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

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

Kdb vs M3 Breakdown


 
Database Model

Time series and columnar database

Time series database

Architecture

Kdb can be deployed on-premises, in the cloud, or as a hybrid solution.

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

License

Closed source

Apache 2.0

Use Cases

High-frequency trading, financial services, market data analysis, IoT, real-time analytics

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

Scalability

Highly scalable with multi-threading and multi-node support, suitable for large-scale data processing

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

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.

Kdb Overview

kdb+ is a high-performance columnar, time series database developed by Kx Systems. Released in 2003, kdb+ is designed to efficiently manage large volumes of data, with a primary focus on financial data, such as stock market trades and quotes. It is built on the principles of the q programming language, which is a descendant of APL and K. The database is known for its speed, scalability, and ability to process both real-time and historical data.

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.


Kdb for Time Series Data

kdb+ is designed to store time series data, making it a natural fit for applications that require high-speed querying and analysis of large volumes of data. Its columnar storage format allows for efficient compression and retrieval of time series data, while its q language provides a powerful and expressive means to manipulate and analyze the data. kdb+ is especially strong for financial data, though it can be used for other types of time series data as well.

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.


Kdb Key Concepts

  • q language: A high-level, domain-specific programming language used for querying and manipulating data in kdb+. It combines SQL-like syntax with a functional programming style.
  • Columnar storage: kdb+ stores data in columns, rather than rows, which allows for faster querying and analysis of time series data.
  • Tables: kdb+ stores data in tables, which are similar to relational tables, but with a focus on columnar storage and time series data.
  • Splayed tables: A table storage format where each column is stored in a separate file, further enhancing 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


Kdb Architecture

kdb+ is a columnar, time series database that employs a custom data model tailored for efficient storage and querying of time series data. It does not use traditional SQL, but instead relies on the q language for querying and data manipulation. The architecture of kdb+ is designed for both in-memory and on-disk storage, with the ability to scale horizontally across multiple machines. The primary components of kdb+ are the database engine, the q language interpreter, and the built-in web server.

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.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

Kdb Features

High performance

kdb+ is known for its speed and performance, with its columnar storage format and q language allowing for rapid querying and analysis of time series data.

Scalability

kdb+ is designed to scale horizontally, making it suitable for handling large volumes of data across multiple machines.

q language

The q language is a powerful, expressive, and high-level language used for querying and manipulating data in kdb+. It combines SQL-like syntax with a functional programming style.

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.


Kdb Use Cases

Financial data analysis

kdb+ is widely used in the financial industry for the storage and analysis of stock market trades, quotes, and other time series financial data.

High-frequency trading

kdb+ is a popular choice for high-frequency trading applications due to its high performance and ability to handle large volumes of real-time data.

IoT and sensor data

kdb+ can be used to store and analyze large volumes of time series data generated by IoT devices and sensors, though its primary focus remains on financial 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.


Kdb Pricing Model

kdb+ is a commercial product, with pricing depending on the deployment model and the number of cores or servers used. Kx Systems offers a free 32-bit version of kdb+ for non-commercial use, with limitations on the amount of memory that can be used. For commercial deployments and full-featured versions, users must contact Kx Systems for pricing details.

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.