📘 Part 1: The Problem with Traditional Spring Batch Scaling
Scaling Spring Batch across multiple nodes typically involves complex setup — often requiring messaging middleware like RabbitMQ or Apache Kafka to enable remote partitioning. While effective, this approach introduces tight infrastructure coupling, deployment overhead, and runtime fragility.
Here’s the crux of the problem:
- You need message brokers to send partition instructions to remote workers.
- The master has no real visibility into how many workers are available at job launch.
- Late-arriving or unavailable nodes may lead to skewed partitioning, failures, or idle workers.
- The coordination state lives in memory or is dispersed across the cluster.
For modern, container-based workloads, this makes orchestration harder — especially when trying to run Spring Batch inside Kubernetes, CI/CD workflows, or ephemeral cloud environments.
🔧 Why I Built This Project
To simplify this, I built an open-source framework:
➡️ spring-batch-db-cluster-partitioning
This framework replaces message brokers with a relational database as the central coordination hub. It supports:
- ✅ Round-robin and fixed-node partition assignment
- ✅ Dynamic node discovery before the job starts
- ✅ Fully stateless master logic, with all orchestration handled via SQL
It’s lightweight, easy to plug into your Spring Batch step, and ready to run in Docker, Kubernetes, or CI pipelines.
🧠 Design Note: Master Node Uptime
While the coordination model is stateless and database-driven, the node that initiates a job acts as its master for the duration of execution. This node:
- Launches the job and assigns partitions
- Monitors worker progress and failures
- Executes final aggregation or post-partition steps, if any
- Writes final job completion status
To preserve job integrity, the master node must remain available while the job is running. However, since coordination state is fully persisted in the database, this node can be any eligible participant — making the model decentralized, resilient, and cloud-native in spirit.
📚 What You’ll Learn in This Series
In the coming parts of this series, I’ll walk you through:
- The architecture and coordination flow
- Partitioning strategies (round-robin, fixed-node)
- Failure handling and node resilience
- How to build and run distributed jobs with this framework
- A real-world ETL use case (CSV to XML conversion) to demonstrate end-to-end job orchestration
If you’ve ever struggled with Spring Batch scaling or want a more DevOps-friendly model, this series is for you.
🔜 Coming Up Next:
Part 2 – How Database-Backed Partitioning Works in Spring Batch
Stay tuned — and ⭐️ the repo if you're excited:
👉 github.com/jchejarla/spring-batch-db-cluster-partitioning
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