Apache Cassandra vs VictoriaMetrics
A detailed comparison
Compare Apache Cassandra 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 Cassandra and VictoriaMetrics so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Cassandra 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 Cassandra vs VictoriaMetrics Breakdown
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| Database Model | Distributed wide-column database |
Time series database |
| Architecture | Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service |
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 | High write throughput applications, time series data, messaging systems, recommendation engines, IoT |
Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring |
| Scalability | Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added |
Horizontally scalable, supports clustering and replication for high availability and performance |
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Apache Cassandra Overview
Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.
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 Cassandra for Time Series Data
Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.
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 Cassandra Key Concepts
- Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
- Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
- Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
- Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.
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 Cassandra Architecture
Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.
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 Cassandra Features
Linear Scalability
Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.
High Availability
With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.
Tunable Consistency
Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.
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 Cassandra Use Cases
Messaging and Social Media Platforms
Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.
IoT and Distributed Systems
With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.
E-commerce
Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.
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 Cassandra Pricing Model
Apache Cassandra 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 Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.
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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