Amazon Timestream for LiveAnalytics vs TDengine
A detailed comparison
Compare Amazon Timestream for LiveAnalytics and TDengine 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 Amazon Timestream for LiveAnalytics and TDengine 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 TDengine 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 TDengine Breakdown
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 |
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| Database Model | Time series database |
Time series database |
| Architecture | Timestream is a fully managed, serverless time series database service that is only available on AWS. |
TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management. |
| License | Closed source |
AGPL 3.0 |
| Use Cases | IoT, DevOps, time series analytics |
IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability |
| Scalability | Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention |
Horizontally scalable with clustering and built-in load balancing. TDengine also provides decoupled compute and storage as well as object storage support for data tiering in some versions |
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Amazon Timestream for LiveAnalytics Overview
Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by 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 for LiveAnalytics easily integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud. AWS also offers Timestream for InfluxDB which is a managed version of InfluxDB that is compatible with InfluxDB 2.x APIs and released in partnership with InfluxData.
TDengine Overview
TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL for querying, and flexible data modeling capabilities.
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 and efficient storage. Its dual-tiered storage architecture, consisting of the memory Store and magnetic Store, allows users to 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.
TDengine for Time Series Data
TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.
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.
TDengine Key Concepts
- Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
- Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
- Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.
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.
TDengine Architecture
TDengine uses a cloud native architecture that combines the advantages of relational databases (support for SQL querying) and NoSQL databases (scalability and flexibility).
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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.
Timestream for InfluxDB
For workloads that require near real-time queries with single millisecond latency AWS recommends using Timestream for InfluxDB rather than LiveAnalytics. Timestream for InfluxDB also provides compatibility with InfluxDB APIs for users who want an AWS managed service without having to update their code.
TDengine Features
Data ingestion
TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion.
Data querying
TDengine provides ANSI SQL support with additional that allows users to easily query time series data using familiar SQL syntax. It supports various aggregation functions, filtering, and joins.
Data retention and compression
TDengine automatically compresses data to save storage space and provides data retention policies to automatically delete old data.
Amazon Timestream for LiveAnalytics Use Cases
IoT applications
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.
Devops
LiveAnalytics can be used for general DevOps workloads like monitoring application health and utilization. For use cases that require real time monitoring with the lowest latency possible, AWS recommends using Timestream for InfluxDB.
Analytics
Amazon Timestream for LiveAnalytics can be used to track analytics data like web and application data. The built-in time series analytics functions can then be used to aggregate and analyze data to get valuable insights with increased developer productivity.
TDengine Use Cases
IoT data storage and analysis
TDengine is designed to handle massive amounts of time series data generated by IoT devices. Its high-performance ingestion, querying, and storage capabilities make it a suitable choice for IoT data storage and analysis.
Industrial IoT monitoring
TDengine can be used to store and analyze data from industrial IoT sensors and devices, helping organizations monitor equipment performance, detect anomalies, and optimize operations.
Infrastructure Monitoring
TDengine can be used to collect and analyze time series data from IT infrastructure components, such as servers, networks, and applications, facilitating real-time monitoring, alerting, and performance optimization.
Amazon Timestream for LiveAnalytics Pricing Model
Amazon Timestream for LiveAnalytics 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.
TDengine Pricing Model
TDengine is open source and free to use under the AGPLv3 license. TDengine also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.
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