AWS Redshift vs VictoriaMetrics

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

The primary purpose of this article is to compare how AWS Redshift 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.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.

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.

AWS Redshift for Time Series Data

AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.

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.

AWS Redshift Key Concepts

• Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
• Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
• Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
• Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.

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.

AWS Redshift Architecture

Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.

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.