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Insight in no time

MonoTS

Schema on the fly

MonoTS focuses on edge-native time-series persistence plus built-in synchronization, so you can keep local control without losing cloud connectivity.

WHY MONOTS

Why MonoTS

"The data flow of insights should be as smooth as transactions."

Instead of assembling separate local databases, connectors, and sync workers, MonoTS keeps ingest, query, and synchronization in a single operational model.

This reduces moving parts at the edge and gives teams a predictable path from local writes to upstream analytics systems.

Key Features

Edge-first Write Path

Keep ingestion stable on constrained edge machines with a lightweight single-process runtime.

Local SQL for Fast Decisions

Query fresh data immediately at the source through gRPC + SQL, without waiting for cloud-side jobs.

Time-aware Storage Layout

Organize data around the mandatory time column and seal Parquet files for efficient pruning.

Built-in Stream Management

Create and control sync pipelines with SQL commands like CREATE STREAM, SHOW STREAMS, and DROP STREAM.

Checkpointed Reliability

Recover automatically after network interruptions with server-side progress tracking and resume.

Pragmatic Schema Evolution

Evolve business columns over time while keeping a strict, performant time-series core model.

MAXIMIZE MONOTS EFFECTIVENESS

Built for Practical Scenarios

Gateway Observability Buffer

Buffer logs and metrics locally at sites, then synchronize upstream for centralized troubleshooting.

Factory and Device Telemetry

Collect high-frequency sensor events near machines and keep cloud pipelines continuously fed.

Unstable-network Environments

Continue ingesting when WAN is flaky, and let streams catch up automatically after reconnection.

FREQUENTLY ASKED QUESTIONS

Frequently asked questions

Is MonoTS only for edge devices?

No. MonoTS is optimized for edge deployment, but it also runs in VM or container environments.

How do clients access MonoTS?

Clients connect through gRPC (CLI and SDK). SQL statements are executed through that API surface.

When does data appear in batch sinks?

For filesystem and Delta-style batch flows, data appears after sealing (auto flush or FLUSH TABLE).

EXPLORE MORE ABOUT MONOTS

Explore more about MonoTS

Start MonoTS in 5 Minutes

Build from source, boot server + CLI, and complete a minimal ingest/query walkthrough.

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Design Reliable Sync Pipelines

Configure Kafka, Delta, and filesystem stream targets with practical checkpoint guidance.

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Review SQL Surface and Types

See supported DDL/DML, required time semantics, and common type compatibility details.

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