v1.2.1.1-RC.1

Release Version: v1.2.1.1-RC.1

Release Type: Release Candidate (RC)

Release Date: 1st June, 2026

Overview

This Release Candidate (RC) introduces performance and stability enhancements to the Registration Processor ecosystem with a focus on improving packet processing throughput, optimizing resource utilization, and reducing latency across the processing pipeline.

The release includes code-level fine-tuning, infrastructure optimizations, and processing-stage improvements to improve scalability and operational stability under higher workloads.

As part of the performance validation carried out for this release, the system is now able to process approximately 3000 – 4000 packets per 10 minutes in controlled performance test environments.

Major Areas of Work

JVM & Resource Optimization

Improved JVM memory and garbage collection tuning to reduce processing delays and improve pod stability under high throughput scenarios.
Enhanced Kubernetes resource calibration to ensure better memory utilization and stable workload execution.

Packet Processing Performance Improvements

Optimized packet validation and classification stages through reduced duplicate service calls and parallel execution of independent operations.
Improved processing efficiency for biometric validation, document validation, and metadata retrieval.
Reduced redundant Packet Manager and database calls across multiple stages to improve overall latency.

Database & Reprocessing Optimization

Enhanced database performance for packet reprocessing through indexing improvements, reducing query execution time and improving retry handling efficiency.

Workflow & ABIS Throughput Improvements

Improved ABIS middleware processing by offloading heavy response handling to worker threads.
Optimized workflow internal actions through parallel execution of independent service operations.

Operational Stability Improvements

Introduced asynchronous logging and worker pool fine-tuning capabilities to improve throughput and reduce processing overhead during peak load.
Improved worker pool handling to allow better concurrency management across processing stages.
Multiple code-level refinements to improve reliability and resilience during large-scale processing.