[](https://bestpractices.coreinfrastructure.org/projects/4201)
[](https://bestpractices.coreinfrastructure.org/projects/4201)
English | [简体中文](README-CN.md) | [Lean more about TSDB](https://tdengine.com/tsdb)
English | [简体中文](README-CN.md) | [Learn more about TSDB](https://tdengine.com/tsdb/)
# What is TDengine？
# What is TDengine？
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) 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. TDengine differentiates itself from other time-seires databases with the following advantages:
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) 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. TDengine differentiates itself from other time-seires databases with the following advantages:
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: 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.
-**[HighPerformance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: 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.
-**[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-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.
-**[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-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.
-**[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: 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.
-**[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: 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.
-**[Ease of Use](https://docs.tdengine.com/get-started/docker/)**: 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.
-**[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: 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.
-**[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: 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.
-**[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: 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.
...
@@ -232,9 +232,9 @@ After building successfully, TDengine can be installed by
...
@@ -232,9 +232,9 @@ After building successfully, TDengine can be installed by
sudo make install
sudo make install
```
```
Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
Users can find more information about directories installed on the system in the [directory and files](https://docs.tdengine.com/reference/directory/) section.
Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.taosdata.com/get-started/package/) for it.
Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.tdengine.com/get-started/package/) for it.
To start the service after installation, in a terminal, use:
To start the service after installation, in a terminal, use:
@@ -68,7 +68,7 @@ A query can be performed on some or all columns. Data and tag columns can all be
...
@@ -68,7 +68,7 @@ A query can be performed on some or all columns. Data and tag columns can all be
### Wildcards
### Wildcards
You can use an asterisk (\*) as a wildcard character to indicate all columns. For standard tables, the asterisk indicates only data columns. For supertables and subtables, tag columns are also included.
You can use an asterisk (\*) as a wildcard character to indicate all columns. For normal tables or sub-tables, the asterisk indicates only data columns. For supertables, tag columns are also included when using asterisk (\*).
res=driver.query(conn,"CREATE TABLE therm1 USING thermometer TAGS ('beijing', 1)")
res=driver.query(conn,"create table therm1 using thermometer tags ('beijing', 1)")
ifres.code~=0then
ifres.code~=0then
print(res.error)
print(res.error)
return
return
...
@@ -86,7 +134,7 @@ else
...
@@ -86,7 +134,7 @@ else
print("create table--- pass")
print("create table--- pass")
end
end
res=driver.query(conn,"INSERT INTO therm1 VALUES ('2019-09-01 00:00:00.001', 20),('2019-09-01 00:00:00.002', 21)")
res=driver.query(conn,"insert into therm1 values ('2019-09-01 00:00:00.001', 20),('2019-09-01 00:00:00.002', 21)")
ifres.code~=0then
ifres.code~=0then
print(res.error)
print(res.error)
...
@@ -99,14 +147,14 @@ else
...
@@ -99,14 +147,14 @@ else
end
end
end
end
res=driver.query(conn,"SELECT COUNT(*) count, AVG(degree) AS av, MAX(degree), MIN(degree) FROM thermometer WHERE location='beijing' or location='tianjin' GROUP BY location, type")
res=driver.query(conn,"select count(*) as cnt, avg(degree) as av, max(degree), min(degree) from thermometer where location='beijing' or location='tianjin' group by location, type")
ifres.code~=0then
ifres.code~=0then
print("select from super table--- failed:"..res.error)
print("select from super table--- failed:"..res.error)
res=driver.query(conn,"create stream stream_avg_degree into avg_degree as select avg(degree) from thermometer interval(5s) sliding(1s)")
functionstream_callback(t)
print("From now on we start continous insert in an definite loop, pls wait for about 10 seconds and check stream table for result.")
print("------------------------")
print("continuous query result:")
forkey,valueinpairs(t)do
print("key:"..key..", value:"..value)
end
end
localstream
res=driver.open_stream(conn,"SELECT COUNT(*) as count, AVG(degree) as avg, MAX(degree) as max, MIN(degree) as min FROM thermometer interval(2s) sliding(2s);)",0,stream_callback)
ifres.code~=0then
print("open stream--- failed:"..res.error)
return
else
print("open stream--- pass")
stream=res.stream
end
print("From now on we start continous insert in an definite (infinite if you want) loop.")
localloop_index=0
localloop_index=0
whileloop_index<30do
whileloop_index<10do
localt=os.time()*1000
localt=os.time()*1000
localv=loop_index
localv=math.random(20)
res=driver.query(conn,string.format("INSERT INTO therm1 VALUES (%d, %d)",t,v))
res=driver.query(conn,string.format("INSERT INTO therm1 VALUES (%d, %d)",t,v))
ifres.code~=0then
ifres.code~=0then
...
@@ -162,7 +193,5 @@ while loop_index < 30 do
...
@@ -162,7 +193,5 @@ while loop_index < 30 do
os.execute("sleep "..1)
os.execute("sleep "..1)
loop_index=loop_index+1
loop_index=loop_index+1
end
end
driver.query(conn,"DROP STREAM IF EXISTS avg_therm_s")
