Batch Processing Architecture

1. Introduction

Batch processing architecture is a design pattern that processes large volumes of data by grouping tasks into batches. This approach is particularly effective for tasks that do not require real-time processing and can improve system performance and resource utilization.

2. Key Concepts

• **Batch**: A collection of data processed together at a specific time.
• **Throughput**: The amount of data processed in a given timeframe.
• **Latency**: The delay before data processing begins.
• **Job Scheduler**: A tool that manages the execution of batch jobs.

3. Architecture Overview

The typical batch processing architecture consists of the following components:

1. Job Scheduler: Initiates batch jobs based on a schedule.
2. Batch Processing Engine: Executes the jobs and handles processing logic.
3. Data Storage: Stores input and output data for batch jobs.
4. Monitoring and Logging: Tracks job status and logs errors.

4. Batch Processing Workflow

        graph TD;
            A[Start] --> B[Job Scheduler];
            B --> C{Job Ready?};
            C -- Yes --> D[Execute Batch Job];
            C -- No --> E[Wait];
            D --> F[Store Output Data];
            F --> G[Log Results];
            G --> H[End];
            E --> B;
        

5. Best Practices

Here are some best practices to ensure effective batch processing:

• **Optimize Data Access**: Minimize database calls and optimize queries.
• **Error Handling**: Implement robust error handling and retry mechanisms.
• **Monitoring**: Continuously monitor job executions and system performance.
• **Scalability**: Design for scalability to handle increasing data volumes.

6. FAQ