diff --git a/README-CN.md b/README-CN.md index e30e38ae7875251df6fe1b5ab487f508ad6eeaa7..0b7e42d4fa19045e94f004ab61159a8d79e4bb82 100644 --- a/README-CN.md +++ b/README-CN.md @@ -303,14 +303,14 @@ Query OK, 2 row(s) in set (0.001700s) TDengine 提供了丰富的应用程序开发接口，其中包括 C/C++、Java、Python、Go、Node.js、C# 、RESTful 等，便于用户快速开发应用： -- [Java](https://docs.taosdata.com/reference/connector/java/) -- [C/C++](https://www.taosdata.com/cn/documentation/connector#c-cpp) -- [Python](https://docs.taosdata.com/reference/connector/python/) -- [Go](https://docs.taosdata.com/reference/connector/go/) -- [Node.js](https://docs.taosdata.com/reference/connector/node/) -- [Rust](https://docs.taosdata.com/reference/connector/rust/) -- [C#](https://docs.taosdata.com/reference/connector/csharp/) -- [RESTful API](https://docs.taosdata.com/reference/rest-api/) +- [Java](https://docs.taosdata.com/connector/java/) +- [C/C++](https://docs.taosdata.com/connector/cpp/) +- [Python](https://docs.taosdata.com/connector/python/) +- [Go](https://docs.taosdata.com/connector/go/) +- [Node.js](https://docs.taosdata.com/connector/node/) +- [Rust](https://docs.taosdata.com/connector/rust/) +- [C#](https://docs.taosdata.com/connector/csharp/) +- [RESTful API](https://docs.taosdata.com/connector/rest-api/) # 成为社区贡献者 diff --git a/README.md b/README.md index 02dd9984e81d7e4b0c8c6ed6ff4a57da4eba1de3..611d97aac9436bdcc732efcf98822f2dd11d74ab 100644 --- a/README.md +++ b/README.md @@ -19,29 +19,29 @@ English | [简体中文](README-CN.md) | We are hiring, check [here](https://tde # What is TDengine？ -TDengine is an open source, high-performance, 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. 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/what-is-a-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. TDengine differentiates itself from other time-seires databases with the following advantages: -- **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. +- **[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. -- **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. +- **[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**: 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**: 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://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. -- **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. +- **[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. -- **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. +- **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: 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. # Documentation -For user manual, system design and architecture, please refer to [TDengine Documentation](https://docs.taosdata.com) ([TDengine 文档](https://docs.taosdata.com)) +For user manual, system design and architecture, please refer to [TDengine Documentation](https://docs.tdengine.com) ([TDengine 文档](https://docs.taosdata.com)) # Building -At the moment, TDengine server supports running on Linux, Windows systems.Any OS application can also choose the RESTful interface of taosAdapter to connect the taosd service . TDengine supports X64/ARM64 CPU , and it will support MIPS64, Alpha64, ARM32, RISC-V and other CPU architectures in the future. +At the moment, TDengine server supports running on Linux and Windows systems. Any application can also choose the RESTful interface provided by taosAdapter to connect the taosd service . TDengine supports X64/ARM64 CPU, and it will support MIPS64, Alpha64, ARM32, RISC-V and other CPU architectures in the future. -You can choose to install through source code according to your needs, [container](https://docs.taosdata.com/get-started/docker/), [installation package](https://docs.taosdata.com/get-started/package/) or [Kubernetes](https://docs.taosdata.com/deployment/k8s/) to install. This quick guide only applies to installing from source. +You can choose to install through source code, [container](https://docs.tdengine.com/get-started/docker/), [installation package](https://docs.tdengine.com/get-started/package/) or [Kubernetes](https://docs.tdengine.com/deployment/k8s/). This quick guide only applies to installing from source. TDengine provide a few useful tools such as taosBenchmark (was named taosdemo) and taosdump. They were part of TDengine. By default, TDengine compiling does not include taosTools. You can use `cmake .. -DBUILD_TOOLS=true` to make them be compiled with TDengine. @@ -256,6 +256,7 @@ After building successfully, TDengine can be installed by: nmake install ``` + ## Quick Run @@ -304,14 +306,14 @@ Query OK, 2 row(s) in set (0.001700s) TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors and relevant documentation. -- [Java](https://docs.taosdata.com/reference/connector/java/) -- [C/C++](https://docs.taosdata.com/reference/connector/cpp/) -- [Python](https://docs.taosdata.com/reference/connector/python/) -- [Go](https://docs.taosdata.com/reference/connector/go/) -- [Node.js](https://docs.taosdata.com/reference/connector/node/) -- [Rust](https://docs.taosdata.com/reference/connector/rust/) -- [C#](https://docs.taosdata.com/reference/connector/csharp/) -- [RESTful API](https://docs.taosdata.com/reference/rest-api/) +- [Java](https://docs.tdengine.com/reference/connector/java/) +- [C/C++](https://docs.tdengine.com/reference/connector/cpp/) +- [Python](https://docs.tdengine.com/reference/connector/python/) +- [Go](https://docs.tdengine.com/reference/connector/go/) +- [Node.js](https://docs.tdengine.com/reference/connector/node/) +- [Rust](https://docs.tdengine.com/reference/connector/rust/) +- [C#](https://docs.tdengine.com/reference/connector/csharp/) +- [RESTful API](https://docs.tdengine.com/reference/rest-api/) # Contribute to TDengine diff --git a/source/dnode/vnode/src/tsdb/tsdbCommit.c b/source/dnode/vnode/src/tsdb/tsdbCommit.c index 020f3b0bc6dedf70efa45a835056c8dd41d9a158..04a6de8472ade43b3e7e2b420f8e2d0c0656a2c8 100644 --- a/source/dnode/vnode/src/tsdb/tsdbCommit.c +++ b/source/dnode/vnode/src/tsdb/tsdbCommit.c @@ -835,6 +835,9 @@ static int32_t tsdbMergeCommitLast(SCommitter *pCommitter, STbDataIter *pIter) { // set block data schema if need if (pBlockData->suid == 0 && pBlockData->uid == 0) { + code = tsdbCommitterUpdateTableSchema(pCommitter, pTbData->suid, pTbData->uid); + if (code) goto _err; + code = tBlockDataInit(pBlockData, pTbData->suid, pTbData->suid ? 0 : pTbData->uid, pCommitter->skmTable.pTSchema); if (code) goto _err; @@ -963,7 +966,20 @@ static int32_t tsdbCommitTableData(SCommitter *pCommitter, STbData *pTbData) { pRow = NULL; } - if (pRow == NULL) goto _exit; + if (pRow == NULL) { + if (pCommitter->dReader.pBlockIdx && tTABLEIDCmprFn(pCommitter->dReader.pBlockIdx, pTbData) == 0) { + SBlockIdx blockIdx = {.suid = pTbData->suid, .uid = pTbData->uid}; + code = tsdbWriteBlock(pCommitter->dWriter.pWriter, &pCommitter->dReader.mBlock, &blockIdx); + if (code) goto _err; + + if (taosArrayPush(pCommitter->dWriter.aBlockIdx, &blockIdx) == NULL) { + code = TSDB_CODE_OUT_OF_MEMORY; + goto _err; + } + } + + goto _exit; + } int32_t iBlock = 0; SBlock block;