Distributed Scheduler Service
A reliable, high-throughput task orchestration engine. Built for distributed cron jobs, delayed executions, and resilient work-flow scheduling at scale.
System Overview
The Scheduler service coordinates delayed and recurring work across tenant-aware queues. The target delivery model is at-least-once, so handlers must be idempotent and the accepted concurrency, delay, retry, and recovery profile must be validated for the deployment.
Distributed Cron
Standard cron expression support with high-availability coordination.
Delayed Tasks
Schedule work for a specific UTC timestamp or relative delay.
Resilient Retries
Configurable exponential backoff and dead-letter queues.
Worker Architecture
The service uses a leader-election model for the scheduler loop and a pull-based worker model for task execution, ensuring that no job is lost even if individual workers crash.
Scheduling Jobs
Interact with the scheduler via ConnectRPC. Jobs are identified by a unique key to prevent accidental duplicate scheduling.
const response = await schedulerClient.createJob({
job: {
key: "onboarding.reminder:user-123",
type: "HTTP_CALLBACK",
schedule: {
oneOff: {
executeAt: { seconds: Date.now() / 1000 + 3600 } // 1 hour from now
}
},
callbackConfig: {
url: "https://api.myapp.com/webhooks/onboarding",
method: "POST"
},
payload: {
userId: "user-123",
template: "welcome_reminder"
}
}
});CLI Operations
Use the lr-ctl tool to inspect and trigger jobs manually during development.
Observability & Monitoring
The Scheduler service emits real-time metrics for job performance, allowing you to track execution patterns and latency.
Execution Drift
Track the delta between the scheduled time and the actual start time to detect worker saturation.
Error Rates
Automated alerting on elevated task failure rates or exhausted retry attempts.
Enterprise Grade Distributed Cron
Managing cron at scale is difficult because of double-execution and missed-window failure modes. The Launch Rail Scheduler uses distributed locking, recorded execution state, retries, and failure signals so teams can detect, diagnose, and recover from unsuccessful runs. Application-level idempotency and alert routing remain part of the deployment design.
Review Scheduler pilot statusCommon Questions & Answers
Everything you need to know about integrating and hosting this Launch Rail service.