Amazon Timestream for LiveAnalytics vs Prometheus

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 Amazon Timestream for LiveAnalytics and Prometheus so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Amazon Timestream for LiveAnalytics and Prometheus 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.

Amazon Timestream for LiveAnalytics Overview

Amazon Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by Amazon Web Services (AWS). Launched in 2020, Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it an ideal choice for IoT, monitoring, and analytics applications that require high ingestion rates, efficient storage, and fast querying capabilities. As a part of the AWS ecosystem, Timestream seamlessly integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud.

Prometheus Overview

Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.

Amazon Timestream for LiveAnalytics for Time Series Data

Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it a suitable choice for a wide range of applications that require high ingestion rates, efficient storage, and fast querying capabilities. Its dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, allows Timestream to automatically manage data retention and optimize storage costs based on data age and access patterns. Additionally, Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

Prometheus for Time Series Data

Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.

Amazon Timestream for LiveAnalytics Key Concepts

• Memory Store: In Amazon Timestream for LiveAnalytics, the Memory Store is a component that stores recent, mutable time series data in memory for fast querying and analysis.
• Magnetic Store: The Magnetic Store in Amazon Timestream for LiveAnalytics is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
• Time-to-Live (TTL): Amazon Timestream for LiveAnalytics allows users to set a TTL on their time series data, which determines how long data is retained in the Memory Store before being moved to the Magnetic Store or deleted.

Prometheus Key Concepts

• Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
• Time Series: A collection of data points for a metric, indexed by timestamp.
• Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
• PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.

Amazon Timestream for LiveAnalytics Architecture

Amazon Timestream for LiveAnalytics is built on a serverless, distributed architecture that supports SQL-like querying capabilities. Its data model is specifically tailored for time series data, using time-stamped records and a flexible schema that can accommodate varying data granularities and dimensions. The core components of Timestream’s architecture include the Memory Store and the Magnetic Store, which together manage data retention, storage, and querying. The Memory Store is optimized for fast querying of recent data, while the Magnetic Store provides cost-efficient, long-term storage for historical data.

Prometheus Architecture

Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.