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QuestDB: High Performance Time-Series Database for Analytics & Monitoring
QuestDB is a high-performance, open-source SQL database for time series. It's designed for ingesting and querying high-throughput time-series data with SQL, featuring columnar storage and just-in-time compilation.
Java
Added on 2025年7月6日15,363
Stars1,269
ForksJava
LanguageProject Introduction
Summary
QuestDB is purpose-built for time-series workloads, offering significantly faster ingestion and query performance compared to general-purpose databases when dealing with time-stamped data. It provides a familiar SQL interface, making it accessible to a broad range of users.
Problem Solved
Traditional relational databases often struggle with the volume, velocity, and unique query patterns of time-series data, leading to performance bottlenecks and complex data management.
Core Features
SQL with Time-Series Extensions
Supports standard SQL with time-series extensions for tasks like time-based windowing, sampling, and joins.
High Performance Ingestion
Optimized for high-throughput data ingestion using InfluxDB Line Protocol, PostgreSQL wire protocol, and REST API.
Columnar Storage & Vectorization
Uses columnar storage and SIMD instructions for fast analytical queries on large datasets.
Tech Stack
Java
C++
SQL
Columnar Storage
Time-Series Data
Use Cases
QuestDB excels in scenarios requiring efficient storage and analysis of time-stamped data points.
IoT Data Management
Details
Collecting and analyzing data from thousands of IoT sensors, vehicles, or industrial machines.
User Value
Enables real-time dashboards and historical analysis for device monitoring and predictive maintenance.
Financial Market Data Analysis
Details
Storing and querying high-frequency trading data or market feeds for algorithmic trading and analysis.
User Value
Provides the speed necessary for capturing market events and performing rapid historical analysis.
Application Performance Monitoring (APM)
Details
Aggregating and querying application metrics, logs, and traces for performance monitoring and incident response.
User Value
Offers efficient storage and querying for large volumes of monitoring data, improving system observability.
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