Watchman uses a scalable architecture based on a distributed systems approach, which enables it to handle a large number of incoming data points with high availability. It also features an efficient query engine that optimizes performance for time-series data analysis, making it suitable for real-time monitoring and historical data analysis.
Key features of Watchman include:
- Scalability: Watchman is designed to scale horizontally to handle large volumes of time series data. It uses a distributed architecture with multiple servers that can be added or removed as needed to increase or decrease capacity.
- High Availability: Watchman provides high availability by replicating data across multiple servers. In case of a server failure, the data can be seamlessly accessed from another server without any data loss.
- Fast Query Engine: Watchman's query engine is optimized for fast retrieval and aggregation of time series data. It supports various query options such as time range filtering, aggregations (e.g., average, sum), and pattern matching.
- Flexible Data Ingestion: Watchman can ingest data from a variety of sources, including files, logs, and IoT devices. It supports different data formats such as JSON, CSV, and custom binary formats.
- Multi-tenant Architecture: Watchman supports multiple tenants, allowing different teams or organizations to store and manage their own time series data independently.
- Alerting and Notifications: Watchman provides features for setting alerts and notifications based on specific data patterns or thresholds. It can integrate with various messaging systems or external services for sending notifications.
- Open Source: Watchman is an open-source project, which means it is freely available for use and modification. It is written in C++ and has a modular architecture, enabling developers to extend and customize it to meet their specific requirements.
Watchman is widely used by large enterprises, including Facebook, Netflix, and Airbnb, for monitoring and analyzing their production systems. It is a powerful tool for managing and extracting insights from vast quantities of time series data, making it a valuable asset for organizations looking to improve their monitoring and alerting capabilities.