diff --git a/documentation/webdocs/markdowndocs/Super Table-ch.md b/documentation/webdocs/markdowndocs/Super Table-ch.md index 524fb51b17566955670966e2c51e7cb38e5df67f..38e6f8c17f900ad9597366f5131b3f1eebeb8863 100644 --- a/documentation/webdocs/markdowndocs/Super Table-ch.md +++ b/documentation/webdocs/markdowndocs/Super Table-ch.md @@ -1,6 +1,6 @@ # 超级表STable：多表聚合 -TDengine要求每个数据采集点单独建表，这样能极大提高数据的插入/查询性能，但是导致系统中表的数量猛增，让应用对表的维护以及聚合、统计操作难度加大。为降低应用的开发难度，TDengine引入了超级表STable (Super Table)的概念。 +TDengine要求每个数据采集点单独建表。独立建表的模式能够避免写入过程中的同步加锁，因此能够极大地提升数据的插入/查询性能。但是独立建表意味着系统中表的数量与采集点的数量在同一个量级。如果采集点众多，将导致系统中表的数量也非常庞大，让应用对表的维护以及聚合、统计操作难度加大。为降低应用的开发难度，TDengine引入了超级表(Super Table, 简称为STable)的概念。 ## 什么是超级表 @@ -9,14 +9,14 @@ STable是同一类型数据采集点的抽象，是同类型采集实例的集 TDengine扩展标准SQL语法用于定义STable，使用关键词tags指定标签信息。语法如下： ```mysql -CREATE TABLE ( TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …) +CREATE TABLE ( TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …) ``` -其中tag_name是标签名，tag_type是标签的数据类型。标签可以使用时间戳之外的其他TDengine支持的数据类型，标签的个数最多为6个，名字不能与系统关键词相同，也不能与其他列名相同。如： +其中tag_name是标签名，tag_type是标签的数据类型。标签可以使用时间戳之外的其他TDengine支持的数据类型，标签的个数最多为32个，名字不能与系统关键词相同，也不能与其他列名相同。如： ```mysql -create table thermometer (ts timestamp, degree float) -tags (location binary(20), type int) +CREATE TABLE thermometer (ts timestamp, degree float) +TAGS (location binary(20), type int) ``` 上述SQL创建了一个名为thermometer的STable，带有标签location和标签type。 @@ -30,7 +30,7 @@ CREATE TABLE USING TAGS (tag_value1,...) 沿用上面温度计的例子，使用超级表thermometer建立单个温度计数据表的语句如下： ```mysql -create table t1 using thermometer tags ('beijing', 10) +CREATE TABLE t1 USING thermometer TAGS ('beijing', 10) ``` 上述SQL以thermometer为模板，创建了名为t1的表，这张表的Schema就是thermometer的Schema，但标签location值为'beijing'，标签type值为10。 diff --git a/documentation/webdocs/markdowndocs/advanced features-ch.md b/documentation/webdocs/markdowndocs/advanced features-ch.md index fc229e71e6c76325938f794ac9fccb542778d2ae..9dc289a8d5765d7ffc5dc9bab5267b61559f0d02 100644 --- a/documentation/webdocs/markdowndocs/advanced features-ch.md +++ b/documentation/webdocs/markdowndocs/advanced features-ch.md @@ -67,7 +67,7 @@ TDengine内嵌支持轻量级的消息订阅与推送服务。使用系统提供 TDengine的订阅与推送服务的状态是客户端维持，TDengine服务器并不维持。因此如果应用重启，从哪个时间点开始获取最新数据，由应用决定。 -订阅相关API请见 [连接器](https://www.taosdata.com/cn/documentation/connector/)。 +订阅相关API文档请见 [C/C++ 数据订阅接口](https://www.taosdata.com/cn/documentation/connector/#C/C++-%E6%95%B0%E6%8D%AE%E8%AE%A2%E9%98%85%E6%8E%A5%E5%8F%A3)，《[TDEngine中订阅的用途和用法](https://www.taosdata.com/blog/2020/02/12/1277.html)》则以一个示例详细介绍了这些ＡＰＩ的用法。 ## 缓存 (Cache) TDengine采用时间驱动缓存管理策略（First-In-First-Out，FIFO），又称为写驱动的缓存管理机制。这种策略有别于读驱动的数据缓存模式（Least-Recent-Use，LRU），直接将最近写入的数据保存在系统的缓存中。当缓存达到临界值的时候，将最早的数据批量写入磁盘。一般意义上来说，对于物联网数据的使用，用户最为关心最近产生的数据，即当前状态。TDengine充分利用了这一特性，将最近到达的（当前状态）数据保存在缓存中。 diff --git a/documentation/webdocs/markdowndocs/advanced features.md b/documentation/webdocs/markdowndocs/advanced features.md index e841a5a6a531c94908b0f027d2d3e808d40ecac5..3eae454da59eec4aa7ea73741dcde2e882cb0821 100644 --- a/documentation/webdocs/markdowndocs/advanced features.md +++ b/documentation/webdocs/markdowndocs/advanced features.md @@ -62,7 +62,7 @@ Time series data is a sequence of data points over time. Inside a table, the dat To reduce the development complexity and improve data consistency, TDengine provides the pub/sub functionality. To publish a message, you simply insert a record into a table. Compared with popular messaging tool Kafka, you subscribe to a table or a SQL query statement, instead of a topic. Once new data points arrive, TDengine will notify the application. The process is just like Kafka. -The detailed API will be introduced in the [connectors](https://www.taosdata.com/en/documentation/connector/) section. +The API documentation is at [C/C++ subscription API](https://www.taosdata.com/en/documentation/connector/#C/C++-subscription-API) section, and you can find more information from blog article (only Chinese version at present) [The usage of subscription](https://www.taosdata.com/blog/2020/02/12/1277.html). ##Caching TDengine allocates a fixed-size buffer in memory, the newly arrived data will be written into the buffer first. Every device or table gets one or more memory blocks. For typical IoT scenarios, the hot data shall always be newly arrived data, they are more important for timely analysis. Based on this observation, TDengine manages the cache blocks in First-In-First-Out strategy. If no enough space in the buffer, the oldest data will be saved into hard disk first, then be overwritten by newly arrived data. TDengine also guarantees every device can keep at least one block of data in the buffer. diff --git a/src/client/src/tscSQLParser.c b/src/client/src/tscSQLParser.c index 60c597b38528535a13d6c30142bc212affceb50c..e5499fd04f68cec22cc548db27cdb27c4da61717 100644 --- a/src/client/src/tscSQLParser.c +++ b/src/client/src/tscSQLParser.c @@ -900,7 +900,7 @@ bool validateOneTags(SSqlCmd* pCmd, TAOS_FIELD* pTagField) { return false; } - if (pTagField->type < TSDB_DATA_TYPE_BOOL && pTagField->type > TSDB_DATA_TYPE_NCHAR) { + if ((pTagField->type < TSDB_DATA_TYPE_BOOL) || (pTagField->type > TSDB_DATA_TYPE_NCHAR)) { invalidSqlErrMsg(tscGetErrorMsgPayload(pCmd), msg6); return false; } @@ -1655,7 +1655,7 @@ int32_t addExprAndResultField(SQueryInfo* pQueryInfo, int32_t colIdx, tSQLExprIt SSchema* pSchema = tsGetSchema(pMeterMetaInfo->pMeterMeta); // functions can not be applied to tags - if (index.columnIndex >= pMeterMetaInfo->pMeterMeta->numOfColumns) { + if ((index.columnIndex >= pMeterMetaInfo->pMeterMeta->numOfColumns) || (index.columnIndex < 0)) { return invalidSqlErrMsg(pQueryInfo->msg, msg6); } @@ -5663,4 +5663,4 @@ int32_t doCheckForQuery(SSqlObj* pSql, SQuerySQL* pQuerySql, int32_t index) { bool hasDefaultQueryTimeRange(SQueryInfo *pQueryInfo) { return (pQueryInfo->stime == 0 && pQueryInfo->etime == INT64_MAX) || (pQueryInfo->stime == INT64_MAX && pQueryInfo->etime == 0); -} \ No newline at end of file +} diff --git a/src/connector/grafana/tdengine/package-lock.json b/src/connector/grafana/tdengine/package-lock.json index 9401fbcd0ae0c67a10ff365d4015c977ba43ade1..f8d2df1150d0c7c2581de4ceb61e5647d52ab871 100644 --- a/src/connector/grafana/tdengine/package-lock.json +++ b/src/connector/grafana/tdengine/package-lock.json @@ -3992,9 +3992,9 @@ } }, "yarn": { - "version": "1.21.1", - "resolved": "https://registry.npmjs.org/yarn/-/yarn-1.21.1.tgz", - "integrity": "sha512-dQgmJv676X/NQczpbiDtc2hsE/pppGDJAzwlRiADMTvFzYbdxPj2WO4PcNyriSt2c4jsCMpt8UFRKHUozt21GQ==" + "version": "1.22.0", + "resolved": "https://registry.npmjs.org/yarn/-/yarn-1.22.0.tgz", + "integrity": "sha512-KMHP/Jq53jZKTY9iTUt3dIVl/be6UPs2INo96+BnZHLKxYNTfwMmlgHTaMWyGZoO74RI4AIFvnWhYrXq2USJkg==" } } } diff --git a/src/connector/grafana/tdengine/package.json b/src/connector/grafana/tdengine/package.json index 8e542bef2647aa6bb4fe2f404665a4ce9c707345..0eb7a76be6cfccd81f680f179c8e59499690201b 100644 --- a/src/connector/grafana/tdengine/package.json +++ b/src/connector/grafana/tdengine/package.json @@ -39,7 +39,7 @@ }, "dependencies": { "lodash": "^4.17.13", - "yarn": "^1.21.1" + "yarn": "^1.22.0" }, "homepage": "https://github.com/taosdata/TDengine/tree/develop/src/connector/grafana/tdengine" } diff --git a/src/connector/grafana/tdengine/yarn.lock b/src/connector/grafana/tdengine/yarn.lock index b2b869cb1ac179ece8d5e673bbabfe0856f9bd6e..fe7e8122ec371f66811c235be81f2e1276ccd5b1 100644 --- a/src/connector/grafana/tdengine/yarn.lock +++ b/src/connector/grafana/tdengine/yarn.lock @@ -2957,7 +2957,7 @@ yargs@~3.10.0: decamelize "^1.0.0" window-size "0.1.0" -yarn@^1.21.1: - version "1.21.1" - resolved "https://registry.yarnpkg.com/yarn/-/yarn-1.21.1.tgz#1d5da01a9a03492dc4a5957befc1fd12da83d89c" - integrity sha512-dQgmJv676X/NQczpbiDtc2hsE/pppGDJAzwlRiADMTvFzYbdxPj2WO4PcNyriSt2c4jsCMpt8UFRKHUozt21GQ== +yarn@^1.22.0: + version "1.22.0" + resolved "https://registry.yarnpkg.com/yarn/-/yarn-1.22.0.tgz#acf82906e36bcccd1ccab1cfb73b87509667c881" + integrity sha512-KMHP/Jq53jZKTY9iTUt3dIVl/be6UPs2INo96+BnZHLKxYNTfwMmlgHTaMWyGZoO74RI4AIFvnWhYrXq2USJkg==