From 04da151fe130ddd40124ea169820f4b82435a765 Mon Sep 17 00:00:00 2001 From: Jeff Tao Date: Sat, 3 Sep 2022 17:26:07 +0800 Subject: [PATCH] Update 02-intro.md --- docs/en/02-intro.md | 79 +++++++++++++++++++++++++++++++++------------ 1 file changed, 58 insertions(+), 21 deletions(-) diff --git a/docs/en/02-intro.md b/docs/en/02-intro.md index ec2bc11699..054fad1f2b 100644 --- a/docs/en/02-intro.md +++ b/docs/en/02-intro.md @@ -5,40 +5,77 @@ title: Introduction to TDengine Cloud Service TDengine Cloud, is the fast, elastic, serverless and cost effective time-series data processing service based on the popular open source time-series database, TDengine. With TDengine Cloud you get the highly optimized and purpose-built for IoT time-series platform, for which TDengine is known. -This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine. +This section introduces the major features, competitive advantages and typical use-cases to help you get a high level overview of TDengine cloud service. ## Major Features The major features are listed below: -1. While TDengine supports [using SQL to insert](../data-in/insert-data), it also supports [Schemaless writing](/reference/schemaless/) just like NoSQL databases. TDengine also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others. -2. TDengine supports seamless integration with third-party tools like [Telegraf](../data-in/telegraf)，[Prometheus](../data-in/prometheus)，they can write data into TDengine with simple configuration and without a single line of code. -3. Support for [time series specific queries](../taos-sql/distinguished), including aggregation, nested query, downsampling, interpolation and others. -4. Support for [user defined functions](../taos-sql/udf). -5. Support for [caching](../taos-sql/database). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios. -6. Support for [stream processing](../taos-sql/stream). -7. Support for [data subscription](../taos-sql/tmq) with the capability to specify filter conditions. -8. High availability is supported by replication including multi-cloud replication. -9. Provides an interactive [command-line interface](../tools/cli) for management, maintenance and ad-hoc queries. -10. Provides many ways to [get data in](../data-in) and [get data out](../data-out) data. -11. Provides a Dashboard to monitor your running instances of TDengine. -12. Provides [connectors](../programming/connector/) for [Java](../programming/connector/java), [Python](../programming/connector/python), [Go](../programming/connector/go), [Rust](../programming/connector/rust), and [Node.js](../programming/connector/node). -13. Provides a [REST API](../programming/connect/rest-api/). -14. Supports seamless integration with [Grafana](../visual/grafana) for visualization. -15. Supports seamless integration with Google Data Studio. - -For more details on features, please read through the entire documentation. +1. Data In + - Supports [using SQL to insert](../data-in/insert-data). + - Supports [Telegraf](../data-in/telegraf/). + - Supports [Prometheus](../data-in/prometheus/). +2. Data Out + - Supports standard [SQL](../data-out/query-data/), including nested query. + - Supports exporting data via tool [taosDump](../data-out/taosdump/). + - Supports writing data to [Prometheus](../data-out/prometheus/). + - Supports exporting data via [data subscription](../tmq/). +3. Data Explorer: browse through databases and even run SQL queryies once you login. +4. Visualization: + - Supports [Grafana](../visual/grafana/) + - Supports Google data studio (to be released soon) + - Supports Grafana cloud (to be released soon) +6. [Stream Processing](../stream/): Not only is the continuous query is supported, but TDengine also supports event driven stream processing, so Flink or Spark is not needed for time-series data processing. +7. [Data Subscription](../tmq/): Application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions. +8. Enterprise + - Supports backuping data everyday. + - Supports replicating a database to another region or cloud. + - Supports VPC peering. + - Supports Allowed IP list for security. +9. Tools + - Provides an interactive [Command-line Interface (CLI)](../tools/cli/) for management and ad-hoc queries. + - Provides a tool [taosBenchmark](../tools/taosbenchmark/) for testing the performance of TDengine. +10. Programming + - Provides [connectors](../programming/connector/) for Java, Python, Go, Rust, Node.js and other programming languages. + - Provides a [REST API](../programming/connector/rest-api/). + +For more details on features, please read through the entire documentation. ## Competitive Advantages -By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine Cloud differentiates itself from other time series platforms, with the following advantages. +By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/) and its cloud native design, TDengine Cloud differentiates itself from other time series data cloud services, with the following advantages. -- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine Cloud is a fast, elastic, serverless purpose built platform for IoT time-series data. It is the only time-series platform to solve the high cardinality issue to support billions of data collection points while outperforming other time-series platforms for data ingestion, querying and data compression. +- **Worry Free**: TDengine Cloud is a fast, elastic, serverless purpose built cloud platform for time-series data. It provides worry-free operations with a fully managed cloud service. You pay as you go. - **[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly. -- **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds. It is Enterprise ready with backup, multi-cloud replication, VPC peering and IP whitelisting. +- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: It is the only time-series platform to solve the high cardinality issue to support billions of data collection points while outperforming other time-series platforms for data ingestion, querying and data compression. - **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine Cloud provides worry-free operations with a fully managed cloud native solution. For developers, it provides a simple interface, simplified solution and seamless integration with third party tools. For data users, it provides SQL support with powerful time series extensions built for data analytics. - **[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way. + +- **Enterprise Ready** It supports backup, multi-cloud/multi-region database replication, VPC peering and IP whitelisting. + +With TDengine cloud, the **total cost of ownership of your time-series data platform can be greatly reduced**. + +1. With its built-in caching, stream processing and data subscription, system complexity and operation cost are highly reduced. +2. With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly. +3. With the elastic, serverless and fully managed service, the operation and maintenance costs are reduced significantly. + +## Technical Ecosystem + +This is how TDengine would be situated, in a typical time-series data processing platform: + +
+ +![TDengine Database Technical Ecosystem ](eco_system.webp) + +
Figure 1. TDengine Technical Ecosystem
+
+ +On the left-hand side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right-hand side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides an interactive command-line interface and a web interface for management and maintenance. + +## Typical Use Cases + +As a high-performance and cloud native time-series database, TDengine's typical use case include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial markets and other fields. TDengine is a purpose-built database optimized for the characteristics of time series data. As such, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM and so on. More generally TDengine is not a suitable storage engine for non-time-series data. -- GitLab