88 lines
4.5 KiB
Markdown
88 lines
4.5 KiB
Markdown
#Documentation
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TDengine is a highly efficient platform to store, query, and analyze time-series data. It works like a relational database, but you are strongly suggested to read through the following documentation before you experience it.
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##Getting Started
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- Quick Start: download, install and experience TDengine in a few seconds
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- TDengine Shell: command-line interface to access TDengine server
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- Major Features: insert/query, aggregation, cache, pub/sub, continuous query
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## Data Model and Architecture
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- Data Model: relational database model, but one table for one device with static tags
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- Architecture: Management Module, Data Module, Client Module
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- Writing Process: records recieved are written to WAL, cache, then ack is sent back to client
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- Data Storage: records are sharded in the time range, and stored column by column
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##TAOS SQL
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- Data Types: support timestamp, int, float, double, binary, nchar, bool, and other types
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- Database Management: add, drop, check databases
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- Table Management: add, drop, check, alter tables
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- Inserting Records: insert one or more records into tables, historical records can be imported
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- Data Query: query data with time range and filter conditions, support limit/offset
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- SQL Functions: support aggregation, selector, transformation functions
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- Downsampling: aggregate data in successive time windows, support interpolation
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##STable: Super Table
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- What is a Super Table: an innovated way to aggregate tables
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- Create a STable: it is like creating a standard table, but with tags defined
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- Create a Table via STable: use STable as the template, with tags specified
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- Aggregate Tables via STable: group tables together by specifying the tags filter condition
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- Create Table Automatically: create tables automatically with a STable as a template
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- Management of STables: create/delete/alter super table just like standard tables
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- Management of Tags: add/delete/alter tags on super tables or tables
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##Advanced Features
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- Continuous Query: query executed by TDengine periodically with a sliding window
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- Publisher/Subscriber: subscribe to the newly arrived data like a typical messaging system
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- Caching: the newly arrived data of each device/table will always be cached
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##Connector
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- C/C++ Connector: primary method to connect to the server through libtaos client library
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- Java Connector: driver for connecting to the server from Java applications using the JDBC API
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- Python Connector: driver for connecting to the server from Python applications
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- RESTful Connector: a simple way to interact with TDengine via HTTP
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- Go Connector: driver for connecting to the server from Go applications
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- Node.js Connector: driver for connecting to the server from node applications
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##Connections with Other Tools
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- Telegraf: pass the collected DevOps metrics to TDengine
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- Grafana: query the data saved in TDengine and visualize them
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- Matlab: access TDengine server from Matlab via JDBC
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- R: access TDengine server from R via JDBC
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##Administrator
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- Directory and Files: files and directories related with TDengine
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- Configuration on Server: customize IP port, cache size, file block size and other settings
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- Configuration on Client: customize locale, default user and others
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- User Management: add/delete users, change passwords
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- Import Data: import data into TDengine from either script or CSV file
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- Export Data: export data either from TDengine shell or from tool taosdump
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- Management of Connections, Streams, Queries: check or kill the connections, queries
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- System Monitor: collect the system metric, and log important operations
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##More on System Architecture
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- Storage Design: column-based storage with optimization on time-series data
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- Query Design: an efficient way to query time-series data
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- Technical blogs to delve into the inside of TDengine
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## More on IoT Big Data
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- [Characteristics of IoT Big Data](https://www.taosdata.com/blog/2019/07/09/characteristics-of-iot-big-data/)
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- [Why don’t General Big Data Platforms Fit IoT Scenarios?](https://www.taosdata.com/blog/2019/07/09/why-does-the-general-big-data-platform-not-fit-iot-data-processing/)
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- [Why TDengine is the Best Choice for IoT Big Data Processing?](https://www.taosdata.com/blog/2019/07/09/why-tdengine-is-the-best-choice-for-iot-big-data-processing/)
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##Tutorials & FAQ
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- <a href='https://www.taosdata.com/en/faq'>FAQ</a>: a list of frequently asked questions and answers
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- <a href='https://www.taosdata.com/en/blog/?categories=4'>Use cases</a>: a few typical cases to explain how to use TDengine in IoT platform
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