What is GTSDB?
Durable and Memory Friendly timeseries database. Utilizes Write Ahead Log (WAL) for records, reducing IO and memory usage. 19,172 ns/op. In-memory-like speed. WAL-class durability.
Problem
Users working with time-series data often use traditional databases which may not efficiently handle large-scale time-series workloads.
The old solutions have high memory usage and input/output overhead, leading to performance bottlenecks.
high memory usage and input/output overhead leading to performance bottlenecks.
Solution
GTSDB is a durable and memory-friendly time-series database that utilizes Write Ahead Log (WAL) for records.
high performance with reduced IO and memory usage, allowing speeds close to in-memory databases while maintaining durability.
It manages 19,172 ns/op, providing efficient data storage and retrieval.
Customers
Data analysts and engineers working in sectors that manage large volumes of time-series data
including industries like finance, IoT, and environmental monitoring.
Organizations looking to optimize database durability and memory efficiency.
Unique Features
Utilizes Write Ahead Log (WAL) for enhanced durability while maintaining minimal IO and memory footprint.
Delivers in-memory-like speed with a performance of 19,172 ns/op.
Durable approach combined with efficient memory management, which is not typically offered by traditional databases.
User Comments
Highly praised for its speed and efficiency in time-series data management.
Users appreciate the memory and IO optimization features.
Considered a reliable solution due to its durable logging method.
Seen as a game-changer for handling large datasets with minimal resource usage.
Valued for its ease of integration into existing data workflows.
Traction
Recent launch with growing interest among data-heavy industries.
Initial statistics and performance indicators show promising results compared to traditional databases.
Market Size
The global time-series database market is anticipated to grow steadily, with a market size expected to reach $16.2 billion by 2025, driven by increased demand from sectors like IoT, finance, and analytics.
