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 StarRocks 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 StarRocks 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 vs StarRocks Breakdown


 
Database Model

Time series database

Data warehouse

Architecture

Timestream is a fully managed, serverless time series database service that is only available on AWS.

StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements.

License

Closed source

Apache 2.0

Use Cases

Monitoring, observability, IoT, real-time analytics

Business intelligence, analytics, real-time data processing, large-scale data storage

Scalability

Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention

Horizontally scalable, with support for distributed storage and query processing

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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.

StarRocks Overview

StarRocks is an open source high-performance analytical data warehouse that enables real-time, multi-dimensional, and highly concurrent data analysis. It features an MPP (Massively Parallel Processing) architecture and is equipped with a fully vectorized execution engine and a columnar storage engine that supports real-time updates.


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.

StarRocks for Time Series Data

StarRocks is primarily focused on data warehousing workloads but can be used for time series data. StarRocks can be used for real time analytics and historical data analysis.


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.

StarRocks Key Concepts

  • MPP Architecture: StarRocks utilizes an MPP architecture, which enables parallel processing and distributed execution of queries, allowing for high-performance and scalability.
  • Vectorized Execution Engine: StarRocks employs a fully vectorized execution engine that leverages SIMD (Single Instruction, Multiple Data) instructions to process data in batches, resulting in optimized query performance.
  • Columnar Storage Engine: The columnar storage engine in StarRocks organizes data by column, which improves query performance by only accessing the necessary columns during query execution.
  • Cost-Based Optimizer (CBO): StarRocks includes a fully-customized cost-based optimizer that evaluates different query execution plans and selects the most efficient plan based on estimated costs.
  • Materialized View: StarRocks supports intelligent materialized views, which are precomputed summaries of data that accelerate query performance by providing faster access to aggregated data.


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.

StarRocks Architecture

StarRock’s architecture includes a fully vectorized execution engine and a columnar storage engine for efficient data processing and storage. It also incorporates features like a cost-based optimizer and materialized views for optimized query performance. StarRocks supports real-time and batch data ingestion from a variety of sources and enables direct analysis of data stored in data lakes without data migration

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Amazon Timestream for LiveAnalytics Features

Serverless architecture

Amazon Timestream for LiveAnalytics serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.

Dual-tiered storage

Timestream’s dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, automatically manages data retention and optimizes storage costs based on data age and access patterns.

SQL-like querying

Amazon Timestream for LiveAnalytics supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

StarRocks Features

Multi-Dimensional Analysis

StarRocks supports multi-dimensional analysis, enabling users to explore data from different dimensions and perspectives.

High Concurrency

StarRocks is designed to handle high levels of concurrency, allowing multiple users to execute queries simultaneously.

Materialized View

StarRocks supports materialized views, which provide precomputed summaries of data for faster query performance.


Amazon Timestream for LiveAnalytics Use Cases

IoT device monitoring

Amazon Timestream for LiveAnalytic’s support for high ingestion rates and efficient storage makes it an ideal choice for monitoring and analyzing data from IoT devices, such as sensors and smart appliances.

Application performance monitoring

Timestream’s fast querying capabilities and ability to handle large volumes of time series data make it suitable for application performance monitoring, allowing users to track and analyze key performance indicators in real-time and identify bottlenecks or issues.

Infrastructure monitoring

Amazon Timestream for LiveAnalytics can be used to monitor and analyze infrastructure metrics, such as CPU utilization, memory usage, and network traffic, enabling organizations to optimize resource utilization, identify potential issues, and maintain a high level of performance for their critical systems.

StarRocks Use Cases

Real-Time Analytics

StarRocks is well-suited for real-time analytics scenarios, where users need to analyze data as it arrives, enabling them to make timely and data-driven decisions.

Ad-Hoc Queries

With its high-performance and highly concurrent data analysis capabilities, StarRocks is ideal for ad-hoc querying, allowing users to explore and analyze data interactively.

Data Lake Analytics

StarRocks supports analyzing data directly from data lakes without the need for data migration. This makes it a valuable tool for organizations leveraging data lakes for storage and analysis.


Amazon Timestream for LiveAnalytics Pricing Model

Amazon Timestream for LiveAnalyticsv offers a pay-as-you-go pricing model based on data ingestion, storage, and query execution. Ingestion costs are determined by the volume of data ingested into Timestream, while storage costs are based on the amount of data stored in the Memory Store and Magnetic Store. Query execution costs are calculated based on the amount of data scanned and processed during query execution. Timestream also offers a free tier for users to explore the service and build proof-of-concept applications without incurring costs.

StarRocks Pricing Model

StarRocks can be deployed on your own hardware using the open source project. There are also a number of commercial vendors offering managed services to run StarRocks in the cloud.