MongoDB vs RRDtool

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

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

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.

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.

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.

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.

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.

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.

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.

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.