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 RRDtool so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how AWS Redshift and RRDtool 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 vs RRDtool Breakdown

Database Model

Data warehouse

Time series database


AWS Redshift utilizes a columnar storage format for fast querying and supports standard SQL. Redshift uses a distributed, shared-nothing architecture, where data is partitioned across multiple compute nodes. Each node is further divided into slices, with each slice processing a subset of data in parallel. Redshift can be deployed in a single-node or multi-node cluster, with the latter providing better performance for large datasets.

RRDtool is a single-node, non-distributed database generally deployed on a single machine


Closed source


Use Cases

Business analytics, large-scale data processing, real-time dashboards, data integration, machine learning

Monitoring, observability, Network performance tracking, System metrics, Log data storage


Supports scaling storage and compute independently, with support for adding or removing nodes as needed

Limited scalability- more suitable for small to medium-sized datasets

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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.

RRDtool Overview

RRDtool, short for Round-Robin Database Tool, is an open-source, high-performance data logging and graphing system designed to handle time series data. Created by Tobias Oetiker in 1999, RRDtool is specifically built for storing and visualizing time-series data, such as network bandwidth, temperatures, or CPU load. Its primary feature is the efficient storage of data points, using a fixed-size database that automatically aggregates and archives older data points, ensuring that the database size remains constant over time.

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.

RRDtool for Time Series Data

RRDtool was created for time series data storage and visualization, making it a great fit for applications that require efficient handling of this type of data. Its round-robin database structure ensures constant storage space usage while providing automatic data aggregation and archiving. However, RRDtool may not be suitable for applications that require complex queries or relational data storage, as its focus is primarily on 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.

RRDtool Key Concepts

  • Round-robin database: A fixed-size database that stores time-series data using a circular buffer, overwriting older data as new data is added.
  • RRD file: A single file that contains all the configuration and data for an RRDtool database.
  • Consolidation function: A function that aggregates multiple data points into a single data point, such as AVERAGE, MIN, MAX, or LAST.

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.

RRDtool Architecture

RRDtool is a specialized time series database that does not use SQL or a traditional relational data model. Instead, it employs a round-robin database structure, with data points stored in a fixed-size, circular buffer. RRDtool is a command-line tool that can be used to create and update RRD files, as well as generate graphs and reports from the stored data. It can be integrated with various scripting languages, such as Perl, Python, and Ruby, through available bindings.

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AWS Redshift Features


Redshift allows you to scale your cluster up or down by adding or removing compute nodes, enabling you to adjust your storage capacity and query performance based on your needs.


Redshift’s columnar storage format and MPP architecture enable it to deliver high-performance query execution for large-scale data warehousing workloads.


Redshift provides a range of security features, including encryption at rest and in transit, network isolation using Amazon Virtual Private Cloud (VPC), and integration with AWS Identity and Access Management (IAM) for access control.

RRDtool Features

Efficient Data Storage

RRDtool’s round-robin database structure ensures constant storage space usage, automatically aggregating and archiving older data points.


RRDtool provides powerful graphing capabilities, allowing users to generate customizable graphs and reports from the stored time series data.

Cross-Platform Support

RRDtool is available on various platforms, including Linux, Unix, macOS, and Windows.

AWS Redshift Use Cases

Data Warehousing

Redshift is designed for large-scale data warehousing workloads, providing a scalable and high-performance solution for storing and analyzing structured data.

Business Intelligence and Reporting

Redshift integrates with various BI and reporting tools, enabling organizations to gain insights from their data and make data-driven decisions.

ETL and Data Integration

Redshift supports data loading and extraction, transformation, and loading (ETL) processes, allowing you to integrate data from various sources and prepare it for analysis.

RRDtool Use Cases

Network Monitoring

RRDtool is often used in network monitoring applications to store and visualize metrics such as bandwidth usage, latency, and packet loss.

Environmental Monitoring

RRDtool can be used to track and visualize environmental data, such as temperature, humidity, and air pressure, over time.

System Performance Monitoring

RRDtool is suitable for storing and displaying system performance metrics, like CPU usage, memory consumption, and disk I/O, for server and infrastructure monitoring.

AWS Redshift Pricing Model

Amazon Redshift offers two pricing models: On-Demand and Reserved Instances. With On-Demand pricing, you pay for the capacity you use on an hourly basis, with no long-term commitments. Reserved Instances offer the option to reserve capacity for a one- or three-year term, with a lower hourly rate compared to On-Demand pricing. In addition to these pricing models, you can also choose between different node types, which offer different amounts of storage, memory, and compute resources.

RRDtool Pricing Model

RRDtool is an open-source software, freely available for use under the GNU General Public License. Users can download, use, and modify the software at no cost. There are no commercial licensing options or paid support services offered directly by the project.