From 0e01b1a9c0dca38c32ede76b6f1b612733c8f38b Mon Sep 17 00:00:00 2001 From: dmchen Date: Wed, 30 Oct 2024 07:29:13 +0000 Subject: [PATCH 1/5] fix/TD-32766-check-ref-before-free-main --- source/dnode/mgmt/mgmt_vnode/src/vmFile.c | 6 +++--- source/dnode/mgmt/mgmt_vnode/src/vmInt.c | 16 ++++++++++++---- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/source/dnode/mgmt/mgmt_vnode/src/vmFile.c b/source/dnode/mgmt/mgmt_vnode/src/vmFile.c index d07fe9c5ac..7566b69c02 100644 --- a/source/dnode/mgmt/mgmt_vnode/src/vmFile.c +++ b/source/dnode/mgmt/mgmt_vnode/src/vmFile.c @@ -38,7 +38,7 @@ int32_t vmGetAllVnodeListFromHash(SVnodeMgmt *pMgmt, int32_t *numOfVnodes, SVnod SVnodeObj *pVnode = *ppVnode; if (pVnode && num < size) { int32_t refCount = atomic_add_fetch_32(&pVnode->refCount, 1); - // dTrace("vgId:%d, acquire vnode list, ref:%d", pVnode->vgId, refCount); + dTrace("vgId:%d,acquire vnode, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); pVnodes[num++] = (*ppVnode); pIter = taosHashIterate(pMgmt->hash, pIter); } else { @@ -52,7 +52,7 @@ int32_t vmGetAllVnodeListFromHash(SVnodeMgmt *pMgmt, int32_t *numOfVnodes, SVnod SVnodeObj *pVnode = *ppVnode; if (pVnode && num < size) { int32_t refCount = atomic_add_fetch_32(&pVnode->refCount, 1); - // dTrace("vgId:%d, acquire vnode list, ref:%d", pVnode->vgId, refCount); + dTrace("vgId:%d, acquire vnode, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); pVnodes[num++] = (*ppVnode); pIter = taosHashIterate(pMgmt->closedHash, pIter); } else { @@ -84,7 +84,7 @@ int32_t vmGetVnodeListFromHash(SVnodeMgmt *pMgmt, int32_t *numOfVnodes, SVnodeOb SVnodeObj *pVnode = *ppVnode; if (pVnode && num < size) { int32_t refCount = atomic_add_fetch_32(&pVnode->refCount, 1); - // dTrace("vgId:%d, acquire vnode list, ref:%d", pVnode->vgId, refCount); + dTrace("vgId:%d, acquire vnode, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); pVnodes[num++] = (*ppVnode); pIter = taosHashIterate(pMgmt->hash, pIter); } else { diff --git a/source/dnode/mgmt/mgmt_vnode/src/vmInt.c b/source/dnode/mgmt/mgmt_vnode/src/vmInt.c index a285043382..0913d7539b 100644 --- a/source/dnode/mgmt/mgmt_vnode/src/vmInt.c +++ b/source/dnode/mgmt/mgmt_vnode/src/vmInt.c @@ -103,7 +103,7 @@ SVnodeObj *vmAcquireVnodeImpl(SVnodeMgmt *pMgmt, int32_t vgId, bool strict) { pVnode = NULL; } else { int32_t refCount = atomic_add_fetch_32(&pVnode->refCount, 1); - // dTrace("vgId:%d, acquire vnode, ref:%d", pVnode->vgId, refCount); + dTrace("vgId:%d, acquire vnode, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); } (void)taosThreadRwlockUnlock(&pMgmt->lock); @@ -115,16 +115,24 @@ SVnodeObj *vmAcquireVnode(SVnodeMgmt *pMgmt, int32_t vgId) { return vmAcquireVno void vmReleaseVnode(SVnodeMgmt *pMgmt, SVnodeObj *pVnode) { if (pVnode == NULL) return; - (void)taosThreadRwlockRdlock(&pMgmt->lock); + //(void)taosThreadRwlockRdlock(&pMgmt->lock); int32_t refCount = atomic_sub_fetch_32(&pVnode->refCount, 1); - // dTrace("vgId:%d, release vnode, ref:%d", pVnode->vgId, refCount); - (void)taosThreadRwlockUnlock(&pMgmt->lock); + dTrace("vgId:%d, release vnode, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); + //(void)taosThreadRwlockUnlock(&pMgmt->lock); } static void vmFreeVnodeObj(SVnodeObj **ppVnode) { if (!ppVnode || !(*ppVnode)) return; SVnodeObj *pVnode = *ppVnode; + + int32_t refCount = 1; + while (refCount > 0) { + dWarn("vgId:%d, vnode is refenced, retry to free in 200ms, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); + taosMsleep(200); + refCount = atomic_load_32(&pVnode->refCount); + } + taosMemoryFree(pVnode->path); taosMemoryFree(pVnode); ppVnode[0] = NULL; From eaba7dfb5fcff1ac8e0afdb47be3aa009f89ed45 Mon Sep 17 00:00:00 2001 From: dmchen Date: Wed, 30 Oct 2024 09:01:50 +0000 Subject: [PATCH 2/5] fix/TD-32766-check-ref-before-free-first-check --- source/dnode/mgmt/mgmt_vnode/src/vmInt.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/dnode/mgmt/mgmt_vnode/src/vmInt.c b/source/dnode/mgmt/mgmt_vnode/src/vmInt.c index 0913d7539b..682c179270 100644 --- a/source/dnode/mgmt/mgmt_vnode/src/vmInt.c +++ b/source/dnode/mgmt/mgmt_vnode/src/vmInt.c @@ -126,7 +126,7 @@ static void vmFreeVnodeObj(SVnodeObj **ppVnode) { SVnodeObj *pVnode = *ppVnode; - int32_t refCount = 1; + int32_t refCount = atomic_load_32(&pVnode->refCount); while (refCount > 0) { dWarn("vgId:%d, vnode is refenced, retry to free in 200ms, vnode:%p, ref:%d", pVnode->vgId, pVnode, refCount); taosMsleep(200); From 385c4934d291883fe56c2f9d39c84cb326d41920 Mon Sep 17 00:00:00 2001 From: Shengliang Guan Date: Thu, 31 Oct 2024 12:11:44 +0800 Subject: [PATCH 3/5] doc: update query result --- docs/zh/05-basic/03-query.md | 309 +++++++++++++++++++---------------- 1 file changed, 165 insertions(+), 144 deletions(-) diff --git a/docs/zh/05-basic/03-query.md b/docs/zh/05-basic/03-query.md index 6afdba0997..21f4b74f73 100644 --- a/docs/zh/05-basic/03-query.md +++ b/docs/zh/05-basic/03-query.md @@ -20,21 +20,22 @@ taosBenchmark --start-timestamp=1600000000000 --tables=100 --records=10000000 -- ```sql SELECT * FROM meters -WHERE voltage > 10 +WHERE voltage > 230 ORDER BY ts DESC -LIMIT 5 +LIMIT 5; ``` -上面的 SQL，从超级表 `meters` 中查询出电压 `voltage` 大于 10 的记录，按时间降序排列，且仅输出前 5 行。查询结果如下： +上面的 SQL，从超级表 `meters` 中查询出电压 `voltage` 大于 230 的记录，按时间降序排列，且仅输出前 5 行。查询结果如下： ```text - ts | current | voltage | phase | groupid | location | -========================================================================================================== -2023-11-14 22:13:10.000 | 1.1294620 | 18 | 0.3531540 | 8 | California.MountainView | -2023-11-14 22:13:10.000 | 1.0294620 | 12 | 0.3631540 | 2 | California.Campbell | -2023-11-14 22:13:10.000 | 1.0294620 | 16 | 0.3531540 | 1 | California.Campbell | -2023-11-14 22:13:10.000 | 1.1294620 | 18 | 0.3531540 | 2 | California.Campbell | -2023-11-14 22:13:10.000 | 1.1294620 | 16 | 0.3431540 | 7 | California.PaloAlto | + ts | current | voltage | phase | groupid | location | +============================================================================================================ +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 1 | California.LosAngles | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 5 | California.Cupertino | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 4 | California.SanFrancisco | +Query OK, 5 row(s) in set (0.145403s) ``` ## 聚合查询 @@ -48,28 +49,28 @@ TDengine 支持通过 GROUP BY 子句，对数据进行聚合查询。SQL 语句 group by 子句用于对数据进行分组，并为每个分组返回一行汇总信息。在 group by 子句中，可以使用表或视图中的任何列作为分组依据，这些列不需要出现在 select 列表中。此外，用户可以直接在超级表上执行聚合查询，无须预先创建子表。以智能电表的数据模型为例，使用 group by 子句的 SQL 如下： ```sql -SELECT groupid,avg(voltage) +SELECT groupid, avg(voltage) FROM meters WHERE ts >= "2022-01-01T00:00:00+08:00" AND ts < "2023-01-01T00:00:00+08:00" -GROUP BY groupid +GROUP BY groupid; ``` 上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2023-01-01T00:00:00+08:00` 的数据，按照 `groupid` 进行分组，求每组的平均电压。查询结果如下： ```text - groupid | avg(voltage) | -========================================== - 8 | 9.104040404040404 | - 5 | 9.078333333333333 | - 1 | 9.087037037037037 | - 7 | 8.991414141414142 | - 9 | 8.789814814814815 | - 6 | 9.051010101010101 | - 4 | 9.135353535353536 | - 10 | 9.213131313131314 | - 2 | 9.008888888888889 | - 3 | 8.783888888888889 | + groupid | avg(voltage) | +====================================== + 8 | 243.961981544901079 | + 5 | 243.961981544901079 | + 1 | 243.961981544901079 | + 7 | 243.961981544901079 | + 9 | 243.961981544901079 | + 6 | 243.961981544901079 | + 4 | 243.961981544901079 | + 10 | 243.961981544901079 | + 2 | 243.961981544901079 | + 3 | 243.961981544901079 | Query OK, 10 row(s) in set (0.042446s) ``` @@ -110,24 +111,24 @@ TDengine 按如下方式处理数据切分子句。 ```sql SELECT location, avg(voltage) FROM meters -PARTITION BY location +PARTITION BY location; ``` 上面的示例 SQL 查询超级表 `meters`，将数据按标签 `location` 进行分组，每个分组计算电压的平均值。查询结果如下： ```text - location | avg(voltage) | -========================================================= - California.SantaClara | 8.793334320000000 | - California.SanFrancisco | 9.017645882352941 | - California.SanJose | 9.156112940000000 | - California.LosAngles | 9.036753507692307 | - California.SanDiego | 8.967037053333334 | - California.Sunnyvale | 8.978572085714285 | - California.PaloAlto | 8.936665800000000 | - California.Cupertino | 8.987654066666666 | - California.MountainView | 9.046297266666667 | - California.Campbell | 9.149999028571429 | + location | avg(voltage) | +====================================================== + California.SantaClara | 243.962050000000005 | + California.SanFrancisco | 243.962050000000005 | + California.SanJose | 243.962050000000005 | + California.LosAngles | 243.962050000000005 | + California.SanDiego | 243.962050000000005 | + California.Sunnyvale | 243.962050000000005 | + California.PaloAlto | 243.962050000000005 | + California.Cupertino | 243.962050000000005 | + California.MountainView | 243.962050000000005 | + California.Campbell | 243.962050000000005 | Query OK, 10 row(s) in set (2.415961s) ``` @@ -200,20 +201,20 @@ SLIMIT 2; 上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据；数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且每个时间窗口向后偏移 5 秒；最后，仅取前 2 个分片的数据作为结果。查询结果如下： ```text - tbname | _wstart | _wend | avg(voltage) | -========================================================================================== - d40 | 2021-12-31 15:59:05.000 | 2021-12-31 16:00:05.000 | 4.000000000000000 | - d40 | 2021-12-31 16:00:05.000 | 2021-12-31 16:01:05.000 | 5.000000000000000 | - d40 | 2021-12-31 16:01:05.000 | 2021-12-31 16:02:05.000 | 8.000000000000000 | - d40 | 2021-12-31 16:02:05.000 | 2021-12-31 16:03:05.000 | 7.666666666666667 | - d40 | 2021-12-31 16:03:05.000 | 2021-12-31 16:04:05.000 | 9.666666666666666 | - d40 | 2021-12-31 16:04:05.000 | 2021-12-31 16:05:05.000 | 15.199999999999999 | - d41 | 2021-12-31 15:59:05.000 | 2021-12-31 16:00:05.000 | 4.000000000000000 | - d41 | 2021-12-31 16:00:05.000 | 2021-12-31 16:01:05.000 | 7.000000000000000 | - d41 | 2021-12-31 16:01:05.000 | 2021-12-31 16:02:05.000 | 9.000000000000000 | - d41 | 2021-12-31 16:02:05.000 | 2021-12-31 16:03:05.000 | 10.666666666666666 | - d41 | 2021-12-31 16:03:05.000 | 2021-12-31 16:04:05.000 | 8.333333333333334 | - d41 | 2021-12-31 16:04:05.000 | 2021-12-31 16:05:05.000 | 9.600000000000000 | + tbname | _wstart | _wend | avg(voltage) | +====================================================================================== + d2 | 2021-12-31 23:59:05.000 | 2022-01-01 00:00:05.000 | 253.000000000000000 | + d2 | 2022-01-01 00:00:05.000 | 2022-01-01 00:01:05.000 | 244.166666666666657 | + d2 | 2022-01-01 00:01:05.000 | 2022-01-01 00:02:05.000 | 241.833333333333343 | + d2 | 2022-01-01 00:02:05.000 | 2022-01-01 00:03:05.000 | 243.166666666666657 | + d2 | 2022-01-01 00:03:05.000 | 2022-01-01 00:04:05.000 | 240.833333333333343 | + d2 | 2022-01-01 00:04:05.000 | 2022-01-01 00:05:05.000 | 244.800000000000011 | + d26 | 2021-12-31 23:59:05.000 | 2022-01-01 00:00:05.000 | 253.000000000000000 | + d26 | 2022-01-01 00:00:05.000 | 2022-01-01 00:01:05.000 | 244.166666666666657 | + d26 | 2022-01-01 00:01:05.000 | 2022-01-01 00:02:05.000 | 241.833333333333343 | + d26 | 2022-01-01 00:02:05.000 | 2022-01-01 00:03:05.000 | 243.166666666666657 | + d26 | 2022-01-01 00:03:05.000 | 2022-01-01 00:04:05.000 | 240.833333333333343 | + d26 | 2022-01-01 00:04:05.000 | 2022-01-01 00:05:05.000 | 244.800000000000011 | Query OK, 12 row(s) in set (0.021265s) ``` @@ -255,19 +256,19 @@ SLIMIT 1; 上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据，数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且时间窗口按照 30 秒进行滑动；最后，仅取前 1 个分片的数据作为结果。查询结果如下： ```text - tbname | _wstart | avg(voltage) | -================================================================ - d40 | 2021-12-31 15:59:30.000 | 4.000000000000000 | - d40 | 2021-12-31 16:00:00.000 | 5.666666666666667 | - d40 | 2021-12-31 16:00:30.000 | 4.333333333333333 | - d40 | 2021-12-31 16:01:00.000 | 5.000000000000000 | - d40 | 2021-12-31 16:01:30.000 | 9.333333333333334 | - d40 | 2021-12-31 16:02:00.000 | 9.666666666666666 | - d40 | 2021-12-31 16:02:30.000 | 10.000000000000000 | - d40 | 2021-12-31 16:03:00.000 | 10.333333333333334 | - d40 | 2021-12-31 16:03:30.000 | 10.333333333333334 | - d40 | 2021-12-31 16:04:00.000 | 13.000000000000000 | - d40 | 2021-12-31 16:04:30.000 | 15.333333333333334 | + tbname | _wstart | avg(voltage) | +============================================================= + d2 | 2021-12-31 23:59:30.000 | 248.333333333333343 | + d2 | 2022-01-01 00:00:00.000 | 246.000000000000000 | + d2 | 2022-01-01 00:00:30.000 | 244.666666666666657 | + d2 | 2022-01-01 00:01:00.000 | 240.833333333333343 | + d2 | 2022-01-01 00:01:30.000 | 239.500000000000000 | + d2 | 2022-01-01 00:02:00.000 | 243.833333333333343 | + d2 | 2022-01-01 00:02:30.000 | 243.833333333333343 | + d2 | 2022-01-01 00:03:00.000 | 241.333333333333343 | + d2 | 2022-01-01 00:03:30.000 | 241.666666666666657 | + d2 | 2022-01-01 00:04:00.000 | 244.166666666666657 | + d2 | 2022-01-01 00:04:30.000 | 244.666666666666657 | Query OK, 11 row(s) in set (0.013153s) ``` @@ -290,13 +291,13 @@ SLIMIT 1; 上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据，数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且时间窗口按照 1 分钟进行切分；最后，仅取前 1 个分片的数据作为结果。查询结果如下： ```text - tbname | _wstart | _wend | avg(voltage) | -================================================================================================================= - d28 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 7.333333333333333 | - d28 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 8.000000000000000 | - d28 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 11.000000000000000 | - d28 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 6.666666666666667 | - d28 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 10.000000000000000 | + tbname | _wstart | _wend | avg(voltage) | +====================================================================================== + d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 | + d2 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 | + d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 | + d2 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 | + d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 | Query OK, 5 row(s) in set (0.016812s) ``` @@ -342,53 +343,65 @@ SLIMIT 2; 上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据；数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，如果窗口内的数据出现缺失，则使用使用前一个非 NULL 值填充数据；最后，仅取前 2 个分片的数据作为结果。查询结果如下： ```text - tbname | _wstart | _wend | avg(voltage) | -================================================================================================================= - d40 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 5.666666666666667 | - d40 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 5.000000000000000 | - d40 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 9.666666666666666 | - d40 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 10.333333333333334 | - d40 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 13.000000000000000 | - d41 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 5.666666666666667 | - d41 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 9.333333333333334 | - d41 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 11.000000000000000 | - d41 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 7.666666666666667 | - d41 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 10.000000000000000 | + tbname | _wstart | _wend | avg(voltage) | +======================================================================================= + d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 | + d2 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 | + d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 | + d2 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 | + d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 | + d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 | + d26 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 | + d26 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 | + d26 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 | + d26 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 | Query OK, 10 row(s) in set (0.022866s) ``` ### 状态窗口 -使用整数（布尔值）或字符串来标识产生记录时候设备的状态量。产生的记录如果具有相同的状态量数值则归属于同一个状态窗口，数值改变后该窗口关闭。TDengine 还支持将 CASE 表达式用在状态量，可以表达某个状态的开始是由满足某个条件而触发，这个状态的结束是由另外一个条件满足而触发的语义。以智能电表为例，电压正常范围是 205V 到 235V，那么可以通过监控电压来判断电路是否正常。 +使用整数（布尔值）或字符串来标识产生记录时候设备的状态量。产生的记录如果具有相同的状态量数值则归属于同一个状态窗口，数值改变后该窗口关闭。TDengine 还支持将 CASE 表达式用在状态量，可以表达某个状态的开始是由满足某个条件而触发，这个状态的结束是由另外一个条件满足而触发的语义。以智能电表为例，电压正常范围是 225V 到 235V，那么可以通过监控电压来判断电路是否正常。 ```sql -SELECT tbname, _wstart, _wend,_wduration, CASE WHEN voltage >= 205 and voltage <= 235 THEN 1 ELSE 0 END status +SELECT tbname, _wstart, _wend,_wduration, CASE WHEN voltage >= 225 and voltage <= 235 THEN 1 ELSE 0 END status FROM meters WHERE ts >= "2022-01-01T00:00:00+08:00" AND ts < "2022-01-01T00:05:00+08:00" PARTITION BY tbname STATE_WINDOW( - CASE WHEN voltage >= 205 and voltage <= 235 THEN 1 ELSE 0 END + CASE WHEN voltage >= 225 and voltage <= 235 THEN 1 ELSE 0 END ) -SLIMIT 10; +SLIMIT 2; ``` -以上 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:05:00+08:00的数据；数据首先按照子表名 tbname 进行数据切分；根据电压是否在正常范围内进行状态窗口的划分；最后，取前 10 个分片的数据作为结果。查询结果如下： +以上 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:05:00+08:00的数据；数据首先按照子表名 tbname 进行数据切分；根据电压是否在正常范围内进行状态窗口的划分；最后，取前 2 个分片的数据作为结果。查询结果如下：（由于数据是随机生成，结果集包含的数据条数会有不同） ```text - tbname | _wstart | _wend | _wduration | status | -===================================================================================================================================== - d76 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d47 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d37 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d87 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d64 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d35 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d83 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d51 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d63 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | - d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 | -Query OK, 10 row(s) in set (0.040495s) + tbname | _wstart | _wend | _wduration | status | +=============================================================================================== + d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:20.000 | 80000 | 0 | + d2 | 2022-01-01 00:01:30.000 | 2022-01-01 00:01:30.000 | 0 | 1 | + d2 | 2022-01-01 00:01:40.000 | 2022-01-01 00:01:40.000 | 0 | 0 | + d2 | 2022-01-01 00:01:50.000 | 2022-01-01 00:01:50.000 | 0 | 1 | + d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:02:20.000 | 20000 | 0 | + d2 | 2022-01-01 00:02:30.000 | 2022-01-01 00:02:30.000 | 0 | 1 | + d2 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:00.000 | 20000 | 0 | + d2 | 2022-01-01 00:03:10.000 | 2022-01-01 00:03:10.000 | 0 | 1 | + d2 | 2022-01-01 00:03:20.000 | 2022-01-01 00:03:40.000 | 20000 | 0 | + d2 | 2022-01-01 00:03:50.000 | 2022-01-01 00:03:50.000 | 0 | 1 | + d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:04:50.000 | 50000 | 0 | + d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:20.000 | 80000 | 0 | + d26 | 2022-01-01 00:01:30.000 | 2022-01-01 00:01:30.000 | 0 | 1 | + d26 | 2022-01-01 00:01:40.000 | 2022-01-01 00:01:40.000 | 0 | 0 | + d26 | 2022-01-01 00:01:50.000 | 2022-01-01 00:01:50.000 | 0 | 1 | + d26 | 2022-01-01 00:02:00.000 | 2022-01-01 00:02:20.000 | 20000 | 0 | + d26 | 2022-01-01 00:02:30.000 | 2022-01-01 00:02:30.000 | 0 | 1 | + d26 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:00.000 | 20000 | 0 | + d26 | 2022-01-01 00:03:10.000 | 2022-01-01 00:03:10.000 | 0 | 1 | + d26 | 2022-01-01 00:03:20.000 | 2022-01-01 00:03:40.000 | 20000 | 0 | + d26 | 2022-01-01 00:03:50.000 | 2022-01-01 00:03:50.000 | 0 | 1 | + d26 | 2022-01-01 00:04:00.000 | 2022-01-01 00:04:50.000 | 50000 | 0 | +Query OK, 22 row(s) in set (0.153403s) ``` ### 会话窗口 @@ -417,18 +430,18 @@ SLIMIT 10; 上面的 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:10:00+08:00的数据；数据先按照子表名 tbname 进行数据切分，再根据 10 分钟的会话窗口进行切分；最后，取前 10 个分片的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下： ```text - tbname | _wstart | _wend | _wduration | count(*) | -===================================================================================================================================== - d76 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d47 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d37 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d87 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d64 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d35 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d83 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d51 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d63 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | - d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 | + tbname | _wstart | _wend | _wduration | count(*) | +=============================================================================================== + d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d52 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d64 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d76 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d28 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d4 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d88 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d77 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | + d54 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 | Query OK, 10 row(s) in set (0.043489s) ``` @@ -458,26 +471,26 @@ FROM meters WHERE ts >= "2022-01-01T00:00:00+08:00" AND ts < "2022-01-01T00:10:00+08:00" PARTITION BY tbname -EVENT_WINDOW START WITH voltage >= 10 END WITH voltage < 20 -LIMIT 10; +EVENT_WINDOW START WITH voltage >= 225 END WITH voltage < 235 +LIMIT 5; ``` -上面的 SQL，查询超级表meters中，时间戳大于等于2022-01-01T00:00:00+08:00，且时间戳小于2022-01-01T00:10:00+08:00的数据；数据先按照子表名tbname进行数据切分，再根据事件窗口条件：电压大于等于 10V，且小于 20V 进行切分；最后，取前 10 行的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下： +上面的 SQL，查询超级表meters中，时间戳大于等于2022-01-01T00:00:00+08:00，且时间戳小于2022-01-01T00:10:00+08:00的数据；数据先按照子表名tbname进行数据切分，再根据事件窗口条件：电压大于等于 225V，且小于 235V 进行切分；最后，取每个分片的前 5 行的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下： ```text - tbname | _wstart | _wend | _wduration | count(*) | -===================================================================================================================================== - d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:00:00.000 | 0 | 1 | - d0 | 2021-12-31 16:00:30.000 | 2021-12-31 16:00:30.000 | 0 | 1 | - d0 | 2021-12-31 16:00:40.000 | 2021-12-31 16:00:40.000 | 0 | 1 | - d0 | 2021-12-31 16:01:20.000 | 2021-12-31 16:01:20.000 | 0 | 1 | - d0 | 2021-12-31 16:02:20.000 | 2021-12-31 16:02:20.000 | 0 | 1 | - d0 | 2021-12-31 16:02:30.000 | 2021-12-31 16:02:30.000 | 0 | 1 | - d0 | 2021-12-31 16:03:10.000 | 2021-12-31 16:03:10.000 | 0 | 1 | - d0 | 2021-12-31 16:03:30.000 | 2021-12-31 16:03:30.000 | 0 | 1 | - d0 | 2021-12-31 16:03:40.000 | 2021-12-31 16:03:40.000 | 0 | 1 | - d0 | 2021-12-31 16:03:50.000 | 2021-12-31 16:03:50.000 | 0 | 1 | -Query OK, 10 row(s) in set (0.034127s) + tbname | _wstart | _wend | _wduration | count(*) | +============================================================================================== + d0 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 90000 | 10 | + d0 | 2022-01-01 00:01:40.000 | 2022-01-01 00:02:30.000 | 50000 | 6 | + d0 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:10.000 | 30000 | 4 | + d0 | 2022-01-01 00:03:20.000 | 2022-01-01 00:07:10.000 | 230000 | 24 | + d0 | 2022-01-01 00:07:20.000 | 2022-01-01 00:07:50.000 | 30000 | 4 | + d1 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 90000 | 10 | + d1 | 2022-01-01 00:01:40.000 | 2022-01-01 00:02:30.000 | 50000 | 6 | + d1 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:10.000 | 30000 | 4 | + d1 | 2022-01-01 00:03:20.000 | 2022-01-01 00:07:10.000 | 230000 | 24 | +…… +Query OK, 500 row(s) in set (0.328557s) ``` ### 计数窗口 @@ -492,17 +505,25 @@ sliding_val 是一个常量，表示窗口滑动的数量，类似于 interval select _wstart, _wend, count(*) from meters where ts >= "2022-01-01T00:00:00+08:00" and ts < "2022-01-01T00:30:00+08:00" -count_window(10); +count_window(1000); ``` -上面的 SQL 查询超级表 meters 中时间戳大于等于 2022-01-01T00:00:00+08:00 且时间戳小于 2022-01-01T00:10:00+08:00 的数据。以每 10 条数据为一组，返回每组的开始时间、结束时间和分组条数。查询结果如下。 +上面的 SQL 查询超级表 meters 中时间戳大于等于 2022-01-01T00:00:00+08:00 且时间戳小于 2022-01-01T00:10:00+08:00 的数据。以每 1000 条数据为一组，返回每组的开始时间、结束时间和分组条数。查询结果如下： ```text -_wstart | _wend |count(*)| -=========================================================== -2021-12-31 16:00:00.000 | 2021-12-31 16:10:00.000 | 10 | -2021-12-31 16:10:00.000 | 2021-12-31 16:20:00.000 | 10 | -2021-12-31 16:20:00.000 | 2021-12-31 16:30:00.000 | 10 | + _wstart | _wend | count(*) | +===================================================================== + 2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 1000 | + 2022-01-01 00:01:40.000 | 2022-01-01 00:03:10.000 | 1000 | + 2022-01-01 00:03:20.000 | 2022-01-01 00:04:50.000 | 1000 | + 2022-01-01 00:05:00.000 | 2022-01-01 00:06:30.000 | 1000 | + 2022-01-01 00:06:40.000 | 2022-01-01 00:08:10.000 | 1000 | + 2022-01-01 00:08:20.000 | 2022-01-01 00:09:50.000 | 1000 | + 2022-01-01 00:10:00.000 | 2022-01-01 00:11:30.000 | 1000 | + 2022-01-01 00:11:40.000 | 2022-01-01 00:13:10.000 | 1000 | + 2022-01-01 00:13:20.000 | 2022-01-01 00:14:50.000 | 1000 | + 2022-01-01 00:15:00.000 | 2022-01-01 00:16:30.000 | 1000 | +Query OK, 10 row(s) in set (0.062794s) ``` ## 时序数据特有函数 @@ -563,14 +584,14 @@ UNION ALL 上面的 SQL，分别查询：子表 d1 的 1 条数据，子表 d11 的 2 条数据，子表 d21 的 3 条数据，并将结果合并。返回的结果如下： ```text - tbname | ts | current | voltage | phase | -================================================================================================= - d11 | 2020-09-13 12:26:40.000 | 1.0260611 | 6 | 0.3620200 | - d11 | 2020-09-13 12:26:50.000 | 2.9544230 | 8 | 1.0048079 | - d21 | 2020-09-13 12:26:40.000 | 1.0260611 | 2 | 0.3520200 | - d21 | 2020-09-13 12:26:50.000 | 2.9544230 | 2 | 0.9948080 | - d21 | 2020-09-13 12:27:00.000 | -0.0000430 | 12 | 0.0099860 | - d1 | 2020-09-13 12:26:40.000 | 1.0260611 | 10 | 0.3520200 | + tbname | ts | current | voltage | phase | +==================================================================================== + d11 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 | + d11 | 2020-09-13 20:26:50.000 | 14.2392311 | 234 | 148.0000000 | + d1 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 | + d21 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 | + d21 | 2020-09-13 20:26:50.000 | 14.2392311 | 234 | 148.0000000 | + d21 | 2020-09-13 20:27:00.000 | 10.0999422 | 251 | 146.0000000 | Query OK, 6 row(s) in set (0.006438s) ``` From 210e0ae69f27fb51936c4db3db24545a3480327f Mon Sep 17 00:00:00 2001 From: Shengliang Guan Date: Thu, 31 Oct 2024 12:39:54 +0800 Subject: [PATCH 4/5] doc: minor changes --- docs/en/14-reference/12-config/index.md | 19 ++++++++++--------- docs/zh/06-advanced/02-cache.md | 4 ++-- .../zh/14-reference/01-components/01-taosd.md | 1 + .../zh/14-reference/01-components/02-taosc.md | 1 + 4 files changed, 14 insertions(+), 11 deletions(-) diff --git a/docs/en/14-reference/12-config/index.md b/docs/en/14-reference/12-config/index.md index 14a1a0fb9d..77d183a5ef 100755 --- a/docs/en/14-reference/12-config/index.md +++ b/docs/en/14-reference/12-config/index.md @@ -701,15 +701,6 @@ The charset that takes effect is UTF-8. | Type | String | | Default Value | _tag_null | -### smlDataFormat - -| Attribute | Description | -| ----------- | ----------------------------------------------------------------------------------- | -| Applicable | Client only | -| Meaning | Whether schemaless columns are consistently ordered, depat, discarded since 3.0.3.0 | -| Value Range | 0: not consistent; 1: consistent. | -| Default | 0 | - ### smlTsDefaultName | Attribute | Description | @@ -719,6 +710,16 @@ The charset that takes effect is UTF-8. | Type | String | | Default Value | _ts | +### smlDot2Underline + +| Attribute | Description | +| -------- | -------------------------------------------------------- | +| Applicable | Client only | +| Meaning | Convert the dot in the supertable name to an underscore | +| Type | Bool | +| Default Value | true | + + ## Compress Parameters ### compressMsgSize diff --git a/docs/zh/06-advanced/02-cache.md b/docs/zh/06-advanced/02-cache.md index b83509c2dd..065adbf50a 100644 --- a/docs/zh/06-advanced/02-cache.md +++ b/docs/zh/06-advanced/02-cache.md @@ -54,10 +54,10 @@ TDengine 利用这些日志文件实现故障前的状态恢复。在写入 WAL 数据库参数 wal_level 和 wal_fsync_period 共同决定了 WAL 的保存行为。。 - wal_level：此参数控制 WAL 的保存级别。级别 1 表示仅将数据写入 WAL，但不立即执行 fsync 函数；级别 2 则表示在写入 WAL 的同时执行 fsync 函数。默认情况下，wal_level 设为 1。虽然执行 fsync 函数可以提高数据的持久性，但相应地也会降低写入性能。 -- wal_fsync_period：当 wal_level 设置为 1 时，这个参数控制执行 fsync 的频率。设置为 0 表示每次写入后立即执行 fsync，这可以确保数据的安全性，但可能会牺牲一些性能。当设置为大于 0 的数值时，表示 fsync 周期，默认为 3000，范围是[1， 180000]，单位毫秒。 +- wal_fsync_period：当 wal_level 设置为 2 时，这个参数控制执行 fsync 的频率。设置为 0 表示每次写入后立即执行 fsync，这可以确保数据的安全性，但可能会牺牲一些性能。当设置为大于 0 的数值时，表示 fsync 周期，默认为 3000，范围是[1， 180000]，单位毫秒。 ```sql -CREATE DATABASE POWER WAL_LEVEL 1 WAL_FSYNC_PERIOD 3000; +CREATE DATABASE POWER WAL_LEVEL 2 WAL_FSYNC_PERIOD 3000; ``` 在创建数据库时可以选择不同的参数类型，来选择性能优先或者可靠性优先。 diff --git a/docs/zh/14-reference/01-components/01-taosd.md b/docs/zh/14-reference/01-components/01-taosd.md index f11321c84f..091f9416eb 100644 --- a/docs/zh/14-reference/01-components/01-taosd.md +++ b/docs/zh/14-reference/01-components/01-taosd.md @@ -180,6 +180,7 @@ charset 的有效值是 UTF-8。 | tmrDebugFlag | 定时器模块的日志开关，取值范围同上 | | uDebugFlag | 共用功能模块的日志开关，取值范围同上 | | rpcDebugFlag | rpc 模块的日志开关，取值范围同上 | +| cDebugFlag | 客户端模块的日志开关，取值范围同上 | | jniDebugFlag | jni 模块的日志开关，取值范围同上 | | qDebugFlag | query 模块的日志开关，取值范围同上 | | dDebugFlag | dnode 模块的日志开关，取值范围同上，缺省值 135 | diff --git a/docs/zh/14-reference/01-components/02-taosc.md b/docs/zh/14-reference/01-components/02-taosc.md index d9f36a2df7..bd1e700041 100755 --- a/docs/zh/14-reference/01-components/02-taosc.md +++ b/docs/zh/14-reference/01-components/02-taosc.md @@ -35,6 +35,7 @@ TDengine 客户端驱动提供了应用编程所需要的全部 API，并且在 |smlAutoChildTableNameDelimiter | schemaless tag之间的连接符，连起来作为子表名，无缺省值 | |smlTagName | schemaless tag 为空时默认的 tag 名字, 缺省值 "_tag_null" | |smlTsDefaultName | schemaless自动建表的时间列名字通过该配置设置, 缺省值 "_ts" | +|smlDot2Underline | schemaless 把超级表名中的 dot 转成下划线 | |enableCoreFile | crash 时是否生成 core 文件，0: 不生成， 1： 生成；缺省值：1 | |enableScience | 是否开启科学计数法显示浮点数; 0: 不开始, 1: 开启；缺省值：1 | |compressMsgSize | 是否对 RPC 消息进行压缩; -1: 所有消息都不压缩; 0: 所有消息都压缩; N (N>0): 只有大于 N 个字节的消息才压缩; 缺省值 -1| From 3d1379a8303ded33019890801abb4a4a2d9210a3 Mon Sep 17 00:00:00 2001 From: Shengliang Guan Date: Thu, 31 Oct 2024 12:43:16 +0800 Subject: [PATCH 5/5] doc: minor changes --- docs/zh/05-basic/03-query.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/zh/05-basic/03-query.md b/docs/zh/05-basic/03-query.md index 21f4b74f73..4a1771d5fd 100644 --- a/docs/zh/05-basic/03-query.md +++ b/docs/zh/05-basic/03-query.md @@ -28,13 +28,13 @@ LIMIT 5; 上面的 SQL，从超级表 `meters` 中查询出电压 `voltage` 大于 230 的记录，按时间降序排列，且仅输出前 5 行。查询结果如下： ```text - ts | current | voltage | phase | groupid | location | -============================================================================================================ -2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | -2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 1 | California.LosAngles | -2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | -2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 5 | California.Cupertino | -2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 4 | California.SanFrancisco | + ts | current | voltage | phase | groupid | location | +=================================================================================================== +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 1 | California.LosAngles | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 5 | California.Cupertino | +2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 4 | California.SanFrancisco | Query OK, 5 row(s) in set (0.145403s) ```