@@ -19,7 +19,7 @@ The major features are listed below:
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.
9. Provides interactive [command-line interface](/reference/taos-shell) for management, maintenance 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.
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@@ -49,7 +49,7 @@ TDengine makes full use of [the characteristics of time series data](https://tde
-**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 costs/migration costs are reduced significantly; 3: with its simple architecture and zero management, the operation and maintainence costs are reduced.
With TDengine, the total cost of ownership of time-series 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 maintenance costs are reduced.
## Technical Ecosystem
In the time-series data processing platform, TDengine stands in a role like this diagram below:
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@@ -58,7 +58,7 @@ 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, and IoT Apps 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 maintenance.
## Suited Scenarios
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@@ -103,7 +103,7 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
| Minimize learning and maintenance costs | | | √ | In addition to being easily configurable, standard SQL support and the Taos shell for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs.|
| Abundant talent supply | √ | | | Given the above, and given the extensive training and professional services provided by TDengine, it is easy to migrate from existing solutions or create a new and lasting solution based on TDengine.|
## Comparision with other databases
## Comparison with other databases
-[Writing Performance Comparison of TDengine and InfluxDB ](https://tdengine.com/2022/02/23/4975.html)
-[Query Performance Comparison of TDengine and InfluxDB](https://tdengine.com/2022/02/24/5120.html)
@@ -153,7 +153,7 @@ The relationship between a STable and the subtables created based on this STable
Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which can greatly reduce the data sets to be scanned, thus greatly improving the performance of data aggregation across multiple DCPs.
In TDengine, it is recommended to use a substable instead of a regular table for a DCP.
In TDengine, it is recommended to use a subtable instead of a regular table for a DCP.
