M3 vs Snowflake
A detailed comparison
Compare M3 and Snowflake 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 M3 and Snowflake so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how M3 and Snowflake 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 Snowflake Breakdown
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| Database Model | Time series database |
Cloud data warehouse |
| 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 |
Snowflake can be deployed across multiple cloud providers, including AWS, Azure, and Google Cloud |
| License | Apache 2.0 |
Closed source |
| Use Cases | Monitoring, observability, IoT, Real-time analytics, large-scale metrics processing |
Big data analytics, Data warehousing, Data engineering, Data sharing, Machine learning |
| Scalability | Horizontally scalable, designed for high availability and large-scale deployments |
Highly scalable with multi-cluster shared data architecture, automatic scaling, and performance isolation |
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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.
Snowflake Overview
Snowflake is a cloud-based data warehousing platform that was founded in 2012 and officially launched in 2014. It is designed to enable organizations to efficiently store, process, and analyze large volumes of structured and semi-structured data. Snowflake’s unique architecture separates storage, compute, and cloud services, allowing users to independently scale and optimize each component.
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.
Snowflake for Time Series Data
While Snowflake is not specifically designed for time series data, it can still effectively store, process, and analyze such data due to its scalable and flexible architecture. Snowflake’s columnar storage format, combined with its powerful query engine and support for SQL, makes it a suitable option for time series data analysis.
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
Snowflake Key Concepts
- Virtual Warehouse: A compute resource in Snowflake that processes queries and performs data loading and unloading. Virtual Warehouses can be independently scaled up or down based on demand.
- Micro-Partition: A storage unit in Snowflake that contains a subset of the data in a table. Micro-partitions are automatically optimized for efficient querying.
- Time Travel: A feature in Snowflake that allows users to query historical data at specific points in time or within a specific time range.
- Data Sharing: The ability to securely share data between Snowflake accounts, without the need to copy or transfer the data.
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.
Snowflake Architecture
Snowflake’s architecture separates storage, compute, and cloud services, allowing users to scale and optimize each component independently. The platform uses a columnar storage format and supports ANSI SQL for querying and data manipulation. Snowflake is built on top of AWS, Azure, and GCP, providing a fully managed, elastic, and secure data warehouse solution. Key components of the Snowflake architecture include databases, tables, virtual warehouses, and micro-partitions.
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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.
Snowflake Features
Elasticity
Snowflake’s architecture allows for independent scaling of storage and compute resources, enabling users to quickly adjust to changing workloads and demands.
Fully Managed
Snowflake is a fully managed service, eliminating the need for users to manage infrastructure, software updates, or backups.
Security
Snowflake provides comprehensive security features, including encryption at rest and in transit, multi-factor authentication, and fine-grained access control.
Data Sharing
Snowflake enables secure data sharing between accounts without the need to copy or transfer 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.
Snowflake Use Cases
Data Warehousing
Snowflake provides a scalable, secure, and fully managed data warehousing solution, making it suitable for organizations that need to store, process, and analyze large volumes of structured and semi-structured data.
Data Lake
Snowflake can serve as a data lake for ingesting and storing large volumes of raw, unprocessed data, which can be later transformed and analyzed as needed.
Data Integration and ETL
Snowflake’s support for SQL and various data loading and unloading options makes it a good choice for data integration and ETL
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.
Snowflake Pricing Model
Snowflake offers a pay-as-you-go pricing model, with separate charges for storage and compute resources. Storage is billed on a per-terabyte, per-month basis, while compute resources are billed based on usage, measured in Snowflake Credits. Snowflake offers various editions, including Standard, Enterprise, Business Critical, and Virtual Private Snowflake, each with different features and pricing options. Users can also opt for on-demand or pre-purchased, discounted Snowflake Credits.
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