Graphite vs MongoDB
A detailed comparison
Compare Graphite and MongoDB 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 Graphite and MongoDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Graphite and MongoDB 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.
Graphite vs MongoDB Breakdown
 |
 |
|
| Database Model | Time series database |
Document database |
| Architecture | Graphite can be deployed on-premises or in the cloud, and it supports horizontal scaling by partitioning data across multiple backend nodes. |
MongoDB uses a flexible, JSON-like document model for storing data, which allows for dynamic schema changes without downtime. It supports ad hoc queries, indexing, and real-time aggregation. MongoDB can be deployed as a standalone server, in a replica set configuration for high availability, or as a sharded cluster for horizontal scaling. It is also available as a managed cloud service called MongoDB Atlas, which provides additional features like automated backups, monitoring, and global distribution. |
| License | Apache 2.0 |
SSPL for community edition, commercial licenses for other versions |
| Use Cases | Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring |
Content management systems, mobile applications, real-time analytics, IoT data management, e-commerce platforms |
| Scalability | Horizontally scalable, supports clustering and replication for high availability and performance |
Horizontally scalable with support for data sharding, replication, and automatic load balancing |
Looking for the most efficient way to get started?
Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.
Graphite Overview
Graphite is an open-source monitoring and graphing tool created in 2006 by Orbitz and open sourced in 2008. Graphite is designed for storing time series data and is widely used for collecting, storing, and visualizing metrics from various sources, such as application performance, system monitoring, and business analytics.
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.
Graphite for Time Series Data
Graphite is specifically designed and optimized for time series data. It uses the Whisper database format, which efficiently stores and manages time series data by automatically aggregating and expiring data based on user-defined retention policies. Graphite supports a wide range of functions for querying, transforming, and aggregating time series data, enabling users to create custom graphs and dashboards. However, as Graphite focuses exclusively on time series data, it may not be suitable for other types of data or use cases that require more advanced data modeling or querying capabilities.
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.
Graphite Key Concepts
- Metric: A metric in Graphite represents a time series data point, consisting of a path (name), timestamp, and value.
- Series: A series is a collection of metrics that are all related to the same thing. For example, you might have a series for CPU usage, a series for memory usage, and a series for disk usage.
- Whisper: Whisper is a fixed-size, file-based time series database format used by Graphite. It automatically manages data retention and aggregation.
- Carbon: Carbon is the daemon responsible for receiving, caching, and storing metrics in Graphite. It listens for incoming metrics and writes them to Whisper files.
- Graphite-web: Graphite-web is the web application that provides a user interface for visualizing and querying the stored 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.
Graphite Architecture
Graphite’s architecture consists of several components, including Carbon, Whisper, and Graphite-web. Carbon is responsible for receiving metrics from various sources, caching them in memory, and storing them in Whisper files. Whisper is a file-based time series database format that efficiently manages data retention and aggregation. Graphite-web is the web application that provides a user interface for querying and visualizing the stored time series data. Graphite can be deployed on a single server or distributed across multiple servers for improved performance and scalability.
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.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
Graphite Features
Real-time monitoring and visualization
Graphite provides real-time monitoring and visualization capabilities, allowing users to track and analyze their time series data as it is collected.
Flexible querying and aggregation functions
Graphite supports a wide range of functions for querying, transforming, and aggregating time series data, enabling users to create custom graphs and dashboards tailored to their specific needs.
Data retention and aggregation
Graphite’s Whisper database format automatically manages data retention and aggregation, reducing storage requirements and improving query performance.
MongoDB Features
Flexible Data Model
MongoDB’s schema-less data model allows for the storage and querying of diverse data types, making it well-suited for handling complex and evolving data structures.
High Availability
MongoDB’s replica set feature ensures high availability through automatic failover and data redundancy.
Horizontal Scalability
MongoDB’s sharded cluster architecture enables horizontal scaling and load balancing, allowing it to handle large-scale data processing and querying.
Graphite Use Cases
Application performance monitoring
Graphite is widely used for monitoring the performance of applications and services, helping developers and operations teams track key metrics, such as response times, error rates, and resource utilization. By visualizing these metrics in real-time, users can identify performance bottlenecks, detect issues, and optimize their applications for better performance and reliability.
Infrastructure and system monitoring
Graphite is also popular for monitoring the health and performance of servers, networks, and other infrastructure components. By collecting and analyzing metrics such as CPU usage, memory consumption, network latency, and disk I/O, IT administrators can ensure their infrastructure is running smoothly and proactively address potential issues before they impact system performance or availability.
Business analytics and metrics
In addition to technical monitoring, Graphite can be used for tracking and visualizing business-related metrics, such as user engagement, sales data, or marketing campaign performance. By visualizing and analyzing these metrics over time, business stakeholders can gain insights into trends, identify opportunities for growth, and make data-driven decisions to improve their operations.
MongoDB Use Cases
Content Management Systems
MongoDB’s flexible data model makes it an ideal choice for content management systems, which often require the ability to store and manage diverse content types, such as articles, images, and videos. The schema-less nature of MongoDB allows for easy adaptation to changing content structures and requirements.
IoT Data Storage and Analytics
MongoDB’s support for high data volumes and horizontal scalability makes it suitable for storing and processing data generated by IoT devices, such as sensor readings and device logs. Its ability to index and query data efficiently allows for real-time analytics and monitoring of IoT devices.
E-commerce Platforms
MongoDB’s flexibility and performance features make it an excellent choice for e-commerce platforms, where diverse product information, customer data, and transaction records need to be stored and queried efficiently. The flexible data model enables easy adaptation to changes in product attributes and customer preferences, while the high availability and scalability features ensure a smooth and responsive user experience.
Graphite Pricing Model
Graphite is an open-source project, and as such, it is freely available for users to download, install, and use without any licensing fees. However, users are responsible for setting up and maintaining their own Graphite infrastructure, which may involve costs related to server hardware, storage, and operational expenses. There are also several commercial products and services that build on top of or integrate with Graphite, offering additional features, support, or managed hosting options at varying price points.
MongoDB Pricing Model
MongoDB offers various pricing options, including a free, open-source Community Edition and a commercial Enterprise Edition, which includes advanced features, management tools, and support. MongoDB Inc. also offers a fully managed cloud-based database-as-a-service, MongoDB Atlas, with a pay-as-you-go pricing model based on storage, data transfer, and compute resources. MongoDB Atlas offers a free tier with limited resources for users who want to try the service without incurring costs.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.
