Documentation

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.

• Quick Start: download, install and experience TDengine in a few seconds
• TDengine Shell: command-line interface to access TDengine server
• Major Features: insert/query, aggregation, cache, pub/sub, continuous query

• Data Model: relational database model, but one table for one device with static tags
• Architecture: Management Module, Data Module, Client Module
• Writing Process: records recieved are written to WAL, cache, then ack is sent back to client
• Data Storage: records are sharded in the time range, and stored column by column

• Data Types: support timestamp, int, float, double, binary, nchar, bool, and other types
• Database Management: add, drop, check databases
• Table Management: add, drop, check, alter tables
• Inserting Records: insert one or more records into tables, historical records can be imported
• Data Query: query data with time range and filter conditions, support limit/offset
• SQL Functions: support aggregation, selector, transformation functions
• Downsampling: aggregate data in successive time windows, support interpolation

• What is a Super Table: an innovated way to aggregate tables
• Create a STable: it is like creating a standard table, but with tags defined
• Create a Table via STable: use STable as the template, with tags specified
• Aggregate Tables via STable: group tables together by specifying the tags filter condition
• Create Table Automatically: create tables automatically with a STable as a template
• Management of STables: create/delete/alter super table just like standard tables
• Management of Tags: add/delete/alter tags on super tables or tables

• Continuous Query: query executed by TDengine periodically with a sliding window
• Publisher/Subscriber: subscribe to the newly arrived data like a typical messaging system
• Caching: the newly arrived data of each device/table will always be cached

• C/C++ Connector: primary method to connect to the server through libtaos client library
• Java Connector: driver for connecting to the server from Java applications using the JDBC API
• Python Connector: driver for connecting to the server from Python applications
• RESTful Connector: a simple way to interact with TDengine via HTTP
• Go Connector: driver for connecting to the server from Go applications
• Node.js Connector: driver for connecting to the server from node applications

• Telegraf: pass the collected DevOps metrics to TDengine
• Grafana: query the data saved in TDengine and visualize them
• Matlab: access TDengine server from Matlab via JDBC
• R: access TDengine server from R via JDBC

• Directory and Files: files and directories related with TDengine
• Configuration on Server: customize IP port, cache size, file block size and other settings
• Configuration on Client: customize locale, default user and others
• User Management: add/delete users, change passwords
• Import Data: import data into TDengine from either script or CSV file
• Export Data: export data either from TDengine shell or from tool taosdump
• Management of Connections, Streams, Queries: check or kill the connections, queries
• System Monitor: collect the system metric, and log important operations

• Storage Design: column-based storage with optimization on time-series data
• Query Design: an efficient way to query time-series data
• Technical blogs to delve into the inside of TDengine

• FAQ: a list of frequently asked questions and answers
• Use cases: a few typical cases to explain how to use TDengine in IoT platform