Prometheus vs Riemann: What are the differences?

Introduction Prometheus and Riemann are both popular monitoring systems used in the field of software development. While they both serve the purpose of monitoring and alerting, there are key differences between the two.

1. Data Model: Prometheus uses a time-series data model, where each sample has a unique timestamp and a set of key-value pairs. This allows for flexible querying and analysis of data over time. On the other hand, Riemann uses an event-based data model, where each event is a map or dictionary-like structure. This structure allows for easy manipulation of events but may not provide the same level of analysis as time-series data.

2. Data Collection: Prometheus collects data through a pull model, where it regularly scrapes targets and collects metrics at regular intervals. It supports a wide range of exporters and integrations, making it easy to collect metrics from different systems. In contrast, Riemann uses a push model, where clients send events to the Riemann server. It allows for real-time data streaming and is well-suited for real-time monitoring and alerting.

3. Aggregation: Prometheus provides built-in support for aggregation functions like sum, average, and rate calculations. This allows for easy analysis of data over time and helps in identifying trends and anomalies. Riemann, on the other hand, focuses more on event processing and does not provide built-in aggregation functions. Aggregation needs to be handled externally, either before sending events to Riemann or in the consumer layer.

4. Alerting: Prometheus has a robust alerting system built into its core. It allows users to define complex alerting rules based on query expressions and supports various notification channels. Riemann also supports alerting but with a more simplified approach. It allows users to define thresholds and actions for events that cross those thresholds. While Prometheus provides more advanced alerting capabilities, Riemann's simplicity can be advantageous for certain use cases.

5. Scalability: Prometheus is designed to be highly scalable and can handle a large number of time-series data. It supports efficient data storage and retrieval, making it suitable for large-scale monitoring setups. Riemann, on the other hand, is not inherently designed for scalability and may face challenges when dealing with a large volume of events. However, Riemann can be used in a distributed setup to overcome some scalability limitations.

6. Ecosystem and Integrations: Prometheus has a vibrant ecosystem with a wide range of exporters, libraries, and integrations available. It integrates well with other monitoring tools like Grafana, Alertmanager, and Kubernetes. Riemann also has a good ecosystem but with a smaller community. It provides various client libraries, plugins, and integrations with tools like Graphite, StatsD, and Nagios.

In Summary, Prometheus and Riemann differ in their data models, data collection approaches, aggregation capabilities, alerting systems, scalability, and ecosystem/integrations. These differences make each tool suitable for different monitoring use cases and requirements.