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.
TDengine is an open source, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. 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 an overview of TDengine, such as a feature list, benchmarks, and competitive advantages, please browse through the [Introduction](./intro) section.
To get an overview of TDengine, such as a feature list, benchmarks, and competitive advantages, please browse through the [Introduction](./intro) section.
TDengine is a high-performance, scalable time-series database with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/continuous-query), [data subscription](/develop/subscribe) and other functionalities to reduce the complexity and cost of development and operation.
TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/continuous-query), [data subscription](/develop/subscribe) and other functionalities to reduce the system complexity and cost of development and operation.
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, typical use-cases and benchmarks to help you get a high level overview of TDengine.
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## Competitive Advantages
## Competitive Advantages
Time-series data is structured, not transactional, and is rarely deleted or updated. TDengine makes full use of [these characteristics of time series data](https://tdengine.com/2019/07/09/86.html) to build its own innovative storage engine and computing engine to differentiate itself from other time series databases, with the following advantages.
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other time series databases, with the following advantages.
-**[High Performance](https://tdengine.com/fast)**: With an innovatively designed and purpose-built storage engine, TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage costs and compute costs.
-**High-Performance**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
-**[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.
-**Simplified 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.
-**[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 better handle time-series. Keeping NoSQL developers in mind, TDengine also supports convenient and flexible, schemaless data ingestion.
-**Cloud Native**: 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.
-**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.
-**Ease of Use**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
-**Seamless Integration**: Without a single line of code, TDengine provide seamless, configurable integration with third-party tools such as Telegraf, Grafana, EMQX, Prometheus, StatsD, collectd, etc. More third-party tools are being integrated.
-**Easy Data Analytics**: 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.
-**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.
-**Open Source**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub. There is an active developer community, and over 139k running instances worldwide.
-**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, and a REST API, there are zero learning costs.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 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 simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
-**Interactive Console**: TDengine provides convenient console access to the database, through a CLI, to run ad hoc queries, maintain the database, or manage the cluster, without any programming.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 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
## Technical Ecosystem
This is how TDengine would be situated, in a typical time-series data processing platform:
This is how TDengine would be situated, in a typical time-series data processing platform:
In order to explain the basic concepts and provide some sample code, the TDengine documentation smart meters as a typical time series use case. We assume the following: 1. Each smart meter collects three metrics i.e. current, voltage, and phase 2. There are multiple smart meters, and 3. Each meter has static attributes like location and group ID. Based on this, collected data will look similar to the following table:
In order to explain the basic concepts and provide some sample code, the TDengine documentation smart meters as a typical time series use case. We assume the following: 1. Each smart meter collects three metrics i.e. current, voltage, and phase; 2. There are multiple smart meters; 3. Each meter has static attributes like location and group ID. Based on this, collected data will look similar to the following table:
The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the STable (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details.
The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the [STable](/concept/#super-table-stable) (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details.
Note: before you read this chapter, please make sure you have already read through [Key Concepts](/concept/), since TDengine introduces new concepts like "one table for one [data collection point](/concept/#data-collection-point)" and "[super table](/concept/#super-table-stable)".
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The characteristics of time-series data from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the size of the cache, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. An example is shown as follows:
The characteristics of time-series data from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the size of the cache, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. An example is shown as follows:
In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2.
In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current from meters whose groupId is 2.
```
```
taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now - 24h;
taos> SELECT count(*), max(current) FROM meters where groupId = 2;
cunt(*) | max(current) |
count(*) | max(current) |
==================================
==================================
5 | 13.4 |
5 | 13.4 |
Query OK, 1 row(s) in set (0.002136s)
Query OK, 1 row(s) in set (0.002136s)
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@@ -81,40 +81,41 @@ In [Select](../../taos-sql././select), all query operations are marked as to whe
In IoT use cases, down sampling is widely used to aggregate data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001.
In IoT use cases, down sampling is widely used to aggregate data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001.
```
```
taos> SELECT sum(current) FROM d1001 INTERVAL(10s);
taos> SELECT _wstart, sum(current) FROM d1001 INTERVAL(10s);
Down sampling also supports time offset. For example, the below SQL statement can be used to get the sum of current from all meters but each time window must start at the boundary of 500 milliseconds.
Down sampling also supports time offset. For example, the below SQL statement can be used to get the sum of current from all meters but each time window must start at the boundary of 500 milliseconds.
```
```
taos> SELECT SUM(current) FROM meters INTERVAL(1s, 500a);
taos> SELECT _wstart, SUM(current) FROM meters INTERVAL(1s, 500a);
In many use cases, it's hard to align the timestamp of the data collected by each collection point. However, a lot of algorithms like FFT require the data to be aligned with same time interval and application programs have to handle this by themselves. In TDengine, it's easy to achieve the alignment using down sampling.
In many use cases, it's hard to align the timestamp of the data collected by each collection point. However, a lot of algorithms like FFT require the data to be aligned with same time interval and application programs have to handle this by themselves. In TDengine, it's easy to achieve the alignment using down sampling.
