AWS DynamoDB vs Mimir
A detailed comparison
Compare AWS DynamoDB and Mimir 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 AWS DynamoDB and Mimir so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how AWS DynamoDB 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.
AWS DynamoDB vs Mimir Breakdown
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| Database Model | Key-value and document store |
Time series database |
| Architecture | DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations |
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 |
| License | Closed source |
APGL 3.0 |
| Use Cases | Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing |
Monitoring, observability, IoT |
| Scalability | Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication |
Horizontally scalable |
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AWS DynamoDB Overview
Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.
Mimir Overview
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.
AWS DynamoDB for Time Series Data
DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.
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.
AWS DynamoDB Key Concepts
Some of the key terms and concepts specific to DynamoDB include:
- Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
- Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
- Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
- Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.
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.
AWS DynamoDB Architecture
DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:
- Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
- Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
- Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.
Mimir Architecture
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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AWS DynamoDB Features
Auto scaling
DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.
Backup and restore
DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.
Global tables
DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.
Streams
DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.
Mimir Features
Scalability
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.
High Availability
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.
Long-term Storage
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.
AWS DynamoDB Use Cases
Session management
DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.
Gaming
DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.
Internet of Things
DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.
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
AWS DynamoDB Pricing Model
DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.
On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.
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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