Distributed Orchestration Platform

A production-grade job orchestration engine with integrated log aggregation and real-time monitoring. Built with Java 17 and Spring Boot, designed to distribute and manage thousands of background jobs across multiple workers while providing centralized logging and system metrics.

Overview

The Distributed Orchestration Platform is a production-grade job orchestration engine with integrated log aggregation and real-time monitoring. Built with Java 17 and Spring Boot, it is designed to distribute and manage thousands of background jobs across multiple workers while providing centralized logging and system metrics. The platform combines three core capabilities: a distributed task scheduler, a log aggregation system, and a monitoring/observability suite. This allows organizations to reliably schedule tasks at scale (10,000+ jobs per minute) with full visibility into execution and failures.

Scalable,

reliable,

observable

Key Features

• Distributed Job Scheduling: A centralized Orchestrator Service accepts job submissions via REST API and distributes them to multiple worker nodes for execution. It supports load-balanced execution, automatic retries with exponential backoff on failures, and distributed locking via Redis to avoid duplicate processing.• Scalable Worker Pool: Multiple Worker Service instances run in parallel to execute jobs, enabling horizontal scaling of throughput. The system can handle 10,000+ jobs per minute, demonstrating high throughput capacity.• Centralized Log Aggregation: A Log Aggregator Service collects logs from all services and workers in real-time. Logs are stored in a time-series optimized database (ClickHouse) and made queryable via a Query Service that supports full-text search with sub-second latency. This means all job output and system logs can be searched and monitored centrally.• Real-Time Monitoring: Integration with Prometheus (for metrics collection) and Grafana (for dashboards) provides real-time insight into system performance. Key metrics (jobs processed, execution duration, failure counts, Kafka queue lag, etc.) are tracked and visualized on pre-built Grafana dashboards (e.g., job throughput, error rates, worker performance). Alerts can be set on these metrics for proactive issue detection.• Robust Persistence and State Management: All job definitions and states are stored in PostgreSQL (ensuring ACID-compliant transactions). The platform defines a clear job lifecycle state machine: jobs move from PENDING to QUEUED (in Kafka), to RUNNING (on a worker), then to COMPLETED or FAILED, with automatic retries and a dead-letter state for permanently failed jobs. This guarantees reliable processing and the ability to inspect failed tasks.• Security and Access Control: The system is secured with JWT-based authentication for client API requests and role-based access control (RBAC) for multi-tenant or admin/user distinctions. Passwords and secrets are encrypted or provided via environment variables, following production security best practices. An API Gateway (Spring Cloud Gateway) sits at the front to handle routing, rate limiting, and authentication enforcement.

Architecture & Components

The platform follows a microservices architecture with each major function in a separate Spring Boot service:

Orchestrator Service (port 8080)

Handles job submissions, scheduling, and coordination of workers. It receives jobs via REST API, assigns IDs and initial PENDING status, then dispatches them to Kafka for workers to consume.

Worker Service (multiple instances, e.g. ports 8081, 8082, 8083)

Executes the actual job logic. Workers subscribe to job messages from Kafka (job queue) and report status/results back, updating PostgreSQL via the Orchestrator.

Log Aggregator Service (port 8084)

Ingests logs from all other services and workers (e.g., via Kafka topics or direct endpoints). It stores logs in ClickHouse for efficient querying.

Query Service (port 8085)

Provides an API to search and filter logs/metrics data from ClickHouse. For example, it allows querying all logs related to a particular job ID or error across the system. This service also exposes metrics data (from Prometheus) via APIs for external consumption if needed.

API Gateway (port 8086)

Routes external API calls to the appropriate internal service, and handles cross-cutting concerns like authentication and rate limiting. Clients only communicate with the gateway, which then forwards requests to Orchestrator or Query services as appropriate.

Infrastructure Components

The system relies on several supporting technologies– Apache Kafka for message queuing (jobs are published to Kafka topics, which workers subscribe to; logs may also stream through Kafka). PostgreSQL 15 as a relational database for persisting jobs, their statuses, and metadata (ensuring consistency and durability for the job state machine). ClickHouse for log storage and retrieval, chosen for its high-performance handling of time-series and analytics queries. Redis 7 for distributed locking and caching, used to coordinate between services (e.g., to prevent duplicate job execution or manage leader election). Prometheus & Grafana for monitoring - Prometheus collects metrics from all services and infrastructure, and Grafana provides visualization dashboards.

Microservices

architecture

scale

Deployment & Scalability

All services and dependencies can be deployed via Docker Compose for easy setup in development. In production, these would be managed on a cluster (Kubernetes or VMs), with each service scaled as needed. The project's Maven multi-module structure makes it straightforward to build and package each component independently.

High Throughput Capacity

The system is designed to handle 10,000+ jobs per minute, demonstrating production-grade scalability. This is achieved through horizontal scaling of worker instances and efficient message queuing via Kafka.

Reliable State Management

With PostgreSQL ensuring ACID-compliant transactions and a well-defined job lifecycle state machine, the platform guarantees reliable processing even under high load. Failed jobs are automatically retried with exponential backoff, and permanently failed jobs are moved to a dead-letter state for inspection.

Full Observability

Every aspect of the system is observable through centralized logging (ClickHouse), metrics collection (Prometheus), and visualization dashboards (Grafana). This enables proactive issue detection and comprehensive system monitoring.

Production-ready

distributed

systems

The Distributed Orchestration Platform represents a comprehensive solution for managing distributed workloads at scale. By combining job orchestration, log aggregation, and real-time monitoring in a unified microservices architecture, it provides organizations with the tools needed to reliably execute thousands of background jobs while maintaining full visibility into system performance. The platform's emphasis on reliability (through robust state management and retry mechanisms), scalability (through horizontal worker scaling), and observability (through integrated logging and monitoring) makes it suitable for production environments requiring high throughput and operational excellence.

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