@@ -11,27 +11,27 @@ This section introduces the major features, competitive advantages, suited scena
The major features are listed below:
1. Besides [using SQL to insert](/develop/insert-data/sql-writing)，supports [Schemaless writing](/reference/schemaless/)，and supports [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) and other protocols.
2. Support seamless integration with third-party data collection agent like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). Without a line of code, those agents can write data points into TDengine just by configuration.
3. Support [all kinds of queries](/query-data), including aggregation, nested query, downsampling, interpolation, etc.
4. Support [user defined functions](/develop/udf)
5. Support [caching](/develop/cache). TDengine always save the last data point in cache, so Redis is not needed in some scenarios.
6. Support [continuous query](/develop/continuous-query).
7. Support [data subscription](/develop/subscribe)，and the filter condition can be specified.
8. Support [cluster](/cluster/), so it can gain more processing power by adding more nodes. The high availability is supported by replication.
9. Provide interactive [command-line intrerface](/reference/taos-shell) for management, maintainence and ad-hoc query.
10. Provide many ways to [import](/operation/import),[export](/operation/export) data.
11. Provide [monitoring](/operation/monitor) on TDengine running instances.
12. Provide [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages.
13. Provide [REST API](/reference/rest-api/).
14. Support the seamless integration with [Grafana](/third-party/grafana) for visualization.
15. Support seamless integration with Google Data Studio.
For more detailed features, please read through the whole document.
1. Besides [using SQL to insert](/develop/insert-data/sql-writing)，it supports [Schemaless writing](/reference/schemaless/)，and it supports [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) and other protocols.
2. Support for seamless integration with third-party data collection agents like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). Without a line of code, those agents can write data points into TDengine just by configuration.
3. Support for [all kinds of queries](/query-data), including aggregation, nested query, downsampling, interpolation, etc.
4. Support for [user defined functions](/develop/udf)
5. Support for [caching](/develop/cache). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios.
6. Support for [continuous query](/develop/continuous-query).
7. Support for [data subscription](/develop/subscribe) with the capability to specify filter conditions.
8. Support for [cluster](/cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
9. Provides interactive [command-line intrerface](/reference/taos-shell) for management, maintainence and ad-hoc query.
10. Provides many ways to [import](/operation/import) and[export](/operation/export) data.
11. Provides[monitoring](/operation/monitor) on TDengine running instances.
12. Provides[connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages.
13. Provides a[REST API](/reference/rest-api/).
14. Supports the seamless integration with [Grafana](/third-party/grafana) for visualization.
15. Supports seamless integration with Google Data Studio.
For more detail on features, please read through the whole documentation.
## Competitive Advantages
TDengine makes full use of [the characteristics of time series data](https://tdengine.com/2019/07/09/86.html), such as structured, no transaction, rarely delete or update, etc., and builds its own innovative storage engine and computing engine to differentiate itself from other TSDBs with the following advantages.
TDengine makes full use of [the characteristics of time series data](https://tdengine.com/2019/07/09/86.html), such as structured, no transaction, rarely delete or update, etc., and builds its own innovative storage engine and computing engine to differentiate itself from other time series databases with the following advantages.
-**High Performance**: TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage cost and compute costs, with an innovatively designed and purpose-built storage engine.
...
...
@@ -45,11 +45,11 @@ TDengine makes full use of [the characteristics of time series data](https://tde
-**Zero Management**: Installation and cluster setup can be done in seconds. Data partitioning and sharding are executed automatically. TDengine’s running status can be monitored via Grafana or other DevOps tools.
-**Zero Learning Cost**: With SQL as the query language, support for ubiquitous tools like Python, Java, C/C++, Go, Rust, Node.js connectors, there is zero learning cost.
-**Zero Learning Costs**: With SQL as the query language and support for ubiquitous tools like Python, Java, C/C++, Go, Rust, and Node.js connectors, there are zero learning costs.
-**Interactive Console**: TDengine provides convenient console access to the database to run ad hoc queries, maintain the database, or manage the cluster without any programming.
With TDengine, the total cost of ownership of time-seriess data platform can be greatly reduced. Because 1: with its superior performance, the computing and storage resources are reduced significantly; 2：with SQL support, it can be seamlessly integrated with many third party tools, and learning cost/migration cost is reduced significantly; 3: with its simple architecture and zero management, the operation and maintainence cost is reduced.
With TDengine, the total cost of ownership of time-seriess data platform can be greatly reduced. Because 1: with its superior performance, the computing and storage resources are reduced significantly; 2：with SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly; 3: with its simple architecture and zero management, the operation and maintainence costs are reduced.
## TDengine Technical Ecosystem
In the time-series data processing platform, TDengine stands in a role like this diagram below:
...
...
@@ -58,16 +58,14 @@ In the time-series data processing platform, TDengine stands in a role like this
On the left side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right side, visualization/BI tools, HMI, Python/R, IoT App can be connected. TDengine itself provides interactive command-line interface and web interface for management and maintainence.
On the left side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides interactive command-line interface and web interface for management and maintainence.
## Suited Scenarios for TDengine
As a high-performance, scalable and SQL supported time-series database, TDengine's typical application scenarios include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial market and other fields. But you shall note that TDengine is a purpose-built database and does tons of optimization based on the characteristics of time series data, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM, etc. This section makes a more detailed analysis of the applicable scenarios.
As a high-performance, scalable and SQL supported time-series database, TDengine's typical application scenarios 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, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM, etc. This section makes a more detailed analysis of the applicable scenarios.
### Characteristics and Requirements of Data Sources
From the perspective of data sources, designers can analyze the applicability of TDengine in target application systems as follows.
| **Data Source Characteristics and Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| A massive amount of total data | | | √ | TDengine provides excellent scale-out functions in terms of capacity, and has a storage structure with matching high compression ratio to achieve the best storage efficiency in the industry.|
...
...
@@ -80,7 +78,7 @@ From the perspective of data sources, designers can analyze the applicability of
| A simple and reliable system architecture | | | √ | TDengine's system architecture is very simple and reliable, with its own message queue, cache, stream computing, monitoring and other functions. There is no need to integrate any additional third-party products. |
| Fault-tolerance and high-reliability | | | √ | TDengine has cluster functions to automatically provide high-reliability and high-availability functions such as fault tolerance and disaster recovery. |
| Standardization support | | | √ | TDengine supports standard SQL and also provides extensions specifically to analyze time-series data. |
| Standardization support | | | √ | TDengine supports standard SQL and provides SQL extensions for time-series data analysis. |
### System Function Requirements
...
...
@@ -95,7 +93,7 @@ From the perspective of data sources, designers can analyze the applicability of
| Very large total processing capacity | | | √ | TDengine’s cluster functions can easily improve processing capacity via multi-server coordination. |
| Extremely high-speed data processing | | | √ | TDengine’s storage and data processing are optimized for IoT, and can process data many times faster than similar products.|
| Extremely fast processing of fine-grained data | | | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
| Extremely fast processing of high resolution data | | | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
