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 MongoDB and VictoriaMetrics so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how MongoDB 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.

MongoDB vs VictoriaMetrics Breakdown


 
Database Model

Document database

Time series database

Architecture

MongoDB uses a flexible, JSON-like document model for storing data, which allows for dynamic schema changes without downtime. It supports ad hoc queries, indexing, and real-time aggregation. MongoDB can be deployed as a standalone server, in a replica set configuration for high availability, or as a sharded cluster for horizontal scaling. It is also available as a managed cloud service called MongoDB Atlas, which provides additional features like automated backups, monitoring, and global distribution.

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

SSPL for community edition, commercial licenses for other versions

Apache 2.0

Use Cases

Content management systems, mobile applications, real-time analytics, IoT data management, e-commerce platforms

Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring

Scalability

Horizontally scalable with support for data sharding, replication, and automatic load balancing

Horizontally scalable, supports clustering and replication for high availability and performance

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MongoDB Overview

MongoDB is a popular, open-source NoSQL database launched in 2009. Designed to handle large volumes of unstructured and semi-structured data, MongoDB offers a flexible, schema-less data model, horizontal scalability, and high performance. Its ease of use, JSON-based document storage, and support for a wide range of programming languages have contributed to its widespread adoption across various industries and applications.

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.


MongoDB for Time Series Data

Although MongoDB is a general-purpose NoSQL database, it can be used for storing and processing time series data. The flexible data model of MongoDB allows for easy adaptation to the evolving structure of time series data, such as the addition of new metrics or the modification of existing ones. MongoDB provides built-in support for time-to-live (TTL) indexes, which automatically expire old data after a specified time period, making it suitable for managing large volumes of time series data with a limited storage capacity. MongoDB has also recently added a custom columnar storage engine and time series collection for time series use cases, meant to improve performance over the default MongoDB storage engine in terms of data compression and query performance.

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.


MongoDB Key Concepts

Some key terminology and concepts specific to MongoDB include:

  • Database: A MongoDB database is a container for collections, which are groups of related documents.
  • Collection: A collection in MongoDB is analogous to a table in relational databases, holding a set of documents.
  • Document: A document in MongoDB is a single record, stored in a JSON-like format called BSON (Binary JSON). Documents within a collection can have different structures.
  • Field: A field is a key-value pair within a document, similar to an attribute or column in a relational database.
  • Index: An index in MongoDB is a data structure that improves the query performance on specific fields within a collection.

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.


MongoDB Architecture

MongoDB’s architecture is centered around its flexible, document-based data model. As a NoSQL database, MongoDB supports a schema-less structure, which allows for the storage and querying of diverse data types, such as nested arrays and documents. MongoDB can be deployed as a standalone server, a replica set, or a sharded cluster. Replica sets provide high availability through automatic failover and data redundancy, while sharded clusters enable horizontal scaling and load balancing by distributing data across multiple servers based on a shard key.

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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MongoDB Features

Flexible Data Model

MongoDB’s schema-less data model allows for the storage and querying of diverse data types, making it well-suited for handling complex and evolving data structures.

High Availability

MongoDB’s replica set feature ensures high availability through automatic failover and data redundancy.

Horizontal Scalability

MongoDB’s sharded cluster architecture enables horizontal scaling and load balancing, allowing it to handle large-scale data processing and querying.

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.


MongoDB Use Cases

Content Management Systems

MongoDB’s flexible data model makes it an ideal choice for content management systems, which often require the ability to store and manage diverse content types, such as articles, images, and videos. The schema-less nature of MongoDB allows for easy adaptation to changing content structures and requirements.

IoT Data Storage and Analytics

MongoDB’s support for high data volumes and horizontal scalability makes it suitable for storing and processing data generated by IoT devices, such as sensor readings and device logs. Its ability to index and query data efficiently allows for real-time analytics and monitoring of IoT devices.

E-commerce Platforms

MongoDB’s flexibility and performance features make it an excellent choice for e-commerce platforms, where diverse product information, customer data, and transaction records need to be stored and queried efficiently. The flexible data model enables easy adaptation to changes in product attributes and customer preferences, while the high availability and scalability features ensure a smooth and responsive user experience.

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.


MongoDB Pricing Model

MongoDB offers various pricing options, including a free, open-source Community Edition and a commercial Enterprise Edition, which includes advanced features, management tools, and support. MongoDB Inc. also offers a fully managed cloud-based database-as-a-service, MongoDB Atlas, with a pay-as-you-go pricing model based on storage, data transfer, and compute resources. MongoDB Atlas offers a free tier with limited resources for users who want to try the service without incurring costs.

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.