Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Graphite and Prometheus so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Graphite and Prometheus perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
Graphite vs Prometheus Breakdown
Time series database
Time series database
Graphite can be deployed on-premises or in the cloud, and it supports horizontal scaling by partitioning data across multiple backend nodes.
Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes.
Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring
Monitoring, alerting, observability, system metrics, application metrics
Horizontally scalable, supports clustering and replication for high availability and performance
Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers)
Graphite is an open-source monitoring and graphing tool created in 2006 by Orbitz and open sourced in 2008. Graphite is designed for storing time series data and is widely used for collecting, storing, and visualizing metrics from various sources, such as application performance, system monitoring, and business analytics.
Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.
Graphite for Time Series Data
Graphite is specifically designed and optimized for time series data. It uses the Whisper database format, which efficiently stores and manages time series data by automatically aggregating and expiring data based on user-defined retention policies. Graphite supports a wide range of functions for querying, transforming, and aggregating time series data, enabling users to create custom graphs and dashboards. However, as Graphite focuses exclusively on time series data, it may not be suitable for other types of data or use cases that require more advanced data modeling or querying capabilities.
Prometheus for Time Series Data
Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.
Graphite Key Concepts
- Metric: A metric in Graphite represents a time series data point, consisting of a path (name), timestamp, and value.
- Series: A series is a collection of metrics that are all related to the same thing. For example, you might have a series for CPU usage, a series for memory usage, and a series for disk usage.
- Whisper: Whisper is a fixed-size, file-based time series database format used by Graphite. It automatically manages data retention and aggregation.
- Carbon: Carbon is the daemon responsible for receiving, caching, and storing metrics in Graphite. It listens for incoming metrics and writes them to Whisper files.
- Graphite-web: Graphite-web is the web application that provides a user interface for visualizing and querying the stored time series data.
Prometheus Key Concepts
- Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
- Time Series: A collection of data points for a metric, indexed by timestamp.
- Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
- PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.
Graphite’s architecture consists of several components, including Carbon, Whisper, and Graphite-web. Carbon is responsible for receiving metrics from various sources, caching them in memory, and storing them in Whisper files. Whisper is a file-based time series database format that efficiently manages data retention and aggregation. Graphite-web is the web application that provides a user interface for querying and visualizing the stored time series data. Graphite can be deployed on a single server or distributed across multiple servers for improved performance and scalability.
Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.
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Real-time monitoring and visualization
Graphite provides real-time monitoring and visualization capabilities, allowing users to track and analyze their time series data as it is collected.
Flexible querying and aggregation functions
Graphite supports a wide range of functions for querying, transforming, and aggregating time series data, enabling users to create custom graphs and dashboards tailored to their specific needs.
Data retention and aggregation
Graphite’s Whisper database format automatically manages data retention and aggregation, reducing storage requirements and improving query performance.
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
Graphite Use Cases
Application performance monitoring
Graphite is widely used for monitoring the performance of applications and services, helping developers and operations teams track key metrics, such as response times, error rates, and resource utilization. By visualizing these metrics in real-time, users can identify performance bottlenecks, detect issues, and optimize their applications for better performance and reliability.
Infrastructure and system monitoring
Graphite is also popular for monitoring the health and performance of servers, networks, and other infrastructure components. By collecting and analyzing metrics such as CPU usage, memory consumption, network latency, and disk I/O, IT administrators can ensure their infrastructure is running smoothly and proactively address potential issues before they impact system performance or availability.
Business analytics and metrics
In addition to technical monitoring, Graphite can be used for tracking and visualizing business-related metrics, such as user engagement, sales data, or marketing campaign performance. By visualizing and analyzing these metrics over time, business stakeholders can gain insights into trends, identify opportunities for growth, and make data-driven decisions to improve their operations.
Prometheus Use Cases
Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.
Application Performance Monitoring (APM)
Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.
Alerting and Anomaly Detection
Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.
Graphite Pricing Model
Graphite is an open-source project, and as such, it is freely available for users to download, install, and use without any licensing fees. However, users are responsible for setting up and maintaining their own Graphite infrastructure, which may involve costs related to server hardware, storage, and operational expenses. There are also several commercial products and services that build on top of or integrate with Graphite, offering additional features, support, or managed hosting options at varying price points.
Prometheus Pricing Model
Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.
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