TDengine is a [high-performance](https://tdengine.com/fast), [scalable](https://tdengine.com/scalable) time series database with [SQL support](https://tdengine.com/sql-support). This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design and other topics. It’s written mainly for architects, developers and system administrators.
To get a global view about TDengine, like feature list, benchmarks, and competitive advantages, please browse through section [Introduction](./intro).
TDengine greatly improves the efficiency of data ingestion, querying and storage by exploiting the characteristics of time series data, introducing the novel concepts of "one table for one data collection point" and "super table", and designing an innovative storage engine. To understand the new concepts in TDengine and make full use of the features and capabilities of TDengine, please read [“Concepts”](./concept) thoroughly.
If you are a developer, please read the [“Developer Guide”](./develop) carefully. This section introduces the database connection, data modeling, data ingestion, query, continuous query, cache, data subscription, user-defined functions, and other functionality in detail. Sample code is provided for a variety of programming languages. In most cases, you can just copy and paste the sample code, make a few changes to accommodate your application, and it will work.
@@ -33,11 +33,11 @@ For more detail on features, please read through the whole documentation.
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.
-**[High Performance](https://tdengine.com/fast)**: 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.
-**Scalable**: TDengine provides out-of-box scalability and high-availability through its native distributed design. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
-**[Scalable](https://tdengine.com/scalable)**: TDengine provides out-of-box scalability and high-availability through its native distributed design. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
-**SQL Support**: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to handle time-series data better, and supporting convenient and flexible schemaless data ingestion.
-**[SQL Support](https://tdengine.com/sql-support)**: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to handle time-series data better, and supporting convenient and flexible schemaless data ingestion.
-**All in One**: TDengine has built-in caching, stream processing and data subscription functions. It is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software in some scenarios. It makes the system architecture much simpler, cost-effective and easier to maintain.
TDengine can be deployed in cluster mode to increase the processing capability and high availability. In cluster mode, any data can have multiple replications for the purpose of high availability and load balance. TDengine cluster can be scaled out easily to support more data collecting points and more data.
TDengine has a native distributed design and provides the ability to scale out. A few of nodes can form a TDengine cluster. If you need to get higher processing power, you just need to add more nodes into the cluster. TDengine uses virtual node technology to virtualize a node into multiple virtual nodes to achieve load balancing. At the same time, TDengine can group virtual nodes on different nodes into virtual node groups, and use the replication mechanism to ensure the high availability of the system. The cluster feature of TDengine is completely open source.
This chapter mainly introduces cluster deployment, maintenance, and how to achieve high availability and load balancing.
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This chapter is mainly written for system administrators, covering download, install/uninstall, data import/export, system monitoring, user management, connection management, etc. Capacity planning and system optimization are also covered.
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TDengine's support for standard SQL commands, common database connector standards (e.g., JDBC), ORM, and other popular time-series database writing protocols (e.g., InfluxDB Line Protocol, OpenTSDB JSON, OpenTSDB Telnet, etc.) makes TDengine very easy to use with third-party tools.
Since TDengine supports standard SQL commands, common database connector standards (e.g., JDBC), ORM, and other popular time-series database writing protocols (e.g., InfluxDB Line Protocol, OpenTSDB JSON, OpenTSDB Telnet, etc.), it is very easy to integrate TDengine with other third party tools. You only need to provide simple configuration, the integration can be done without a line of code.
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