Why OpenGemini Is Emerging as the Go-To Open-Source Time-Series Database

OpenGemini Live Session Overview

On July 19, the openGemini community together with Huawei Cloud DTT held a live event titled “OpenGemini Time-Series Database Application Scenarios and Technical Practices”. The presenter covered eight topics: openGemini’s characteristics, application scenarios, open‑source goals and value, competitive advantages, core capabilities, ecosystem, operations management, and the community roadmap.

Three Core Characteristics

Open Source OpenGemini uses the Apache 2.0 license, allowing commercial use, custom commercial versions, and the building of monitoring systems, IoT platforms, and related services.

High Performance Backed by Huawei Cloud SRE monitoring workloads, OpenGemini delivers excellent read/write throughput and efficient analytical queries.

Distributed Architecture Designed as a distributed cluster from the start, it offers strong scalability and flexibility beyond the limits of single‑node databases.

Focus on Massive Telemetry Data

With the rapid growth of cloud computing, AI, 5G, and IoT, industries such as automotive, manufacturing, logistics, power, and cloud operations generate petabytes of telemetry data daily. OpenGemini addresses this scale with high concurrency, low latency, and low cost.

In production, OpenGemini runs on about 25 clusters in Huawei Cloud, the largest with 70 nodes, handling 20 TB of data per day, write throughput of 40 million points/second, and read QPS of 50 k.

Six Key Capabilities

Performance Advantage Compared with InfluxDB, OpenGemini delivers more than 2× speedup on simple queries and over 5× on medium queries, while avoiding OOM failures on complex workloads.

Streaming Aggregation Performs real‑time aggregation and down‑sampling during data ingestion, reducing I/O amplification.

Multi‑Level Downsampling Extracts feature representations of historical data and replaces raw details, cutting storage costs by up to 50%.

Log Search Implements a lightweight full‑text index with dynamic tokenization, offering high‑efficiency log retrieval compared with traditional ELK stacks.

Anomaly Detection & Prediction An AI‑driven analysis framework detects 13 common anomaly patterns with fast, accurate, stream‑batch unified processing.

High‑Cardinality Engine A novel engine (HSCE) eliminates index bloat, supports unlimited timelines, and is being extended with additional aggregation methods.

Core Capabilities

OpenGemini is fully compatible with InfluxDB 1.x APIs, functions, and line protocol, can serve as a backend for Prometheus and OpenTelemetry, and will soon support data reliability, materialized views, partitioning, and retention policies.

Operations Management Component

The community provides the ts-monitor component to collect node and kernel metrics, which can be visualized in Grafana for real‑time monitoring of CPU, memory, write bandwidth, latency, concurrency, and QPS.

Ecosystem Integration

Because of InfluxDB compatibility, existing ingestion tools, SDKs, and analytics platforms work with OpenGemini. It runs on major Linux distributions, supports x86 and ARM64 (with macOS and Windows planned), and can be deployed via Docker, Kubernetes, KubeEdge, or the openGemini‑operator project. Migration tools from InfluxDB are available, and ES‑to‑OpenGemini migration is under development.

Overall, OpenGemini offers a versatile, open‑source solution for massive telemetry data storage and analysis, aiming to become a leading time‑series database community.