Company Overview

A leading global smart-hardware manufacturer operates multi-category production lines (e.g., smartphones, smart-home devices). Each day, the plants generate massive volumes of equipment telemetry, production logs, and business-system data. To achieve end-to-end traceability, real-time monitoring of core KPIs, and faster decisions, the company standardized on YMatrix HTAP as its data backbone.

Challenges

• Data scale & timeliness – 180+ device types; daily new data ~130 GB. The platform must sustain rapid writes to keep FPY (first-pass yield/良品率), OEE, and UPH continuously up to date.

• Lagging alerting – Threshold-only alarms trigger after degradation; by the time limits are crossed, yield has already dropped.

• Hard-to-analyze data – Large volumes of unstructured and time-series data strain traditional databases, slowing process optimization and KPI computation.

• Diverse scenarios – Power a central command screen for live KPIs and an in-house BI system for dashboards and labor statistics.

• Legacy limits – Prior Hadoop-ecosystem + multi-DB stack (e.g., Greenplum, Oracle) struggled with real-time compute; complex reports were slow.

Why YMatrix HTAP

YMatrix delivers a hyper-converged architecture that unifies transactions and analytics on one engine, simplifying the stack while meeting strict latency targets.

• High-throughput ingestion with MatrixGate for rapid intake from large device fleets.

• Real-time hybrid processing (HTAP) to write time-series, structured, and unstructured data in real time and query in seconds.

• In-database streaming (Domino) for dynamic KPI computation (e.g., FPY) to surface anomalies instantly and reduce scrap.

• Developer/Ops efficiency via PL/Python, decoupling algorithm work from DB operations and easing maintenance at scale.

Architecture at a Glance

• Unified OLTP + OLAP on YMatrix HTAP—no dual systems, no cross-system drift.

• Streaming pipelines in-DB—transforms and aggregations happen where data lives.

• Elastic scale-out—add nodes to handle bursts in ingest or analytics without re-architecture.

• Consistent data model—a single source of truth for shop-floor metrics and management dashboards.

Outcomes

Higher production efficiency

• Sustained ingest 50,000+ rows/second, supporting hundreds of thousands of devices.

• Peak QPS up to 500,000 with P95 latency < 2.5 s, ensuring stable line operations.

• By end-2024: 12 TB+ total data; largest single table 1.8 TB+—a robust foundation for plant-wide analytics.

Lower production costs

• One platform for multimodal data cuts architectural complexity and O&M effort.

• Faster KPI queries for FPY/OEE/UPH and real-time alerting improve yield and reduce scrap and rework.

Broader business support

• Central command screen displays live core KPIs for management visibility.

• In-house BI covers labor management and operational dashboards for continuous improvement.

• In-database ML enables predictive maintenance and faster process iteration—sustaining competitive advantage.

What Made the Difference

• HTAP on one stack → real-time KPIs without maintaining parallel OLTP/OLAP systems.

• Domino streaming → second-level metric updates and early anomaly detection.

• MatrixGate ingestion → smooth intake from heterogeneous devices and IoT platforms.

• Operational simplicity → fewer moving parts, clearer ownership, faster change cycles.

Conclusion

This anonymized deployment shows how YMatrix HTAP resolves smart-factory pain points—multi-source data, complex analytics, and strict latency—while leaving room to scale. The same platform supports today’s needs and tomorrow’s roadmap (e.g., hot/cold tiering, digital twins). For leading manufacturers, YMatrix is becoming the standard data nerve center of the modern factory.