- Deploys serverless functions on Kubernetes, Docker Swarm, or bare metal
- Processes events from HTTP, Kafka, Kinesis, MQTT, RabbitMQ, Cron, and more
- Maintains warm function instances with multi-worker concurrency inside each container
- Provides a web-based dashboard for deploying, testing, and monitoring functions
- Supports Python, Go, Java, Node.js, .NET, and shell runtimes

## Architecture Overview
Each Nuclio function runs in a container with an embedded event processor. The processor listens on configured triggers, decodes events, and dispatches them to worker threads running user code. A controller (on Kubernetes, a CRD operator) manages function lifecycle, scaling, and ingress. This in-process model avoids the overhead of spinning up new containers per event, achieving throughput rates of hundreds of thousands of events per second.

## Self-Hosting & Configuration
- Deploy on Kubernetes with Helm charts or on Docker with a single docker run command
- Configure triggers, resources, and scaling via YAML function specs or the dashboard
- Set min/max replicas and target concurrency for autoscaling on Kubernetes
- Mount volumes, secrets, and config maps for function dependencies
- GPU support available for ML inference workloads

## Key Features
- Sub-millisecond warm-start latency with multi-worker parallelism
- Built-in triggers for Kafka, MQTT, Kinesis, RabbitMQ, and V3IO stream
- Integrated web dashboard for code editing, deployment, and log viewing
- GPU-aware scheduling for real-time ML serving functions
- Versioned function deployments with canary and blue-green support

## Comparison with Similar Tools
- **AWS Lambda** — managed FaaS with broader integrations; Nuclio is self-hosted and optimized for low-latency data processing
- **OpenFaaS** — Kubernetes FaaS with simpler architecture; Nuclio offers tighter event-source integrations and higher throughput
- **Knative Serving** — scales Kubernetes pods per request; Nuclio's in-process workers avoid per-request container overhead
- **Fission** — fast Kubernetes FaaS with environment pooling; Nuclio adds a dashboard and native streaming triggers

## FAQ
**Q: How does Nuclio achieve low latency?**
A: Functions run as persistent processes with multiple worker threads. Events are dispatched in-process, avoiding container startup overhead.

**Q: Can Nuclio run outside Kubernetes?**
A: Yes. The local Docker platform lets you run Nuclio functions without a Kubernetes cluster.

**Q: Does it support GPU workloads?**
A: Yes. Function specs can request GPU resources, and the scheduler places them on GPU-enabled nodes.

**Q: Who maintains Nuclio?**
A: Nuclio is developed by Iguazio (now part of McKinsey) and the open-source community under the Apache 2.0 license.

## Sources
- https://github.com/nuclio/nuclio
- https://nuclio.io/docs/latest/

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Source: https://tokrepo.com/en/workflows/asset-451ff9c1
Author: Script Depot