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

The primary purpose of this article is to compare how M3 and TDengine 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 TDengine Breakdown


 
Database Model

Time series database

Time series 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

TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management.

License

Apache 2.0

AGPL 3.0

Use Cases

Monitoring, observability, IoT, Real-time analytics, large-scale metrics processing

IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability

Scalability

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

Horizontally scalable with clustering and built-in load balancing. TDengine also provides decoupled compute and storage as well as object storage support for data tiering in some versions

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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.

TDengine Overview

TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL for querying, and flexible data modeling capabilities.


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.

TDengine for Time Series Data

TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.


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

TDengine Key Concepts

  • Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
  • Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
  • Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.


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.

TDengine Architecture

TDengine uses a cloud native architecture that combines the advantages of relational databases (support for SQL querying) and NoSQL databases (scalability and flexibility).

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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.

TDengine Features

Data ingestion

TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion.

Data querying

TDengine provides ANSI SQL support with additional that allows users to easily query time series data using familiar SQL syntax. It supports various aggregation functions, filtering, and joins.

Data retention and compression

TDengine automatically compresses data to save storage space and provides data retention policies to automatically delete old data.


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.

TDengine Use Cases

IoT data storage and analysis

TDengine is designed to handle massive amounts of time series data generated by IoT devices. Its high-performance ingestion, querying, and storage capabilities make it a suitable choice for IoT data storage and analysis.

Industrial IoT monitoring

TDengine can be used to store and analyze data from industrial IoT sensors and devices, helping organizations monitor equipment performance, detect anomalies, and optimize operations.

Infrastructure Monitoring

TDengine can be used to collect and analyze time series data from IT infrastructure components, such as servers, networks, and applications, facilitating real-time monitoring, alerting, and performance optimization.


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.

TDengine Pricing Model

TDengine is open source and free to use under the AGPLv3 license. TDengine also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.