GroveStreams is a temporal relational database with built-in analytics, forecasting, and an AI query layer. It reduces the entire temporal data management surface to a single primitive — the stream. Every property on every entity and every relationship between entities is a stream with its own independent time axis, queryable through a single language (GS SQL), with built-in AI forecasting and correlation detection. No history tables. No materialized views. No batch roll-up jobs. One primitive replaces them all.
The entire database is a living memory of everything that ever happened and how things were connected at any point in time. Every data point knows when it happened. Every relationship knows when it changed. Every query can travel through time.
The Deep Cell Model
Visualize GroveStreams as a table where each component (entity) is a row and each stream is a column — but under every cell is the complete versioned history of that value at precise timestamps. Every cell holds not just the current value, but every value it has ever had, each with its own timestamp. A temperature reading every second? Stream. A rate schedule that changes once a year? Stream. A pump-to-tank relationship that changes twice a year? Also a stream. All use identical query semantics.
This is schema-per-instance rather than schema-per-table. Each entity carries its own temporal structure. No rigid table schemas forcing every row into the same shape. No NULL-filled columns for data that simply doesn't apply.
One Primitive Replaces Five Infrastructure Concerns
A skilled DBA can build temporal history tracking, relationship history, roll-up aggregations, and cross-entity correlation in PostgreSQL or any traditional database. But they must design the history tables, write the triggers, build the materialized views, maintain the batch jobs, and do it all from scratch — for every project, every time. GroveStreams makes all of that automatic and native.
Streams subsume what traditionally requires:
- Data tables
- History tables with triggers
- Materialized views for roll-ups
- Batch aggregation jobs
- Junction tables for relationships
Temporal Relationships
Relationships between entities are streams. When Pump-A is reconnected from Tank-7 to Tank-12, a new data point is appended to the stream. The old connection stays in the history. Any stream can store a UID pointing to another component or stream, enabling temporal relationship tracking today. This is the key architectural differentiator vs. every other platform — relationships have full temporal history, not just a current-state pointer.
Independent Time Axes
Each stream has its own time axis. No shared clock forcing NULL-filling or interpolation. An energy meter reading every fifteen minutes and a contract status that changes quarterly coexist naturally. Sparse data is native — not a special case. When correlating streams with different time axes, roll-ups project both onto a common temporal grid — the same roll-up infrastructure that powers analytics also solves cross-stream temporal correlation.
The Core AI Data Foundation
Standard SQL is hard for LLMs to get right with time-series. Handling time zones, gaps, interpolation, and temporal joins leads to hallucinated syntax and broken queries. GroveStreams solves this at the platform level. GS SQL provides time-series extensions — rollups, time filters, gap filling — sitting on top of a massive-scale store. AI agents get complete temporal context natively, not through fragile prompt engineering.
Ask your data anything. Get answers in seconds, not sprints. An AI agent using GS SQL can answer questions like:
- "What was the average energy cost per meter during peak hours last quarter, and how does it compare to the same period last year?"
- "Which pumps were connected to Tank-12 when the pressure anomaly occurred on March 15th?"
- "Show me every equipment relationship change that happened within 24 hours of a maintenance event across all facilities."
- "What is the predicted demand for the next 72 hours based on the last 90 days of consumption data?"
- "Which streams are most correlated with the temperature anomalies we saw last week?"
Streaming Intelligence Engine
Static reports tell you what happened. GroveStreams tells you what's happening — and what's about to happen. Every capability in the platform maps directly to the needs of intelligent agents:
| Platform Capability | For AI Agents | What It Means |
|---|---|---|
| Ingestion (100M+ pts/stream) | Infinite Context Window | Agents can reason over years of history, not just the last few hours |
| Rollups & Aggregation | Agent Memory | Raw noise summarized into meaningful trends automatically |
| Derived Streams & Formulas | Reasoning Logic | Pre-processed data so the AI doesn't have to compute it on every query |
| HTTP/MQTT Actions | Agent Tools | AI can act on conclusions — reboot a server, adjust a setpoint, trigger a workflow |
| Forecasting (TFT, Prophet, N-BEATS...) | Predictive Planning | Agents get foresight, not just hindsight |
| GS SQL | Native Query Language | Purpose-built for temporal queries — no hallucinated SQL syntax |
How GroveStreams Compares
| Capability | Multi-System Alternative | GroveStreams |
|---|---|---|
| Properties | Static values or separate TS sync | Every property is a temporal stream |
| Relationships | Static pointers (no history) | Streams with full temporal history |
| Time-Series Storage | External system (separate DB) | Core architecture (100M+ pts/stream) |
| Cross-Stream Correlation | History tables, triggers, batch jobs | Roll-ups align time axes; equi-join on entity ID |
| Query Language | Multiple (SQL + Graph + TS API) | Unified GS SQL |
| AI Forecasting | External ML services | Built-in (8 model types per stream) |
| Real-Time Roll-ups | Manual (Spark/Flink jobs) | Automatic, configurable hierarchies |
| Derived Streams | Custom pipeline code | Excel-style formulas, real-time |
| Schema Evolution | Table locks / pipeline rebuilds | Live reconciliation, zero downtime |
| Third-Party Ecosystem | Hundreds of connectors (Kafka, Spark, dbt, etc.) | Focused set (MQTT, HTTP, OData, RSS) |
