A Time Series Database (TSDB) stores and processes chronologically ordered data points, each containing a timestamp and one or more metric values. This data is prevalent in IoT, monitoring, financial trading, industrial control, sensor networks, weather records, and server metric monitoring.
Background
TSDBs emerged for several key reasons:
-
Rapid IoT growth: A massive number of sensors and devices generate high-volume, time-stamped data (temperature, humidity, device status) that traditional relational databases cannot ingest or query efficiently.
-
Growing monitoring demands: IT O&M, industrial control, and energy management systems require high-concurrency writes, efficient compression, and fast time-range queries that traditional databases cannot sustain.
-
Big data analytics and prediction: Predictive maintenance, market forecasting, and climate modeling require databases that store large-scale time series data and support complex analytical queries.
-
Resource optimization and cost control: TSDBs use data compression, efficient indexing, and storage optimization to reduce storage and compute costs while maintaining query performance.
TSDBs address these needs with high write throughput, data compression, and optimized time-range queries.
Features
GanosBase TSDB is a time series database built on the cloud-native database . It is fully compatible with TimescaleDB Apache 2.0 and adds continuous aggregation, time series compression, and statistical analysis. Key features:
-
High-performance reads and writes: GanosBase TSDB handles tens of thousands of writes per second and provides fast historical data retrieval for real-time monitoring.
-
Continuous aggregation: Define materialized views that automatically compute aggregates (sum, average, max, min) over time intervals (minute, hour, day). GanosBase TSDB refreshes these views in the background as data changes.
-
Cross-modality fusion: Store and query time series alongside spatiotemporal data. Multi-dimensional partitioning (time, space) with automatic partition pruning accelerates retrieval.
-
Low-cost storage: GanosBase TSDB achieves over 70% storage reduction through deduplication and high-ratio compression. Cold data can be archived to Object Storage Service (OSS) to further reduce costs.
-
Data retention policies: Configure automatic deletion of expired data based on business rules to control storage costs and maintain performance.
-
Extensibility and high availability: GanosBase TSDB leverages PolarDB for horizontal scaling and automatic failover.
Together, these features make GanosBase TSDB well suited for large-scale monitoring, analytics, and prediction workloads.
Use cases
GanosBase TSDB applies to the following scenarios:
-
IoT: Smart sensors and connected devices continuously emit time-stamped metrics (temperature, humidity, pressure) that require real-time ingestion and analysis for remote monitoring and fault prediction.
-
Monitoring: IT infrastructure (server performance, network traffic) and application performance management systems track metrics over time to detect anomalies and optimize performance.
-
Financial services: Stock prices, trading volumes, and exchange rates are time series data suited for high-frequency trading analysis, trend prediction, and risk management.
-
Energy and utilities: Store and analyze operational and consumption data from smart grids and hydropower stations to improve energy distribution and enable predictive maintenance.
-
Industrial automation: Manufacturing equipment status monitoring and process control generate high-volume time series data for device health management and production optimization.