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 Mimir so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how M3 and Mimir 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 Mimir Breakdown
Time series database
Time series database
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
Grafana Mimir is a time series database designed for high-performance, real-time monitoring, and analytics. It features a distributed architecture, allowing for horizontal scaling across multiple nodes to handle large volumes of data and queries. It can be deployed on-prem due to being open source or as a managed solution hosted by Grafana
Monitoring, observability, IoT, Real-time analytics, large-scale metrics processing
Monitoring, observability, IoT
Horizontally scalable, designed for high availability and large-scale deployments
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.
Grafana Mimir is an open-source software project that provides a scalable long-term storage solution for Prometheus. Started at Grafana Labs and announced in 2022, Grafana Mimir aims to become the most scalable and performant open-source time series database for metrics. The project incorporates the knowledge and experience gained by Grafana Labs engineers from running Grafana Enterprise Metrics and Grafana Cloud Metrics at massive scale.
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.
Mimir for Time Series Data
Grafana Mimir is well-suited for handling time series data, making it a suitable choice for scenarios involving metric storage and analysis. It provides long-term storage capabilities for Prometheus, a popular open-source monitoring and alerting system. With Grafana Mimir, users can store and query time series metrics over extended periods, allowing for historical analysis and trend detection. It is especially useful for applications that require scalable and performant storage of time series data for metrics monitoring and observability purposes.
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
Mimir Key Concepts
- Metrics: In Grafana Mimir, metrics represent the measurements or observations tracked over time. They can include various types of data, such as system metrics, application performance metrics, or sensor data.
- Long-term Storage: Grafana Mimir provides a storage solution specifically tailored for long-term retention of time series data, allowing users to store and query historical metrics over extended periods.
- Microservices: Grafana Mimir adopts a microservices-based architecture, where the system consists of multiple horizontally scalable microservices that can operate independently and in parallel.
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.
Grafana Mimir adopts a microservices-based architecture, where the system comprises multiple horizontally scalable microservices. These microservices can operate independently and in parallel, allowing for efficient distribution of workload and scalability. Grafana Mimir’s components are compiled into a single binary, providing a unified and cohesive system. The architecture is designed to be highly available and multi-tenant, enabling multiple users and applications to utilize the database concurrently. This distributed architecture ensures scalability and resilience in handling large-scale metric storage and retrieval scenarios.
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M3 uses a commit log to ensure data integrity, providing durability for write operations.
M3’s client peer streaming fetches data blocks from peers for bootstrapping purposes, optimizing data retrieval and distribution.
M3 implements various caching policies to efficiently manage memory usage, keeping frequently accessed data blocks in memory for faster reads.
Grafana Mimir is designed to scale horizontally, enabling the system to handle growing data volumes and increasing workloads. Its horizontally scalable microservices architecture allows for seamless expansion and improved performance.
Grafana Mimir provides high availability by ensuring redundancy and fault tolerance. It allows for replication and distribution of data across multiple nodes, ensuring data durability and continuous availability of stored metrics.
Grafana Mimir offers a dedicated solution for long-term storage of time series metrics. It provides efficient storage and retrieval mechanisms, allowing users to retain and analyze historical metric data over extended periods.
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.
Mimir Use Cases
Metrics Monitoring and Observability
Grafana Mimir is well-suited for monitoring and observability use cases. It enables the storage and analysis of time series metrics, allowing users to monitor the performance, health, and behavior of their systems and applications in real-time.
Long Term Metric Storage
With its focus on providing scalable long-term storage, Grafana Mimir is ideal for applications that require retaining and analyzing historical metric data over extended periods. It allows users to store and query large volumes of time series data generated by Prometheus.
Trend and anomaly detection
By using Mimir for storing long term historical data it can be useful for detecting trends in your metrics and also for comparing current metrics to historical data to detect outliers and anomalies
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.
Mimir Pricing Model
Grafana Mimir is an open-source project, which means it is freely available for usage and does not require any licensing fees. Users can download the source code and deploy Grafana Mimir on their own infrastructure without incurring direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
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