diff --git a/docs-en/02-intro/index.md b/docs-en/02-intro/index.md index c3e86fcbee41aa847134958225b1b856354a2444..9f2f6b1b67b428bdf3df7f3ead00766989075d76 100644 --- a/docs-en/02-intro/index.md +++ b/docs-en/02-intro/index.md @@ -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. @@ -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: @@ -58,7 +58,7 @@ In the time-series data processing platform, TDengine stands in a role like this
Figure 1. TDengine Technical Ecosystem
-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 @@ -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) diff --git a/docs-en/04-concept/index.md b/docs-en/04-concept/index.md index f71674fc0ddc483c1c3371e56bdf17c39506f985..abc553ab6d90042cb2389ba0b71d3b5395dcebfd 100644 --- a/docs-en/04-concept/index.md +++ b/docs-en/04-concept/index.md @@ -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. ## Database