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 M3 and MongoDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how M3 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.

M3 vs MongoDB Breakdown


 
Database Model

Time series database

Document database

Architecture

The M3 stack can be deployed on-premises or in the cloud, using containerization technologies like Kubernetes or as a managed service on platforms like AWS or GCP

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, large-scale metrics processing

Content management systems, mobile applications, real-time analytics, IoT data management, e-commerce platforms

Scalability

Horizontally scalable, designed for high availability and large-scale deployments

Horizontally scalable with support for data sharding, replication, and automatic load balancing

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M3 Overview

M3 is a distributed time series database written entirely in Go. It is designed to collect a high volume of monitoring time series data, distribute storage in a horizontally scalable manner, and efficiently leverage hardware resources. M3 was initially developed by Uber as a scalable remote storage backend for Prometheus and Graphite and later open-sourced for broader use.

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.


M3 for Time Series Data

M3 is specifically designed for time-series data. It is a distributed and scalable time-series database optimized for handling large volumes of high-resolution data points, making it an ideal solution for storing, querying, and analyzing time-series data.

M3’s architecture focuses on providing fast and efficient querying capabilities, as well as high ingestion rates, which are essential for working with time-series data. Its horizontal scalability and high availability ensure that it can handle the demands of large-scale deployments and maintain performance as data volumes grow.

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.


M3 Key Concepts

  • Time Series Compression: M3 has the ability to compress time series data, resulting in significant memory and disk savings. It uses two compression algorithms, M3TSZ and protobuf encoding, to achieve efficient data compression.
  • Sharding: M3 uses virtual shards that are assigned to physical nodes. Timeseries keys are hashed to a fixed set of virtual shards, making horizontal scaling and node management seamless.
  • Consistency Levels: M3 provides variable consistency levels for read and write operations, as well as cluster connection operations. Write consistency levels include One (success of a single node), Majority (success of the majority of nodes), and All (success of all nodes). Read consistency level is One, which corresponds to reading from a single nod

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.


M3 Architecture

M3 is designed to be horizontally scalable and handle high data throughput. It uses fileset files as the primary unit of long-term storage, storing compressed streams of time series values. These files are flushed to disk after a block time window becomes unreachable. M3 has a commit log, equivalent to the commit log or write-ahead-log in other databases, which ensures data integrity. Client Peer streaming is responsible for fetching blocks from peers for bootstrapping purposes. M3 also implements caching policies to optimize efficient reads by determining which flushed blocks are kept in memory.

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.

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M3 Features

Commit Log

M3 uses a commit log to ensure data integrity, providing durability for write operations.

Peer Streaming

M3’s client peer streaming fetches data blocks from peers for bootstrapping purposes, optimizing data retrieval and distribution.

Caching Mechanisms

M3 implements various caching policies to efficiently manage memory usage, keeping frequently accessed data blocks in memory for faster reads.

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.


M3 Use Cases

Monitoring and Observability

M3 is particularly suitable for large-scale monitoring and observability tasks, as it can store and manage massive volumes of time-series data generated by infrastructure, applications, and microservices. Organizations can use M3 to analyze, visualize, and detect anomalies in the metrics collected from various sources, enabling them to identify potential issues and optimize their systems.

IoT and Sensor Data

M3 can be used to store and process the vast amounts of time-series data generated by IoT devices and sensors. By handling data from millions of devices and sensors, M3 can provide organizations with valuable insights into the performance, usage patterns, and potential issues of their connected devices. This information can be used for optimization, predictive maintenance, and improving the overall efficiency of IoT systems.

Financial Data Analysis

Financial organizations can use M3 to store and analyze time-series data related to stocks, bonds, commodities, and other financial instruments. By providing fast and efficient querying capabilities, M3 can help analysts and traders make more informed decisions based on historical trends, current market conditions, and potential future developments.

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.


M3 Pricing Model

M3 is an open source database and can be used freely, although you will have to account for the cost of managing your infrastructure and the hardware used to run M3. Chronosphere is the co-maintainer of M3 along with Uber and also offers a hosted observability that uses M3 as the backend storage layer.

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.