TSDB
Time-Series Data Backbone
Preserve high-frequency equipment data, quality and temporal context for historical queries, store-and-forward and last-value recovery.
The problem
A real-time screen only shows the present. Network interruptions, bursts and unclear retention policies can undermine traceability, analytics and audit evidence.
The outcome
Build a queryable, retainable and recoverable time-series foundation at the edge.
Operational context
Return every historical value to its operational context.
TSDB preserves value, time, quality, tags and provenance so trends, anomaly analysis and incident review share a trustworthy foundation.
- 01TDengine storage abstractionProvide in-memory and TDengine providers so development, validation and production storage can change by environment.
- 02Store and forwardQueue data through temporary interruptions and restore history in sequence when connectivity returns.
- 03History and last valueSupport time-range queries, latest-value retrieval and state recovery after service restarts.
Core capabilities
From product module to reusable operational capability.
The codebase contains storage providers and a time-series service foundation. Production capacity, retention, redundancy and stress testing are completed per deployment.
TDengine storage abstraction
Provide in-memory and TDengine providers so development, validation and production storage can change by environment.
Store and forward
Queue data through temporary interruptions and restore history in sequence when connectivity returns.
History and last value
Support time-range queries, latest-value retrieval and state recovery after service restarts.
Retention and capacity governance
Set retention by data importance, sampling frequency and regulatory need instead of accumulating data without limits.
Data and responsibility flow
Every step stays visible, verifiable and governable.
- 01Receive tags with timestamps and quality
- 02Write batches to time-series storage
- 03Queue during interruptions and monitor pressure
- 04Serve trends, analytics, DTZ and HFS queries
Use cases
Start with one high-value loop.
- Equipment trends and anomaly review
- Energy baselines and peak analysis
- Quality-event traceability
- Data preservation during edge disconnection
Responsibility boundary
High availability and zero data loss are not single switches. Disk capacity, queue limits, replicas, failure domains and recovery objectives must be validated together.
Current technical baseline: The codebase contains storage providers and a time-series service foundation. Production capacity, retention, redundancy and stress testing are completed per deployment.