#batch

We decompose Spring Boot deployment from an operational architecture perspective rather than comparing features, and provide criteria for which deployment model to choose.

We present practical tuning standards that stably optimize batch throughput within system limits rather than blindly increasing it.

We organize failure classification, DLQ, retry, and idempotency patterns to design deployment failures into a recoverable state rather than avoidable ones.

We present a final decision matrix and reference architecture that combines @Scheduled, Quartz, Spring Batch, and manual deployment to suit actual operating conditions.

The redundant execution, distributed locks, and failure recovery trade-offs that occur when applying @Scheduled to the operating environment are organized based on architecture.

This course covers JobStore, Misfire, and cluster design standards for using Quartz as an operational control system rather than a simple scheduler.

We dissect Spring Batch's Reader/Processor/Writer structure from an operational perspective and present standards for designing a restartable batch.

Spring Batch parallel processing strategies are compared in terms of throughput, order guarantee, and operational complexity, and selection criteria are presented.

We summarize API design, permission control, audit trail, reprocessing, and rollback strategies to safely operate manual deployment.

We present paging/index/isolation level design standards to simultaneously secure DB query performance and consistency in mass deployment.

We summarize the OpenSearch deployment design criteria and operational trade-offs considering both bulk querying and mass indexing from a practical perspective.

We present criteria for selecting an execution entity/lock strategy to prevent duplicate execution of batches in multi-instances and control operational complexity.

Design that maintains quality and reduces inference costs by separating scheduling and model selection criteria