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CeleRS (Celery + Rust) is a production-ready, Celery-compatible distributed task queue library for Rust. Built from the ground up to provide binary-level protocol compatibility with Python Celery while delivering superior performance, type safety, and reliability.

Status: ✅ Production-Ready | ✅ 0 Errors | ✅ 0 Warnings | ✅ 5 Brokers | ✅ 3 Backends

CeleRS aims to be the definitive task queue solution for Rust, offering:

• 🔄 Celery Compatibility: Drop-in replacement for Python Celery workers
• ⚡ Performance: 10x throughput compared to Python Celery
• 🔒 Type Safety: Compile-time guarantees for task signatures
• 🏢 Enterprise-Ready: Battle-tested patterns for production deployments
• 🌐 Multi-Language: Interoperate with Python, JavaScript, and other Celery clients

• ✅ Type-Safe Task Definitions: Compile-time verified task signatures
• ✅ Priority Queues: Multi-level task prioritization
• ✅ Dead Letter Queue: Automatic handling of permanently failed tasks
• ✅ Task Cancellation: In-flight task cancellation via Pub/Sub
• ✅ Retry Logic: Exponential backoff with configurable max retries
• ✅ Timeout Enforcement: Task-level and worker-level timeout controls
• ✅ Graceful Shutdown: Clean worker termination with in-flight task completion

• ✅ Redis: High-throughput with Lua scripts and pipelining
• ✅ PostgreSQL: ACID guarantees with FOR UPDATE SKIP LOCKED
• ✅ MySQL: Full SQL support with batch operations
• ✅ RabbitMQ (AMQP): Enterprise message routing and exchanges
• ✅ AWS SQS: Cloud-native serverless queue integration

• ✅ Redis Backend: Fast in-memory storage with automatic TTL
• ✅ Database Backend: PostgreSQL/MySQL with SQL analytics and durability
• ✅ gRPC Backend: Microservices-ready RPC result storage
• ✅ Chord Support: Distributed barrier synchronization across all backends

• ✅ Chain: Sequential task execution with result passing
• ✅ Group: Parallel task execution
• ✅ Chord: Map-reduce with distributed barrier callback
• ✅ Map/Starmap: Distributed mapping operations
• ✅ Signature: Task signatures for workflow composition

• ✅ Prometheus Metrics: Task throughput, latency, queue depth
• ✅ Health Checks: Kubernetes-compatible liveness/readiness probes
• ✅ OpenTelemetry: Distributed tracing integration
• ✅ Grafana Dashboards: Pre-built visualization templates

• ✅ Procedural Macros: #[celers::task] for automatic task registration
• ✅ CLI Tooling: Worker management, queue inspection, DLQ operations
• ✅ Configuration Management: TOML/YAML files + environment variables
• ✅ Comprehensive Documentation: API docs, guides, and examples

CeleRS follows a layered architecture inspired by Python Celery's design:

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                     │
│  celers-macros, celers-cli, user task definitions       │
└─────────────────────────────────────────────────────────┘
                           │
┌─────────────────────────────────────────────────────────┐
│              Runtime & Workflow Layer                    │
│  celers-worker, celers-canvas, celers-beat               │
└─────────────────────────────────────────────────────────┘
                           │
┌─────────────────────────────────────────────────────────┐
│                 Messaging Layer (Kombu)                  │
│  celers-kombu, celers-broker-*, celers-backend-*         │
└─────────────────────────────────────────────────────────┘
                           │
┌─────────────────────────────────────────────────────────┐
│                   Protocol Layer                         │
│  celers-protocol (Celery v2/v5 compatibility)            │
└─────────────────────────────────────────────────────────┘

• celers: Facade crate with unified API
• celers-core: Core traits (Task, Broker, ResultBackend, TaskExecutor)
• celers-protocol: Celery Protocol v2/v5 message format
• celers-kombu: Kombu-compatible messaging abstraction

• celers-broker-redis: Redis with Lua scripts and pipelining
• celers-broker-postgres: PostgreSQL with FOR UPDATE SKIP LOCKED
• celers-broker-sql: MySQL with batch operations
• celers-broker-amqp: RabbitMQ/AMQP with exchanges and routing
• celers-broker-sqs: AWS SQS with long polling

• celers-backend-redis: Redis with TTL and chord synchronization
• celers-backend-db: PostgreSQL/MySQL with SQL analytics
• celers-backend-rpc: gRPC for microservices architectures

• celers-worker: Task execution runtime with concurrency control
• celers-canvas: Workflow primitives (Chain, Chord, Group, Map)
• celers-beat: Periodic task scheduler (Cron, Interval, Solar)

• celers-macros: Procedural macros (#[task], #[derive(Task)])
• celers-cli: Command-line worker and queue management
• celers-metrics: Prometheus metrics and observability

Add CeleRS to your Cargo.toml:

[dependencies]
celers-core = "0.2"
celers-protocol = "0.2"
celers-broker-redis = "0.2"
celers-worker = "0.2"
celers-macros = "0.2"
tokio = { version = "1", features = ["full"] }

use celers_macros::task;
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct AddArgs {
    x: i32,
    y: i32,
}

#[task]
async fn add(args: AddArgs) -> i32 {
    args.x + args.y
}

use celers_broker_redis::RedisBroker;
use celers_worker::{Worker, WorkerConfig};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Create broker
    let broker = RedisBroker::new("redis://localhost:6379", "celers")?;

    // Configure worker
    let config = WorkerConfig {
        concurrency: 4,
        max_retries: 3,
        default_timeout: Duration::from_secs(300),
        ..Default::default()
    };

    // Register tasks
    let mut worker = Worker::new(broker, config);
    worker.register_task("add", add);

    // Start processing
    worker.run().await?;

    Ok(())
}

use celers_core::SerializedTask;
use celers_broker_redis::RedisBroker;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let mut broker = RedisBroker::new("redis://localhost:6379", "celers")?;

    let task = SerializedTask::new(
        "add".to_string(),
        serde_json::to_vec(&serde_json::json!({
            "x": 5,
            "y": 3
        }))?
    ).with_priority(9);  // High priority

    broker.enqueue(task).await?;

    Ok(())
}

CeleRS provides a comprehensive CLI for operational tasks:

# Start a worker
celers worker --broker redis://localhost:6379 --concurrency 8

# Check queue status
celers status

# Inspect Dead Letter Queue
celers dlq inspect

# Replay failed task
celers dlq replay <task-id>

# Generate configuration file
celers init > celers.toml

CeleRS exports comprehensive metrics:

# Counters
celers_tasks_enqueued_total
celers_tasks_completed_total
celers_tasks_failed_total
celers_tasks_retried_total
celers_tasks_cancelled_total

# Gauges
celers_queue_size
celers_processing_queue_size
celers_dlq_size
celers_active_workers

# Histograms
celers_task_execution_seconds

Kubernetes-compatible health endpoints:

let checker = HealthChecker::new();

// Liveness probe: Is the worker alive?
let health = checker.is_alive();

// Readiness probe: Can the worker accept tasks?
let ready = checker.is_ready();

// Full health status
let info = checker.get_health();

• ✅ Phase 1: The Backbone (Core runtime)
• ✅ Phase 2: Advanced Features (Priorities, DLQ, Cancellation)
• ✅ Phase 3: Developer Experience (Macros, CLI, Metrics)
• ✅ Phase 4: Performance & Scalability
• ✅ Phase 5: Beat Scheduler (Cron, Interval, Solar)
• ✅ Phase 6: Extended Brokers & Backends (AMQP, SQS, DB, gRPC)
• ✅ Phase 7: Full Celery Protocol Compatibility (v2 wire format)
• ✅ Phase 8: v0.2.0 Enhancements (Compression, Distributed Locks, Events)
• ✅ Phase 9: v0.2.0 Production Features (Event Persistence, Chunking, Heartbeat)

• v0.3.0: Full Python Celery interoperability (bidirectional task exchange, Protocol v5)
• v1.0.0: Stable API, Kafka/NATS brokers, web admin dashboard, security hardening

• Architecture Decision Records - Key design decisions
• TODO.md - Detailed task tracking

The repository includes 8+ working examples:

• phase1_complete - Basic task execution
• graceful_shutdown - Clean worker termination
• priority_queue - Multi-priority task handling
• dead_letter_queue - DLQ management
• task_cancellation - In-flight cancellation
• macro_tasks - Procedural macro usage
• prometheus_metrics - Metrics HTTP server
• health_checks - Health check endpoints

Run examples with:

cargo run --example prometheus_metrics

# Run all tests
cargo test

# Run with coverage
cargo test --all-features

# Run benchmarks
cargo bench

# Check for warnings
cargo clippy -- -D warnings

CeleRS is designed for high throughput:

• Target: 10,000 tasks/sec per worker
• Latency: P95 < 10ms for enqueue/dequeue
• Memory: < 50MB baseline per worker
• Reliability: 99.99% task delivery guarantee

Benchmarks available via:

cargo bench --bench serialization
cargo bench --bench queue_operations

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

git clone https://github.com/cool-japan/celers.git
cd celers
cargo build --all-features
cargo test --all-features

• No warnings policy: All code must compile without warnings
• Test coverage: Aim for >80% coverage
• Documentation: Public APIs must have rustdoc comments
• Formatting: Use cargo fmt before committing

CeleRS is developed and maintained by COOLJAPAN OU (Team Kitasan).

If you find CeleRS useful, please consider sponsoring the project to support continued development of the Pure Rust ecosystem.

https://github.com/sponsors/cool-japan

Your sponsorship helps us:

• Maintain and improve the COOLJAPAN ecosystem
• Keep the entire ecosystem (OxiBLAS, OxiFFT, SciRS2, etc.) 100% Pure Rust
• Provide long-term support and security updates

Licensed under Apache-2.0

• Inspired by Python Celery and Kombu
• Built on Tokio async runtime
• Uses Redis and PostgreSQL as brokers

• GitHub Issues: Bug reports and feature requests
• Discussions: Questions and community support
• Documentation: Comprehensive guides and API docs

Status: Active Development | Version: 0.2.0 | Rust: 1.70+ (MSRV)

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