Apache Doris vs Prometheus
A detailed comparison
Compare Apache Doris and Prometheus for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Apache Doris and Prometheus so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Doris 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.
Apache Doris vs Prometheus Breakdown
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 |
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| Database Model | Data warehouse |
Time series database |
| Architecture | Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON. |
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. |
| License | Apache 2.0 |
Apache 2.0 |
| Use Cases | Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding |
Monitoring, alerting, observability, system metrics, application metrics |
| Scalability | Horizontally scalable with distributed storage and compute |
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) |
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Apache Doris Overview
Apache Doris is an MPP-based interactive SQL data warehousing system designed for reporting and analysis. It is known for its high performance, real-time analytics capabilities, and ease of use. Apache Doris integrates technologies from Google Mesa and Apache Impala. Unlike other SQL-on-Hadoop systems, Doris is designed to be a simple and tightly coupled system that does not rely on external dependencies. It aims to provide a streamlined and efficient solution for data warehousing and analytics.
Prometheus Overview
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.
Apache Doris for Time Series Data
Apache Doris can be effectively used with time series data for real-time analytics and reporting. With its high performance and sub-second response time, Doris can handle massive amounts of time-stamped data and provide timely query results. It supports both high-concurrent point query scenarios and high-throughput complex analysis scenarios, making it suitable for analyzing time series data with varying levels of complexity.
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.
Apache Doris Key Concepts
- MPP (Massively Parallel Processing): Apache Doris leverages MPP architecture, which allows it to distribute data processing across multiple nodes, enabling parallel execution and scalability.
- SQL: Apache Doris supports SQL as the query language, providing a familiar and powerful interface for data analysis and reporting.
- Point Query: Point query refers to retrieving a specific data point or a small subset of data from the database.
- Complex Analysis: Apache Doris can handle complex analysis scenarios that involve processing large volumes of data and performing advanced computations and aggregations.
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.
Apache Doris Architecture
Apache Doris is based on MPP architecture, which enables it to distribute data and processing across multiple nodes for parallel execution. It is a standalone system and does not depend on other systems or frameworks. Apache Doris combines the technology of Google Mesa and Apache Impala to provide a simple and tightly coupled system for data warehousing and analytics. It leverages SQL as the query language and supports efficient data processing and query optimization techniques to ensure high performance and scalability.
Prometheus Architecture
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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Apache Doris Features
High Performance
Apache Doris is designed for high-performance data analytics, delivering sub-second query response times even with massive amounts of data.
Real-Time Analytics
Apache Doris enables real-time data analysis, allowing users to gain insights and make informed decisions based on up-to-date information.
Scalability
Apache Doris can scale horizontally by adding more nodes to the cluster, allowing for increased data storage and processing capacity.
Prometheus Features
Pull-based Model
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
PromQL
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Alerting
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
Apache Doris Use Cases
Real-Time Analytics
Apache Doris is well-suited for real-time analytics scenarios where timely insights and analysis of large volumes of data are crucial. It enables businesses to monitor and analyze real-time data streams, make data-driven decisions, and detect patterns or anomalies in real time.
Reporting and Business Intelligence
Apache Doris can be used for generating reports and conducting business intelligence activities. It supports fast and efficient querying of data, allowing users to extract meaningful insights and visualize data for reporting and analysis purposes.
Data Warehousing
Apache Doris is suitable for building data warehousing solutions that require high-performance analytics and querying capabilities. It provides a scalable and efficient platform for storing, managing, and analyzing large volumes of data for reporting and decision-making.
Prometheus Use Cases
Infrastructure Monitoring
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.
Apache Doris Pricing Model
As an open-source project, Apache Doris is freely available for usage and does not require any licensing fees. Users can download the source code and set up Apache Doris on their own infrastructure without incurring any direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
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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