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TDengine

What is TDengine？

TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.

  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.

  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.

  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..

  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.

  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.

  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.

Documentation

For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation for details.

Building

At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.

To build TDengine, use CMake 2.8 or higher versions in the project directory:

mkdir build &&cd build
cmake .. && cmake --build .

Running

To start the TDengine server, run the command below in terminal:

./build/bin/taosd -c test/cfg

In another terminal, use the TDengine shell to connect the server:

./build/bin/taos -c test/cfg

Installing

After building successfully, TDengine can be installed by:

make install

Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine. Users can also choose to install from packages for it.

Start the service in the terminal.

taosd

Then users can use the TDengine shell to connect the TDengine server.

taos

If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.

Try TDengine

It is easy to run SQL commands in the terminal which is the same as other SQL databases.

createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;

Developing with TDengine

TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.

Contribute TDengine

Please follow the contribution guidelines to contribute to the project.

About

An open-source big data platform designed and optimized for the Internet of Things (IoT).

www.taosdata.com

Resources

Readme
AGPL-3.0 license

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
TDengine
What is TDengine？
TDengine is an open-sourced big data platform under GNU AGPL v3.0, designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
  • 10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
  • 1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
  • Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust..
  • Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
  • Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
  • Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
Documentation
For user manual, system design and architecture, engineering blogs, refer to TDengine Documentation
for details.
Building
At the moment, TDengine only supports building and running on Linux systems. You can choose to install from packages or from the source code. This quick guide is for installation from the source only.
To build TDengine, use CMake 2.8 or higher versions in the project directory:
mkdir build &&cd build
cmake .. && cmake --build .
Running
To start the TDengine server, run the command below in terminal:
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine shell to connect the server:
./build/bin/taos -c test/cfg

Installing
After building successfully, TDengine can be installed by:
make install
Users can find more information about directories installed on the system in the directory and files section. It should be noted that installing from source code does not configure service management for TDengine.
Users can also choose to install from packages for it.
Start the service in the terminal.
taosd
Then users can use the TDengine shell to connect the TDengine server.
taos
If the terminal connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Try TDengine
It is easy to run SQL commands in the terminal which is the same as other SQL databases.
createdatabasedb;
use db;
createtablet (ts timestamp, a int);
insert into t values ('2019-07-15 00:00:00', 1);
insert into t values ('2019-07-15 01:00:00', 2);
select*from t;
dropdatabase db;
Developing with TDengine
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors.
Contribute TDengine
Please follow the contribution guidelines to contribute to the project.
About
An open-source big data platform designed and optimized for the Internet of Things (IoT).
www.taosdata.com
Resources
Readme
AGPL-3.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages