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 Redis and VictoriaMetrics so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Redis 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.
Redis vs VictoriaMetrics Breakdown
Time series database
Redis 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.
Caching, message brokering, real-time analytics, session storage, geospatial data processing
Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring
Horizontally scalable via partitioning and clustering, supports data replication
Horizontally scalable, supports clustering and replication for high availability and performance
Redis, which stands for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It was created by Salvatore Sanfilippo in 2009 and has since gained significant popularity due to its high performance and flexibility. Redis supports various data structures, such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, and geospatial indexes with radius queries.
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.
Redis for Time Series Data
Redis has a dedicated module for working with time series data called RedisTimeSeries. RedisTimeSeries offers functionality like downsampling, data retention policies, and specialized queries for time series data in Redis. Being an in-memory database, Redis will be very fast for reading and writing time series data, but due to the cost of RAM compared to disk using Redis could become expensive depending on the size of your dataset. If your use case doesn’t require extremely fast response times, you could save money by going with a more traditional time series database.
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.
Redis Key Concepts
- In-memory store: Redis stores data in memory, which allows for faster data access and manipulation compared to disk-based databases .
- Data structures: Redis supports a wide range of data structures, including strings, hashes, lists, sets, and more, which provide flexibility in how data is modeled and stored.
- Persistence: Redis offers optional data persistence, allowing data to be periodically saved to disk or written to a log for durability.
- Pub/Sub: Redis provides a publish/subscribe messaging system, enabling real-time communication between clients without the need for a centralized message broker.
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.
Redis is a NoSQL database that uses a key-value data model, where each key is associated with a value stored as one of Redis’ supported data structures. The database is single-threaded, which simplifies its internal architecture and reduces contention. Redis can be deployed as a standalone server, a cluster, or a master-replica setup for scalability and high availability. The Redis Cluster mode automatically shards data across multiple nodes, providing data partitioning and fault tolerance.
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.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
Redis supports atomic operations on complex data types, allowing developers to perform powerful operations without worrying about race conditions or other concurrent processing issues.
Broad data structure support
Redis supports a range of data structures such as lists, sets, sorted sets, hashes, bitmaps, hyperloglog, and geospatial indexes. This flexibility allows developers to use Redis for a wide variety of tasks by using data structures that are optimized for their data in terms of performance characteristics.
Redis provides a publish/subscribe messaging system for real-time communication between clients.
Developers can run Lua scripts in the Redis server, enabling complex operations to be executed atomically in the server itself, reducing network round trips.
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.
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.
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.
Redis Use Cases
Redis is often used as a cache to store frequently accessed data and reduce the load on other databases or services, improving application performance and reducing latency.
Redis can be used to implement task queues, which are useful for managing tasks that take longer to process and should be executed asynchronously. This is particularly common in web applications, where background tasks can be processed independently of the request/response cycle
Real-time analysis and machine learning
Redis’ high performance and low-latency data access make it suitable for real-time analysis and machine learning applications, such as processing streaming data, media streaming, and handling time-series data. This can be achieved using Redis’ data structures and capabilities like sorted sets, timestamps, and pub/sub messaging.
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.
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.
Redis Pricing Model
Redis is open-source software, which means it can be deployed and used freely on your own infrastructure. However, there are also managed Redis services available, such as Redis Enterprise which offer additional features, support, and ease of deployment. Pricing for these services typically depends on factors like the size of the instance, data storage, and data transfer.
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.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.