Apache Doris vs VictoriaMetrics
A detailed comparison
Compare Apache Doris and VictoriaMetrics 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 VictoriaMetrics so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Doris and VictoriaMetrics 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 VictoriaMetrics 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. |
VictoriaMetrics can be deployed as a single-node instance for small-scale applications or as a clustered setup for large-scale applications, offering horizontal scalability and replication. |
| License | Apache 2.0 |
Apache 2.0 |
| Use Cases | Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding |
Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring |
| Scalability | Horizontally scalable with distributed storage and compute |
Horizontally scalable, supports clustering and replication for high availability and performance |
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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.
VictoriaMetrics Overview
VictoriaMetrics is an open source time series database developed by the company VictoriaMetrics. The database aims to assist individuals and organizations in addressing their big data challenges by providing state-of-the-art monitoring and observability solutions. VictoriaMetrics is designed to be a fast, cost-effective, and scalable monitoring solution and time series database.
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.
VictoriaMetrics for Time Series Data
VictoriaMetrics is designed for time series data, making it a solid choice for applications that involve the storage and analysis of time-stamped data. It provides high-performance storage and retrieval capabilities, enabling efficient handling of large volumes of time series data.
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.
VictoriaMetrics Key Concepts
- Time Series: VictoriaMetrics stores data in the form of time series, which are sequences of data points indexed by time.
- Metric: A metric represents a specific measurement or observation that is tracked over time.
- Tag: Tags are key-value pairs associated with a time series and are used for filtering and grouping data.
- Field: Fields contain the actual data values associated with a time series.
- Query Language: VictoriaMetrics supports its own query language, which allows users to retrieve and analyze time series data based on specific criteria.
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.
VictoriaMetrics Architecture
VictoriaMetrics is available in two forms: Single-server-VictoriaMetrics and VictoriaMetrics Cluster. The Single-server-VictoriaMetrics is an all-in-one binary that is easy to use and maintain. It vertically scales well and can handle millions of metrics per second. On the other hand, VictoriaMetrics Cluster consists of components that allow for building horizontally scalable clusters, enabling high availability and scalability in demanding environments. The architecture of VictoriaMetrics enables users to choose the deployment option that best suits their needs and scale their database infrastructure as required.
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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.
VictoriaMetrics Features
High performance
VictoriaMetrics is optimized for high-performance storage and retrieval of time series data. It can efficiently handle millions of metrics per second and offers fast query execution for real-time analysis.
Scalability
The architecture of VictoriaMetrics allows for both vertical and horizontal scalability, enabling users to scale their monitoring and time series database infrastructure as their data volume and demand grow.
Cost-effectiveness
VictoriaMetrics provides a cost-effective solution for managing time series data. Its efficient storage and query capabilities contribute to minimizing operational costs while maintaining high performance.
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.
VictoriaMetrics Use Cases
Monitoring and Observability
VictoriaMetrics is widely used for monitoring and observability purposes, allowing organizations to collect, store, and analyze metrics and performance data from various systems and applications. It provides the necessary tools and capabilities to track and visualize key performance indicators, troubleshoot issues, and gain insights into system behavior.
IoT Data Management
VictoriaMetrics is suitable for handling large volumes of time series data generated by IoT devices. It can efficiently store and process sensor data, enabling real-time monitoring and analysis of IoT ecosystems. VictoriaMetrics allows for tracking and analyzing data from factories, manufacturing plants, satellites, and other IoT devices.
Capacity Planning
VictoriaMetrics enables retrospective analysis and forecasting of metrics for capacity planning purposes. It allows organizations to analyze historical data, identify patterns and trends, and make informed decisions about resource allocation and future capacity requirements.
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.
VictoriaMetrics Pricing Model
VictoriaMetrics is an open source project, which means it is available for free usage and doesn’t require any licensing fees. Users can download the binary releases, Docker images, or source code to set up and deploy VictoriaMetrics without incurring any direct costs. VictoriaMetrics also has paid offerings for on-prem Enterprise products and managed VictoriaMetrics instances.
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