feature: optimize interval with limit
This commit is contained in:
wangjiaming0909
parent
c57defa1c9
commit
fba43e1748
|
|
@ -25,6 +25,7 @@ extern "C" {
|
|||
#include "tsort.h"
|
||||
#include "ttszip.h"
|
||||
#include "tvariant.h"
|
||||
#include "theap.h"
|
||||
|
||||
#include "dataSinkMgt.h"
|
||||
#include "executil.h"
|
||||
|
|
@ -418,6 +419,14 @@ typedef struct SIntervalAggOperatorInfo {
|
|||
EOPTR_EXEC_MODEL execModel; // operator execution model [batch model|stream model]
|
||||
STimeWindowAggSupp twAggSup;
|
||||
SArray* pPrevValues; // SArray<SGroupKeys> used to keep the previous not null value for interpolation.
|
||||
// for limit optimization
|
||||
bool limited;
|
||||
int64_t limit;
|
||||
bool slimited;
|
||||
int64_t slimit;
|
||||
uint64_t curGroupId; // initialize to UINT64_MAX
|
||||
uint64_t handledGroupNum;
|
||||
BoundedQueue* pBQ;
|
||||
} SIntervalAggOperatorInfo;
|
||||
|
||||
typedef struct SMergeAlignedIntervalAggOperatorInfo {
|
||||
|
|
|
|||
|
|
@ -2118,8 +2118,9 @@ int32_t buildGroupIdMapForAllTables(STableListInfo* pTableListInfo, SReadHandle*
|
|||
if (code != TSDB_CODE_SUCCESS) {
|
||||
return code;
|
||||
}
|
||||
if (pScanNode->groupOrderScan) pTableListInfo->numOfOuputGroups = taosArrayGetSize(pTableListInfo->pTableList);
|
||||
|
||||
if (groupSort) {
|
||||
if (groupSort || pScanNode->groupOrderScan) {
|
||||
code = sortTableGroup(pTableListInfo);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -275,7 +275,6 @@ SOperatorInfo* createOperator(SPhysiNode* pPhyNode, SExecTaskInfo* pTaskInfo, SR
|
|||
SNode* pTagIndexCond, const char* pUser, const char* dbname) {
|
||||
int32_t type = nodeType(pPhyNode);
|
||||
const char* idstr = GET_TASKID(pTaskInfo);
|
||||
|
||||
if (pPhyNode->pChildren == NULL || LIST_LENGTH(pPhyNode->pChildren) == 0) {
|
||||
SOperatorInfo* pOperator = NULL;
|
||||
if (QUERY_NODE_PHYSICAL_PLAN_TABLE_SCAN == type) {
|
||||
|
|
|
|||
|
|
@ -848,6 +848,7 @@ static SSDataBlock* doTableScan(SOperatorInfo* pOperator) {
|
|||
return result;
|
||||
}
|
||||
|
||||
while (1) {
|
||||
if ((++pInfo->currentGroupId) >= tableListGetOutputGroups(pInfo->base.pTableListInfo)) {
|
||||
setOperatorCompleted(pOperator);
|
||||
return NULL;
|
||||
|
|
@ -869,9 +870,7 @@ static SSDataBlock* doTableScan(SOperatorInfo* pOperator) {
|
|||
if (result != NULL) {
|
||||
return result;
|
||||
}
|
||||
|
||||
setOperatorCompleted(pOperator);
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -876,7 +876,67 @@ bool needDeleteWindowBuf(STimeWindow* pWin, STimeWindowAggSupp* pTwSup) {
|
|||
return pTwSup->maxTs != INT64_MIN && pWin->ekey < pTwSup->maxTs - pTwSup->deleteMark;
|
||||
}
|
||||
|
||||
static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResultRowInfo, SSDataBlock* pBlock,
|
||||
static bool tsKeyCompFn(void* l, void* r, void* param) {
|
||||
TSKEY* lTS = (TSKEY*)l;
|
||||
TSKEY* rTS = (TSKEY*)r;
|
||||
SIntervalAggOperatorInfo* pInfo = param;
|
||||
return pInfo->binfo.outputTsOrder == ORDER_ASC ? *lTS < *rTS : *lTS > *rTS;
|
||||
}
|
||||
|
||||
static bool isCalculatedWin(SIntervalAggOperatorInfo* pInfo, const STimeWindow* win, uint64_t tableGroupId) {
|
||||
char keyBuf[sizeof(TSKEY) + sizeof(uint64_t)] = {0};
|
||||
SET_RES_WINDOW_KEY(keyBuf, (char*)&win->skey, sizeof(TSKEY), tableGroupId);
|
||||
return tSimpleHashGet(pInfo->aggSup.pResultRowHashTable, keyBuf, GET_RES_WINDOW_KEY_LEN(sizeof(TSKEY))) != NULL;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief check if cur window should be filtered out by limit info
|
||||
* @retval true if should be filtered out
|
||||
* @retval false if not filtering out
|
||||
* @note If no limit info, we skip filtering.
|
||||
* If input/output ts order mismatch, we skip filtering too.
|
||||
* eg. input ts order: desc, and output ts order: asc, limit: 10
|
||||
* IntervalOperator should output the first 10 windows, however, we can't find the first 10 windows until we scan
|
||||
* every tuple in every block.
|
||||
* And the boundedQueue keeps refreshing all records with smaller ts key.
|
||||
*/
|
||||
static bool filterWindowWithLimit(SIntervalAggOperatorInfo* pOperatorInfo, STimeWindow* win, uint64_t groupId) {
|
||||
if (!pOperatorInfo->limited // if no limit info, no filter will be applied
|
||||
|| pOperatorInfo->binfo.inputTsOrder !=
|
||||
pOperatorInfo->binfo.outputTsOrder // if input/output ts order mismatch, no filter
|
||||
) {
|
||||
return false;
|
||||
}
|
||||
if (pOperatorInfo->limit == 0) return true;
|
||||
|
||||
if (pOperatorInfo->pBQ == NULL) {
|
||||
pOperatorInfo->pBQ = createBoundedQueue(pOperatorInfo->limit - 1, tsKeyCompFn, taosMemoryFree, pOperatorInfo);
|
||||
}
|
||||
|
||||
bool shouldFilter = false;
|
||||
// if BQ has been full, compare it with top of BQ
|
||||
if (taosBQSize(pOperatorInfo->pBQ) == taosBQMaxSize(pOperatorInfo->pBQ) + 1) {
|
||||
PriorityQueueNode* top = taosBQTop(pOperatorInfo->pBQ);
|
||||
shouldFilter = tsKeyCompFn(top->data, &win->skey, pOperatorInfo);
|
||||
}
|
||||
if (shouldFilter) {
|
||||
return true;
|
||||
} else if (isCalculatedWin(pOperatorInfo, win, groupId)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// cur win not been filtered out and not been pushed into BQ yet, push it into BQ
|
||||
PriorityQueueNode node = {.data = taosMemoryMalloc(sizeof(TSKEY))};
|
||||
*((TSKEY*)node.data) = win->skey;
|
||||
|
||||
if (NULL == taosBQPush(pOperatorInfo->pBQ, &node)) {
|
||||
taosMemoryFree(node.data);
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
static bool hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResultRowInfo, SSDataBlock* pBlock,
|
||||
int32_t scanFlag) {
|
||||
SIntervalAggOperatorInfo* pInfo = (SIntervalAggOperatorInfo*)pOperatorInfo->info;
|
||||
|
||||
|
|
@ -891,8 +951,21 @@ static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResul
|
|||
TSKEY ts = getStartTsKey(&pBlock->info.window, tsCols);
|
||||
SResultRow* pResult = NULL;
|
||||
|
||||
if (tableGroupId != pInfo->curGroupId) {
|
||||
pInfo->handledGroupNum += 1;
|
||||
if (pInfo->slimited && pInfo->handledGroupNum > pInfo->slimit) {
|
||||
return true;
|
||||
} else {
|
||||
pInfo->curGroupId = tableGroupId;
|
||||
destroyBoundedQueue(pInfo->pBQ);
|
||||
pInfo->pBQ = NULL;
|
||||
}
|
||||
}
|
||||
|
||||
STimeWindow win =
|
||||
getActiveTimeWindow(pInfo->aggSup.pResultBuf, pResultRowInfo, ts, &pInfo->interval, pInfo->binfo.inputTsOrder);
|
||||
if (filterWindowWithLimit(pInfo, &win, tableGroupId)) return false;
|
||||
|
||||
int32_t ret = setTimeWindowOutputBuf(pResultRowInfo, &win, (scanFlag == MAIN_SCAN), &pResult, tableGroupId,
|
||||
pSup->pCtx, numOfOutput, pSup->rowEntryInfoOffset, &pInfo->aggSup, pTaskInfo);
|
||||
if (ret != TSDB_CODE_SUCCESS || pResult == NULL) {
|
||||
|
|
@ -929,7 +1002,7 @@ static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResul
|
|||
while (1) {
|
||||
int32_t prevEndPos = forwardRows - 1 + startPos;
|
||||
startPos = getNextQualifiedWindow(&pInfo->interval, &nextWin, &pBlock->info, tsCols, prevEndPos, pInfo->binfo.inputTsOrder);
|
||||
if (startPos < 0) {
|
||||
if (startPos < 0 || filterWindowWithLimit(pInfo, &nextWin, tableGroupId)) {
|
||||
break;
|
||||
}
|
||||
// null data, failed to allocate more memory buffer
|
||||
|
|
@ -963,6 +1036,7 @@ static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResul
|
|||
if (pInfo->timeWindowInterpo) {
|
||||
saveDataBlockLastRow(pInfo->pPrevValues, pBlock, pInfo->pInterpCols);
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
void doCloseWindow(SResultRowInfo* pResultRowInfo, const SIntervalAggOperatorInfo* pInfo, SResultRow* pResult) {
|
||||
|
|
@ -1043,7 +1117,7 @@ static int32_t doOpenIntervalAgg(SOperatorInfo* pOperator) {
|
|||
|
||||
// the pDataBlock are always the same one, no need to call this again
|
||||
setInputDataBlock(pSup, pBlock, pInfo->binfo.inputTsOrder, scanFlag, true);
|
||||
hashIntervalAgg(pOperator, &pInfo->binfo.resultRowInfo, pBlock, scanFlag);
|
||||
if (hashIntervalAgg(pOperator, &pInfo->binfo.resultRowInfo, pBlock, scanFlag)) break;
|
||||
}
|
||||
|
||||
initGroupedResultInfo(&pInfo->groupResInfo, pInfo->aggSup.pResultRowHashTable, pInfo->binfo.outputTsOrder);
|
||||
|
|
@ -1495,6 +1569,7 @@ void destroyIntervalOperatorInfo(void* param) {
|
|||
|
||||
cleanupGroupResInfo(&pInfo->groupResInfo);
|
||||
colDataDestroy(&pInfo->twAggSup.timeWindowData);
|
||||
destroyBoundedQueue(pInfo->pBQ);
|
||||
taosMemoryFreeClear(param);
|
||||
}
|
||||
|
||||
|
|
@ -1658,6 +1733,17 @@ SOperatorInfo* createIntervalOperatorInfo(SOperatorInfo* downstream, SIntervalPh
|
|||
pInfo->interval = interval;
|
||||
pInfo->twAggSup = as;
|
||||
pInfo->binfo.mergeResultBlock = pPhyNode->window.mergeDataBlock;
|
||||
if (pPhyNode->window.node.pLimit) {
|
||||
SLimitNode* pLimit = (SLimitNode*)pPhyNode->window.node.pLimit;
|
||||
pInfo->limited = true;
|
||||
pInfo->limit = pLimit->limit + pLimit->offset;
|
||||
}
|
||||
if (pPhyNode->window.node.pSlimit) {
|
||||
SLimitNode* pLimit = (SLimitNode*)pPhyNode->window.node.pSlimit;
|
||||
pInfo->slimited = true;
|
||||
pInfo->slimit = pLimit->limit + pLimit->offset;
|
||||
pInfo->curGroupId = UINT64_MAX;
|
||||
}
|
||||
|
||||
if (pPhyNode->window.pExprs != NULL) {
|
||||
int32_t numOfScalar = 0;
|
||||
|
|
|
|||
|
|
@ -847,7 +847,6 @@ static int32_t createWindowLogicNodeByInterval(SLogicPlanContext* pCxt, SInterva
|
|||
: (pSelect->hasTimeLineFunc ? getRequireDataOrder(true, pSelect) : DATA_ORDER_LEVEL_IN_BLOCK);
|
||||
pWindow->node.resultDataOrder =
|
||||
pCxt->pPlanCxt->streamQuery ? DATA_ORDER_LEVEL_GLOBAL : getRequireDataOrder(true, pSelect);
|
||||
|
||||
pWindow->pTspk = nodesCloneNode(pInterval->pCol);
|
||||
if (NULL == pWindow->pTspk) {
|
||||
nodesDestroyNode((SNode*)pWindow);
|
||||
|
|
|
|||
|
|
@ -1546,11 +1546,33 @@ static bool planOptNodeListHasTbname(SNodeList* pKeys) {
|
|||
}
|
||||
|
||||
static bool partTagsIsOptimizableNode(SLogicNode* pNode) {
|
||||
return ((QUERY_NODE_LOGIC_PLAN_PARTITION == nodeType(pNode) ||
|
||||
(QUERY_NODE_LOGIC_PLAN_AGG == nodeType(pNode) && NULL != ((SAggLogicNode*)pNode)->pGroupKeys &&
|
||||
NULL != ((SAggLogicNode*)pNode)->pAggFuncs)) &&
|
||||
1 == LIST_LENGTH(pNode->pChildren) &&
|
||||
QUERY_NODE_LOGIC_PLAN_SCAN == nodeType(nodesListGetNode(pNode->pChildren, 0)));
|
||||
bool ret = 1 == LIST_LENGTH(pNode->pChildren) &&
|
||||
QUERY_NODE_LOGIC_PLAN_SCAN == nodeType(nodesListGetNode(pNode->pChildren, 0));
|
||||
if (!ret) return ret;
|
||||
switch (nodeType(pNode)) {
|
||||
case QUERY_NODE_LOGIC_PLAN_PARTITION: {
|
||||
if (pNode->pParent && nodeType(pNode->pParent) == QUERY_NODE_LOGIC_PLAN_WINDOW) {
|
||||
SWindowLogicNode* pWindow = (SWindowLogicNode*)pNode->pParent;
|
||||
if (pWindow->winType == WINDOW_TYPE_INTERVAL) {
|
||||
// if interval has slimit, we push down partition node to scan, and scan will set groupOrderScan to true
|
||||
// we want to skip groups of blocks after slimit satisfied
|
||||
// if interval only has limit, we do not push down partition node to scan
|
||||
// we want to get grouped output from partition node and make use of limit
|
||||
// if no slimit and no limit, we push down partition node and groupOrderScan is false, cause we do not need
|
||||
// group ordered output
|
||||
if (!pWindow->node.pSlimit && pWindow->node.pLimit) ret = false;
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case QUERY_NODE_LOGIC_PLAN_AGG: {
|
||||
SAggLogicNode* pAgg = (SAggLogicNode*)pNode;
|
||||
ret = pAgg->pGroupKeys && pAgg->pAggFuncs;
|
||||
} break;
|
||||
default:
|
||||
ret = false;
|
||||
break;
|
||||
}
|
||||
return ret;
|
||||
}
|
||||
|
||||
static SNodeList* partTagsGetPartKeys(SLogicNode* pNode) {
|
||||
|
|
@ -1691,6 +1713,8 @@ static int32_t partTagsOptimize(SOptimizeContext* pCxt, SLogicSubplan* pLogicSub
|
|||
scanPathOptSetGroupOrderScan(pScan);
|
||||
pParent->hasGroupKeyOptimized = true;
|
||||
}
|
||||
if (pNode->pParent->pSlimit)
|
||||
pScan->groupOrderScan = true;
|
||||
|
||||
NODES_CLEAR_LIST(pNode->pChildren);
|
||||
nodesDestroyNode((SNode*)pNode);
|
||||
|
|
@ -2644,21 +2668,77 @@ static int32_t tagScanOptimize(SOptimizeContext* pCxt, SLogicSubplan* pLogicSubp
|
|||
}
|
||||
|
||||
static bool pushDownLimitOptShouldBeOptimized(SLogicNode* pNode) {
|
||||
if (NULL == pNode->pLimit || 1 != LIST_LENGTH(pNode->pChildren)) {
|
||||
if ((NULL == pNode->pLimit && pNode->pSlimit == NULL) || 1 != LIST_LENGTH(pNode->pChildren)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
SLogicNode* pChild = (SLogicNode*)nodesListGetNode(pNode->pChildren, 0);
|
||||
// push down to sort node
|
||||
if (QUERY_NODE_LOGIC_PLAN_SORT == nodeType(pChild)) {
|
||||
// if we have pushed down, we skip it
|
||||
if (pChild->pLimit) return false;
|
||||
} else if (QUERY_NODE_LOGIC_PLAN_SCAN != nodeType(pChild) || QUERY_NODE_LOGIC_PLAN_SORT == nodeType(pNode)) {
|
||||
// push down to table scan node
|
||||
// if pNode is sortNode, we skip push down limit info to table scan node
|
||||
if (pChild->pLimit || pChild->pSlimit) return false;
|
||||
return true;
|
||||
}
|
||||
|
||||
static void swapLimit(SLogicNode* pParent, SLogicNode* pChild) {
|
||||
pChild->pLimit = pParent->pLimit;
|
||||
pParent->pLimit = NULL;
|
||||
}
|
||||
|
||||
static void cloneLimit(SLogicNode* pParent, SLogicNode* pChild) {
|
||||
SLimitNode* pLimit = NULL;
|
||||
if (pParent->pLimit) {
|
||||
pChild->pLimit = nodesCloneNode(pParent->pLimit);
|
||||
pLimit = (SLimitNode*)pChild->pLimit;
|
||||
pLimit->limit += pLimit->offset;
|
||||
pLimit->offset = 0;
|
||||
}
|
||||
|
||||
if (pParent->pSlimit) {
|
||||
pChild->pSlimit = nodesCloneNode(pParent->pSlimit);
|
||||
pLimit = (SLimitNode*)pChild->pSlimit;
|
||||
pLimit->limit += pLimit->offset;
|
||||
pLimit->offset = 0;
|
||||
}
|
||||
}
|
||||
|
||||
static bool pushDownLimitHow(SLogicNode* pNodeWithLimit, SLogicNode* pNodeLimitPushTo);
|
||||
static bool pushDownLimitTo(SLogicNode* pNodeWithLimit, SLogicNode* pNodeLimitPushTo) {
|
||||
switch (nodeType(pNodeLimitPushTo)) {
|
||||
case QUERY_NODE_LOGIC_PLAN_WINDOW: {
|
||||
SWindowLogicNode* pWindow = (SWindowLogicNode*)pNodeLimitPushTo;
|
||||
if (pWindow->winType != WINDOW_TYPE_INTERVAL) break;
|
||||
cloneLimit(pNodeWithLimit, pNodeLimitPushTo);
|
||||
return true;
|
||||
}
|
||||
case QUERY_NODE_LOGIC_PLAN_FILL:
|
||||
case QUERY_NODE_LOGIC_PLAN_SORT: {
|
||||
cloneLimit(pNodeWithLimit, pNodeLimitPushTo);
|
||||
SNode* pChild = NULL;
|
||||
FOREACH(pChild, pNodeLimitPushTo->pChildren) { pushDownLimitHow(pNodeLimitPushTo, (SLogicNode*)pChild); }
|
||||
return true;
|
||||
}
|
||||
case QUERY_NODE_LOGIC_PLAN_SCAN:
|
||||
if (nodeType(pNodeWithLimit) == QUERY_NODE_LOGIC_PLAN_PROJECT && pNodeWithLimit->pLimit) {
|
||||
swapLimit(pNodeWithLimit, pNodeLimitPushTo);
|
||||
return true;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
||||
static bool pushDownLimitHow(SLogicNode* pNodeWithLimit, SLogicNode* pNodeLimitPushTo) {
|
||||
switch (nodeType(pNodeWithLimit)) {
|
||||
case QUERY_NODE_LOGIC_PLAN_PROJECT:
|
||||
case QUERY_NODE_LOGIC_PLAN_FILL:
|
||||
return pushDownLimitTo(pNodeWithLimit, pNodeLimitPushTo);
|
||||
case QUERY_NODE_LOGIC_PLAN_SORT: {
|
||||
SSortLogicNode* pSort = (SSortLogicNode*)pNodeWithLimit;
|
||||
if (sortPriKeyOptIsPriKeyOrderBy(pSort->pSortKeys)) return pushDownLimitTo(pNodeWithLimit, pNodeLimitPushTo);
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
static int32_t pushDownLimitOptimize(SOptimizeContext* pCxt, SLogicSubplan* pLogicSubplan) {
|
||||
|
|
@ -2669,17 +2749,9 @@ static int32_t pushDownLimitOptimize(SOptimizeContext* pCxt, SLogicSubplan* pLog
|
|||
|
||||
SLogicNode* pChild = (SLogicNode*)nodesListGetNode(pNode->pChildren, 0);
|
||||
nodesDestroyNode(pChild->pLimit);
|
||||
if (QUERY_NODE_LOGIC_PLAN_SORT == nodeType(pChild)) {
|
||||
pChild->pLimit = nodesCloneNode(pNode->pLimit);
|
||||
SLimitNode* pLimit = (SLimitNode*)pChild->pLimit;
|
||||
pLimit->limit += pLimit->offset;
|
||||
pLimit->offset = 0;
|
||||
} else {
|
||||
pChild->pLimit = pNode->pLimit;
|
||||
pNode->pLimit = NULL;
|
||||
}
|
||||
if (pushDownLimitHow(pNode, pChild)) {
|
||||
pCxt->optimized = true;
|
||||
|
||||
}
|
||||
return TSDB_CODE_SUCCESS;
|
||||
}
|
||||
|
||||
|
|
@ -2980,6 +3052,7 @@ static const SOptimizeRule optimizeRuleSet[] = {
|
|||
{.pName = "sortNonPriKeyOptimize", .optimizeFunc = sortNonPriKeyOptimize},
|
||||
{.pName = "SortPrimaryKey", .optimizeFunc = sortPrimaryKeyOptimize},
|
||||
{.pName = "SmaIndex", .optimizeFunc = smaIndexOptimize},
|
||||
{.pName = "PushDownLimit", .optimizeFunc = pushDownLimitOptimize},
|
||||
{.pName = "PartitionTags", .optimizeFunc = partTagsOptimize},
|
||||
{.pName = "MergeProjects", .optimizeFunc = mergeProjectsOptimize},
|
||||
{.pName = "EliminateProject", .optimizeFunc = eliminateProjOptimize},
|
||||
|
|
@ -2988,7 +3061,6 @@ static const SOptimizeRule optimizeRuleSet[] = {
|
|||
{.pName = "RewriteUnique", .optimizeFunc = rewriteUniqueOptimize},
|
||||
{.pName = "LastRowScan", .optimizeFunc = lastRowScanOptimize},
|
||||
{.pName = "TagScan", .optimizeFunc = tagScanOptimize},
|
||||
{.pName = "PushDownLimit", .optimizeFunc = pushDownLimitOptimize},
|
||||
{.pName = "TableCountScan", .optimizeFunc = tableCountScanOptimize},
|
||||
};
|
||||
// clang-format on
|
||||
|
|
|
|||
|
|
@ -498,6 +498,18 @@ static int32_t stbSplRewriteFromMergeNode(SMergeLogicNode* pMerge, SLogicNode* p
|
|||
}
|
||||
break;
|
||||
}
|
||||
case QUERY_NODE_LOGIC_PLAN_WINDOW: {
|
||||
SWindowLogicNode* pWindow = (SWindowLogicNode*)pNode;
|
||||
if (pMerge->node.pLimit) {
|
||||
nodesDestroyNode(pMerge->node.pLimit);
|
||||
pMerge->node.pLimit = NULL;
|
||||
}
|
||||
if (pMerge->node.pSlimit) {
|
||||
nodesDestroyNode(pMerge->node.pSlimit);
|
||||
pMerge->node.pSlimit = NULL;
|
||||
}
|
||||
break;
|
||||
}
|
||||
default:
|
||||
break;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@
|
|||
,,y,system-test,./pytest.sh python3 ./test.py -f 2-query/nestedQuery_math.py -Q 4
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 2-query/nestedQuery_time.py -Q 4
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 2-query/nestedQuery_26.py -Q 4
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 2-query/interval_limit_opt.py -Q 4
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 7-tmq/tmqShow.py
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 7-tmq/tmqDropStb.py
|
||||
,,y,system-test,./pytest.sh python3 ./test.py -f 7-tmq/subscribeStb0.py
|
||||
|
|
|
|||
|
|
@ -1603,58 +1603,58 @@ QUERY_PLAN: Time Range: [-9223372036854775808,
|
|||
taos> select _wstart, last(ts), avg(c2) from meters interval(10s) order by _wstart desc;
|
||||
_wstart | last(ts) | avg(c2) |
|
||||
================================================================================
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000000000 |
|
||||
|
||||
taos> select _wstart, last(ts), avg(c2) from meters interval(10s) order by _wstart asc;
|
||||
_wstart | last(ts) | avg(c2) |
|
||||
================================================================================
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000000000 |
|
||||
|
||||
taos> select _wstart, first(ts), avg(c2) from meters interval(10s) order by _wstart asc;
|
||||
_wstart | first(ts) | avg(c2) |
|
||||
================================================================================
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000000000 |
|
||||
|
||||
taos> select _wstart, first(ts), avg(c2) from meters interval(10s) order by _wstart desc;
|
||||
_wstart | first(ts) | avg(c2) |
|
||||
================================================================================
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000 |
|
||||
2022-05-24 00:01:00.000 | 2022-05-24 00:01:08.000 | 210.000000000000000 |
|
||||
2022-05-23 00:01:00.000 | 2022-05-23 00:01:08.000 | 116.000000000000000 |
|
||||
2022-05-22 00:01:00.000 | 2022-05-22 00:01:08.000 | 196.000000000000000 |
|
||||
2022-05-21 00:01:00.000 | 2022-05-21 00:01:08.000 | 11.000000000000000 |
|
||||
2022-05-20 00:01:00.000 | 2022-05-20 00:01:08.000 | 120.000000000000000 |
|
||||
2022-05-19 00:01:00.000 | 2022-05-19 00:01:08.000 | 243.000000000000000 |
|
||||
2022-05-18 00:01:00.000 | 2022-05-18 00:01:08.000 | 58.000000000000000 |
|
||||
2022-05-17 00:01:00.000 | 2022-05-17 00:01:08.000 | 59.000000000000000 |
|
||||
2022-05-16 00:01:00.000 | 2022-05-16 00:01:08.000 | 136.000000000000000 |
|
||||
2022-05-15 00:01:00.000 | 2022-05-15 00:01:08.000 | 234.000000000000000 |
|
||||
|
||||
taos> select last(a) as d from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s)) order by d;
|
||||
d |
|
||||
|
|
@ -1792,35 +1792,35 @@ taos> select last(b) as d from (select last(ts) as b, avg(c2) as c from meters i
|
|||
taos> select _wstart, first(a) as d, avg(c) from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s) order by a desc) where a > '2022-05-15 00:01:00.000' and a < '2022-05-21 00:01:08.000' interval(5h) fill(linear) order by d desc;
|
||||
_wstart | d | avg(c) |
|
||||
================================================================================
|
||||
2022-05-20 20:00:00.000 | 2022-05-21 00:01:00.000 | 11.000000000 |
|
||||
2022-05-20 15:00:00.000 | 2022-05-20 18:01:00.000 | 38.250000000 |
|
||||
2022-05-20 10:00:00.000 | 2022-05-20 12:01:00.000 | 65.500000000 |
|
||||
2022-05-20 05:00:00.000 | 2022-05-20 06:01:00.000 | 92.750000000 |
|
||||
2022-05-20 00:00:00.000 | 2022-05-20 00:01:00.000 | 120.000000000 |
|
||||
2022-05-19 19:00:00.000 | 2022-05-19 19:13:00.000 | 144.600000000 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.200000000 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000 |
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000 |
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000 |
|
||||
2022-05-18 13:00:00.000 | 2022-05-18 14:25:00.000 | 169.000000000 |
|
||||
2022-05-18 08:00:00.000 | 2022-05-18 09:37:00.000 | 132.000000000 |
|
||||
2022-05-18 03:00:00.000 | 2022-05-18 04:49:00.000 | 95.000000000 |
|
||||
2022-05-17 22:00:00.000 | 2022-05-18 00:01:00.000 | 58.000000000 |
|
||||
2022-05-17 17:00:00.000 | 2022-05-17 19:13:00.000 | 58.200000000 |
|
||||
2022-05-17 12:00:00.000 | 2022-05-17 14:25:00.000 | 58.400000000 |
|
||||
2022-05-17 07:00:00.000 | 2022-05-17 09:37:00.000 | 58.600000000 |
|
||||
2022-05-17 02:00:00.000 | 2022-05-17 04:49:00.000 | 58.800000000 |
|
||||
2022-05-16 21:00:00.000 | 2022-05-17 00:01:00.000 | 59.000000000 |
|
||||
2022-05-16 16:00:00.000 | 2022-05-16 19:13:00.000 | 74.400000000 |
|
||||
2022-05-16 11:00:00.000 | 2022-05-16 14:25:00.000 | 89.800000000 |
|
||||
2022-05-16 06:00:00.000 | 2022-05-16 09:37:00.000 | 105.200000000 |
|
||||
2022-05-16 01:00:00.000 | 2022-05-16 04:49:00.000 | 120.600000000 |
|
||||
2022-05-15 20:00:00.000 | 2022-05-16 00:01:00.000 | 136.000000000 |
|
||||
2022-05-15 15:00:00.000 | 2022-05-15 18:01:00.000 | 160.500000000 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000 |
|
||||
2022-05-20 20:00:00.000 | 2022-05-21 00:01:00.000 | 11.000000000000000 |
|
||||
2022-05-20 15:00:00.000 | 2022-05-20 18:01:00.000 | 38.250000000000000 |
|
||||
2022-05-20 10:00:00.000 | 2022-05-20 12:01:00.000 | 65.500000000000000 |
|
||||
2022-05-20 05:00:00.000 | 2022-05-20 06:01:00.000 | 92.750000000000000 |
|
||||
2022-05-20 00:00:00.000 | 2022-05-20 00:01:00.000 | 120.000000000000000 |
|
||||
2022-05-19 19:00:00.000 | 2022-05-19 19:13:00.000 | 144.599999999999994 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.199999999999989 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000000011 |
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000000006 |
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000000000 |
|
||||
2022-05-18 13:00:00.000 | 2022-05-18 14:25:00.000 | 169.000000000000000 |
|
||||
2022-05-18 08:00:00.000 | 2022-05-18 09:37:00.000 | 132.000000000000000 |
|
||||
2022-05-18 03:00:00.000 | 2022-05-18 04:49:00.000 | 95.000000000000000 |
|
||||
2022-05-17 22:00:00.000 | 2022-05-18 00:01:00.000 | 58.000000000000000 |
|
||||
2022-05-17 17:00:00.000 | 2022-05-17 19:13:00.000 | 58.200000000000003 |
|
||||
2022-05-17 12:00:00.000 | 2022-05-17 14:25:00.000 | 58.399999999999999 |
|
||||
2022-05-17 07:00:00.000 | 2022-05-17 09:37:00.000 | 58.600000000000001 |
|
||||
2022-05-17 02:00:00.000 | 2022-05-17 04:49:00.000 | 58.799999999999997 |
|
||||
2022-05-16 21:00:00.000 | 2022-05-17 00:01:00.000 | 59.000000000000000 |
|
||||
2022-05-16 16:00:00.000 | 2022-05-16 19:13:00.000 | 74.400000000000006 |
|
||||
2022-05-16 11:00:00.000 | 2022-05-16 14:25:00.000 | 89.799999999999997 |
|
||||
2022-05-16 06:00:00.000 | 2022-05-16 09:37:00.000 | 105.200000000000003 |
|
||||
2022-05-16 01:00:00.000 | 2022-05-16 04:49:00.000 | 120.599999999999994 |
|
||||
2022-05-15 20:00:00.000 | 2022-05-16 00:01:00.000 | 136.000000000000000 |
|
||||
2022-05-15 15:00:00.000 | 2022-05-15 18:01:00.000 | 160.500000000000000 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000000000 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000000000 |
|
||||
|
||||
taos> explain verbose true select _wstart, first(a) as d, avg(c) from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s) order by a desc) where a > '2022-05-15 00:01:00.000' and a < '2022-05-21 00:01:08.000' interval(5h) fill(linear) order by d desc\G;
|
||||
*************************** 1.row ***************************
|
||||
|
|
@ -2673,51 +2673,51 @@ taos> select ts, c2 from d1 order by ts asc, c2 desc limit 5,5;
|
|||
taos> select _wstart, first(a) as d, avg(c) from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s) order by a desc) where a > '2022-05-15 00:01:00.000' and a < '2022-05-21 00:01:08.000' interval(5h) fill(linear) order by avg(c) desc;
|
||||
_wstart | d | avg(c) |
|
||||
================================================================================
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000 |
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.200000000 |
|
||||
2022-05-18 13:00:00.000 | 2022-05-18 14:25:00.000 | 169.000000000 |
|
||||
2022-05-15 15:00:00.000 | 2022-05-15 18:01:00.000 | 160.500000000 |
|
||||
2022-05-19 19:00:00.000 | 2022-05-19 19:13:00.000 | 144.600000000 |
|
||||
2022-05-15 20:00:00.000 | 2022-05-16 00:01:00.000 | 136.000000000 |
|
||||
2022-05-18 08:00:00.000 | 2022-05-18 09:37:00.000 | 132.000000000 |
|
||||
2022-05-16 01:00:00.000 | 2022-05-16 04:49:00.000 | 120.600000000 |
|
||||
2022-05-20 00:00:00.000 | 2022-05-20 00:01:00.000 | 120.000000000 |
|
||||
2022-05-16 06:00:00.000 | 2022-05-16 09:37:00.000 | 105.200000000 |
|
||||
2022-05-18 03:00:00.000 | 2022-05-18 04:49:00.000 | 95.000000000 |
|
||||
2022-05-20 05:00:00.000 | 2022-05-20 06:01:00.000 | 92.750000000 |
|
||||
2022-05-16 11:00:00.000 | 2022-05-16 14:25:00.000 | 89.800000000 |
|
||||
2022-05-16 16:00:00.000 | 2022-05-16 19:13:00.000 | 74.400000000 |
|
||||
2022-05-20 10:00:00.000 | 2022-05-20 12:01:00.000 | 65.500000000 |
|
||||
2022-05-16 21:00:00.000 | 2022-05-17 00:01:00.000 | 59.000000000 |
|
||||
2022-05-17 02:00:00.000 | 2022-05-17 04:49:00.000 | 58.800000000 |
|
||||
2022-05-17 07:00:00.000 | 2022-05-17 09:37:00.000 | 58.600000000 |
|
||||
2022-05-17 12:00:00.000 | 2022-05-17 14:25:00.000 | 58.400000000 |
|
||||
2022-05-17 17:00:00.000 | 2022-05-17 19:13:00.000 | 58.200000000 |
|
||||
2022-05-17 22:00:00.000 | 2022-05-18 00:01:00.000 | 58.000000000 |
|
||||
2022-05-20 15:00:00.000 | 2022-05-20 18:01:00.000 | 38.250000000 |
|
||||
2022-05-20 20:00:00.000 | 2022-05-21 00:01:00.000 | 11.000000000 |
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000000000 |
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000000006 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000000000 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000000011 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000000000 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.199999999999989 |
|
||||
2022-05-18 13:00:00.000 | 2022-05-18 14:25:00.000 | 169.000000000000000 |
|
||||
2022-05-15 15:00:00.000 | 2022-05-15 18:01:00.000 | 160.500000000000000 |
|
||||
2022-05-19 19:00:00.000 | 2022-05-19 19:13:00.000 | 144.599999999999994 |
|
||||
2022-05-15 20:00:00.000 | 2022-05-16 00:01:00.000 | 136.000000000000000 |
|
||||
2022-05-18 08:00:00.000 | 2022-05-18 09:37:00.000 | 132.000000000000000 |
|
||||
2022-05-16 01:00:00.000 | 2022-05-16 04:49:00.000 | 120.599999999999994 |
|
||||
2022-05-20 00:00:00.000 | 2022-05-20 00:01:00.000 | 120.000000000000000 |
|
||||
2022-05-16 06:00:00.000 | 2022-05-16 09:37:00.000 | 105.200000000000003 |
|
||||
2022-05-18 03:00:00.000 | 2022-05-18 04:49:00.000 | 95.000000000000000 |
|
||||
2022-05-20 05:00:00.000 | 2022-05-20 06:01:00.000 | 92.750000000000000 |
|
||||
2022-05-16 11:00:00.000 | 2022-05-16 14:25:00.000 | 89.799999999999997 |
|
||||
2022-05-16 16:00:00.000 | 2022-05-16 19:13:00.000 | 74.400000000000006 |
|
||||
2022-05-20 10:00:00.000 | 2022-05-20 12:01:00.000 | 65.500000000000000 |
|
||||
2022-05-16 21:00:00.000 | 2022-05-17 00:01:00.000 | 59.000000000000000 |
|
||||
2022-05-17 02:00:00.000 | 2022-05-17 04:49:00.000 | 58.799999999999997 |
|
||||
2022-05-17 07:00:00.000 | 2022-05-17 09:37:00.000 | 58.600000000000001 |
|
||||
2022-05-17 12:00:00.000 | 2022-05-17 14:25:00.000 | 58.399999999999999 |
|
||||
2022-05-17 17:00:00.000 | 2022-05-17 19:13:00.000 | 58.200000000000003 |
|
||||
2022-05-17 22:00:00.000 | 2022-05-18 00:01:00.000 | 58.000000000000000 |
|
||||
2022-05-20 15:00:00.000 | 2022-05-20 18:01:00.000 | 38.250000000000000 |
|
||||
2022-05-20 20:00:00.000 | 2022-05-21 00:01:00.000 | 11.000000000000000 |
|
||||
|
||||
taos> select _wstart, first(a) as d, avg(c) from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s) order by a desc) where a > '2022-05-15 00:01:00.000' and a < '2022-05-21 00:01:08.000' interval(5h) fill(linear) order by avg(c) desc limit 2;
|
||||
_wstart | d | avg(c) |
|
||||
================================================================================
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000 |
|
||||
2022-05-18 23:00:00.000 | 2022-05-19 00:01:00.000 | 243.000000000000000 |
|
||||
2022-05-15 00:00:00.000 | 2022-05-15 00:01:00.000 | 234.000000000000000 |
|
||||
|
||||
taos> select _wstart, first(a) as d, avg(c) from (select _wstart as a, last(ts) as b, avg(c2) as c from meters interval(10s) order by a desc) where a > '2022-05-15 00:01:00.000' and a < '2022-05-21 00:01:08.000' interval(5h) fill(linear) order by avg(c) desc limit 2,6;
|
||||
_wstart | d | avg(c) |
|
||||
================================================================================
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.200000000 |
|
||||
2022-05-19 04:00:00.000 | 2022-05-19 04:49:00.000 | 218.400000000000006 |
|
||||
2022-05-15 05:00:00.000 | 2022-05-15 06:01:00.000 | 209.500000000000000 |
|
||||
2022-05-18 18:00:00.000 | 2022-05-18 19:13:00.000 | 206.000000000000000 |
|
||||
2022-05-19 09:00:00.000 | 2022-05-19 09:37:00.000 | 193.800000000000011 |
|
||||
2022-05-15 10:00:00.000 | 2022-05-15 12:01:00.000 | 185.000000000000000 |
|
||||
2022-05-19 14:00:00.000 | 2022-05-19 14:25:00.000 | 169.199999999999989 |
|
||||
|
||||
taos> select last(ts), c2 as d from d1 group by c2 order by c2 desc limit 10;
|
||||
last(ts) | d |
|
||||
|
|
|
|||
|
|
@ -0,0 +1,266 @@
|
|||
import taos
|
||||
import sys
|
||||
import time
|
||||
import socket
|
||||
import os
|
||||
import threading
|
||||
import math
|
||||
|
||||
from util.log import *
|
||||
from util.sql import *
|
||||
from util.cases import *
|
||||
from util.dnodes import *
|
||||
from util.common import *
|
||||
# from tmqCommon import *
|
||||
|
||||
class TDTestCase:
|
||||
def __init__(self):
|
||||
self.vgroups = 4
|
||||
self.ctbNum = 10
|
||||
self.rowsPerTbl = 10000
|
||||
self.duraion = '1h'
|
||||
|
||||
def init(self, conn, logSql, replicaVar=1):
|
||||
self.replicaVar = int(replicaVar)
|
||||
tdLog.debug(f"start to excute {__file__}")
|
||||
tdSql.init(conn.cursor(), False)
|
||||
|
||||
def create_database(self,tsql, dbName,dropFlag=1,vgroups=2,replica=1, duration:str='1d'):
|
||||
if dropFlag == 1:
|
||||
tsql.execute("drop database if exists %s"%(dbName))
|
||||
|
||||
tsql.execute("create database if not exists %s vgroups %d replica %d duration %s"%(dbName, vgroups, replica, duration))
|
||||
tdLog.debug("complete to create database %s"%(dbName))
|
||||
return
|
||||
|
||||
def create_stable(self,tsql, paraDict):
|
||||
colString = tdCom.gen_column_type_str(colname_prefix=paraDict["colPrefix"], column_elm_list=paraDict["colSchema"])
|
||||
tagString = tdCom.gen_tag_type_str(tagname_prefix=paraDict["tagPrefix"], tag_elm_list=paraDict["tagSchema"])
|
||||
sqlString = f"create table if not exists %s.%s (%s) tags (%s)"%(paraDict["dbName"], paraDict["stbName"], colString, tagString)
|
||||
tdLog.debug("%s"%(sqlString))
|
||||
tsql.execute(sqlString)
|
||||
return
|
||||
|
||||
def create_ctable(self,tsql=None, dbName='dbx',stbName='stb',ctbPrefix='ctb',ctbNum=1,ctbStartIdx=0):
|
||||
for i in range(ctbNum):
|
||||
sqlString = "create table %s.%s%d using %s.%s tags(%d, 'tb%d', 'tb%d', %d, %d, %d)" % \
|
||||
(dbName,ctbPrefix,i+ctbStartIdx,dbName,stbName,(i+ctbStartIdx) % 5,i+ctbStartIdx,i+ctbStartIdx,i+ctbStartIdx,i+ctbStartIdx,i+ctbStartIdx)
|
||||
tsql.execute(sqlString)
|
||||
|
||||
tdLog.debug("complete to create %d child tables by %s.%s" %(ctbNum, dbName, stbName))
|
||||
return
|
||||
|
||||
def insert_data(self,tsql,dbName,ctbPrefix,ctbNum,rowsPerTbl,batchNum,startTs,tsStep):
|
||||
tdLog.debug("start to insert data ............")
|
||||
tsql.execute("use %s" %dbName)
|
||||
pre_insert = "insert into "
|
||||
sql = pre_insert
|
||||
|
||||
for i in range(ctbNum):
|
||||
rowsBatched = 0
|
||||
sql += " %s%d values "%(ctbPrefix,i)
|
||||
for j in range(rowsPerTbl):
|
||||
if (i < ctbNum/2):
|
||||
sql += "(%d, %d, %d, %d,%d,%d,%d,true,'binary%d', 'nchar%d') "%(startTs + j*tsStep, j%10, j%10, j%10, j%10, j%10, j%10, j%10, j%10)
|
||||
else:
|
||||
sql += "(%d, %d, NULL, %d,NULL,%d,%d,true,'binary%d', 'nchar%d') "%(startTs + j*tsStep, j%10, j%10, j%10, j%10, j%10, j%10)
|
||||
rowsBatched += 1
|
||||
if ((rowsBatched == batchNum) or (j == rowsPerTbl - 1)):
|
||||
tsql.execute(sql)
|
||||
rowsBatched = 0
|
||||
if j < rowsPerTbl - 1:
|
||||
sql = "insert into %s%d values " %(ctbPrefix,i)
|
||||
else:
|
||||
sql = "insert into "
|
||||
if sql != pre_insert:
|
||||
tsql.execute(sql)
|
||||
tdLog.debug("insert data ............ [OK]")
|
||||
return
|
||||
|
||||
def prepareTestEnv(self):
|
||||
tdLog.printNoPrefix("======== prepare test env include database, stable, ctables, and insert data: ")
|
||||
paraDict = {'dbName': 'test',
|
||||
'dropFlag': 1,
|
||||
'vgroups': 2,
|
||||
'stbName': 'meters',
|
||||
'colPrefix': 'c',
|
||||
'tagPrefix': 't',
|
||||
'colSchema': [{'type': 'INT', 'count':1},{'type': 'BIGINT', 'count':1},{'type': 'FLOAT', 'count':1},{'type': 'DOUBLE', 'count':1},{'type': 'smallint', 'count':1},{'type': 'tinyint', 'count':1},{'type': 'bool', 'count':1},{'type': 'binary', 'len':10, 'count':1},{'type': 'nchar', 'len':10, 'count':1}],
|
||||
'tagSchema': [{'type': 'INT', 'count':1},{'type': 'nchar', 'len':20, 'count':1},{'type': 'binary', 'len':20, 'count':1},{'type': 'BIGINT', 'count':1},{'type': 'smallint', 'count':1},{'type': 'DOUBLE', 'count':1}],
|
||||
'ctbPrefix': 't',
|
||||
'ctbStartIdx': 0,
|
||||
'ctbNum': 100,
|
||||
'rowsPerTbl': 10000,
|
||||
'batchNum': 3000,
|
||||
'startTs': 1537146000000,
|
||||
'tsStep': 600000}
|
||||
|
||||
paraDict['vgroups'] = self.vgroups
|
||||
paraDict['ctbNum'] = self.ctbNum
|
||||
paraDict['rowsPerTbl'] = self.rowsPerTbl
|
||||
|
||||
tdLog.info("create database")
|
||||
self.create_database(tsql=tdSql, dbName=paraDict["dbName"], dropFlag=paraDict["dropFlag"], vgroups=paraDict["vgroups"], replica=self.replicaVar, duration=self.duraion)
|
||||
|
||||
tdLog.info("create stb")
|
||||
self.create_stable(tsql=tdSql, paraDict=paraDict)
|
||||
|
||||
tdLog.info("create child tables")
|
||||
self.create_ctable(tsql=tdSql, dbName=paraDict["dbName"], \
|
||||
stbName=paraDict["stbName"],ctbPrefix=paraDict["ctbPrefix"],\
|
||||
ctbNum=paraDict["ctbNum"],ctbStartIdx=paraDict["ctbStartIdx"])
|
||||
self.insert_data(tsql=tdSql, dbName=paraDict["dbName"],\
|
||||
ctbPrefix=paraDict["ctbPrefix"],ctbNum=paraDict["ctbNum"],\
|
||||
rowsPerTbl=paraDict["rowsPerTbl"],batchNum=paraDict["batchNum"],\
|
||||
startTs=paraDict["startTs"],tsStep=paraDict["tsStep"])
|
||||
return
|
||||
|
||||
def check_first_rows(self, all_rows, limited_rows, offset: int = 0):
|
||||
for i in range(0, len(limited_rows) - 1):
|
||||
if limited_rows[i] != all_rows[i + offset]:
|
||||
tdLog.info("row: %d, row in all: %s" % (i+offset+1, str(all_rows[i+offset])))
|
||||
tdLog.info("row: %d, row in limted: %s" % (i+1, str(limited_rows[i])))
|
||||
tdLog.exit("row data check failed")
|
||||
tdLog.info("all rows are the same as query without limit..")
|
||||
|
||||
def query_and_check_with_slimit(self, sql: str, max_limit: int, step: int, offset: int = 0):
|
||||
self.query_and_check_with_limit(sql, max_limit, step, offset, ' slimit ')
|
||||
|
||||
def query_and_check_with_limit(self, sql: str, max_limit: int, step: int, offset: int = 0, limit_str: str = ' limit '):
|
||||
for limit in range(0, max_limit, step):
|
||||
limited_sql = sql + limit_str + str(offset) + "," + str(limit)
|
||||
tdLog.info("query with sql: %s " % (sql) + limit_str + " %d,%d" % (offset, limit))
|
||||
all_rows = tdSql.getResult(sql)
|
||||
limited_rows = tdSql.getResult(limited_sql)
|
||||
tdLog.info("all rows: %d, limited rows: %d" % (len(all_rows), len(limited_rows)))
|
||||
if limit_str == ' limit ':
|
||||
if limit + offset <= len(all_rows) and len(limited_rows) != limit:
|
||||
tdLog.exit("limited sql has less rows than limit value which is not right, \
|
||||
limit: %d, limited_rows: %d, all_rows: %d, offset: %d" % (limit, len(limited_rows), len(all_rows), offset))
|
||||
elif limit + offset > len(all_rows) and offset < len(all_rows) and offset + len(limited_rows) != len(all_rows):
|
||||
tdLog.exit("limited sql has less rows than all_rows which is not right, \
|
||||
limit: %d, limited_rows: %d, all_rows: %d, offset: %d" % (limit, len(limited_rows), len(all_rows), offset))
|
||||
elif offset >= len(all_rows) and len(limited_rows) != 0:
|
||||
tdLog.exit("limited rows should be zero, \
|
||||
limit: %d, limited_rows: %d, all_rows: %d, offset: %d" % (limit, len(limited_rows), len(all_rows), offset))
|
||||
|
||||
self.check_first_rows(all_rows, limited_rows, offset)
|
||||
|
||||
def test_interval_limit_asc(self, offset: int = 0):
|
||||
sqls = ["select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from meters interval(1s) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from meters interval(1m) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from meters interval(1h) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from meters interval(1d) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from t1 interval(1s) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from t1 interval(1m) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from t1 interval(1h) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), first(ts) from t1 interval(1d) "]
|
||||
for sql in sqls:
|
||||
self.query_and_check_with_limit(sql, 5000, 500, offset)
|
||||
|
||||
def test_interval_limit_desc(self, offset: int = 0):
|
||||
sqls = ["select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from meters interval(1s) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from meters interval(1m) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from meters interval(1h) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from meters interval(1d) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from t1 interval(1s) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from t1 interval(1m) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from t1 interval(1h) ",
|
||||
"select _wstart, _wend, count(*), sum(c1), avg(c2), last(ts) from t1 interval(1d) "]
|
||||
for sql in sqls:
|
||||
self.query_and_check_with_limit(sql, 5000, 500, offset)
|
||||
|
||||
def test_interval_limit_offset(self):
|
||||
for offset in range(0, 1000, 500):
|
||||
self.test_interval_limit_asc(offset)
|
||||
self.test_interval_limit_desc(offset)
|
||||
self.test_interval_fill_limit(offset)
|
||||
self.test_interval_order_by_limit(offset)
|
||||
self.test_interval_partition_by_slimit(offset)
|
||||
|
||||
def test_interval_fill_limit(self, offset: int = 0):
|
||||
sqls = [
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-09-17 09:30:00.000' interval(1s) fill(linear)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-09-17 09:30:00.000' interval(1m) fill(linear)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-09-17 09:30:00.000' interval(1h) fill(linear)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-09-17 09:30:00.000' interval(1d) fill(linear)"
|
||||
]
|
||||
for sql in sqls:
|
||||
self.query_and_check_with_limit(sql, 5000, 1000, offset)
|
||||
|
||||
def test_interval_order_by_limit(self, offset: int = 0):
|
||||
sqls = [
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) order by b",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) order by a desc",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), last(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) order by a desc",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) order by count(*), sum(c1), a",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) order by a, count(*), sum(c1)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) fill(linear) order by b",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) fill(linear) order by a desc",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) fill(linear) order by a desc",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) fill(linear) order by count(*), sum(c1), a",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), avg(c2), first(ts) from meters \
|
||||
where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' interval(1m) fill(linear) order by a, count(*), sum(c1)",
|
||||
]
|
||||
for sql in sqls:
|
||||
self.query_and_check_with_limit(sql, 6000, 2000, offset)
|
||||
|
||||
def test_interval_partition_by_slimit(self, offset: int = 0):
|
||||
sqls = [
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts) from meters "
|
||||
"where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' partition by t1 interval(1m)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts) from meters "
|
||||
"where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' partition by t1 interval(1h)",
|
||||
"select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts) from meters "
|
||||
"where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' partition by c3 interval(1m)",
|
||||
]
|
||||
for sql in sqls:
|
||||
self.query_and_check_with_slimit(sql, 10, 2, offset)
|
||||
|
||||
def test_interval_partition_by_slimit_limit(self):
|
||||
sql = "select * from (select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts),c3 from meters " \
|
||||
"where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' partition by c3 interval(1m) slimit 10 limit 2) order by c3 asc"
|
||||
tdSql.query(sql)
|
||||
tdSql.checkRows(20)
|
||||
tdSql.checkData(0, 4, 0)
|
||||
tdSql.checkData(1, 4, 0)
|
||||
tdSql.checkData(2, 4, 1)
|
||||
tdSql.checkData(3, 4, 1)
|
||||
tdSql.checkData(18, 4, 9)
|
||||
tdSql.checkData(19, 4, 9)
|
||||
|
||||
sql = "select * from (select _wstart as a, _wend as b, count(*), sum(c1), last(c2), first(ts),c3 from meters " \
|
||||
"where ts >= '2018-09-17 09:00:00.000' and ts <= '2018-10-17 09:30:00.000' partition by c3 interval(1m) slimit 2,2 limit 2) order by c3 asc"
|
||||
tdSql.query(sql)
|
||||
tdSql.checkRows(4)
|
||||
tdSql.checkData(0, 4, 2)
|
||||
tdSql.checkData(1, 4, 2)
|
||||
tdSql.checkData(2, 4, 9)
|
||||
tdSql.checkData(3, 4, 9)
|
||||
|
||||
def run(self):
|
||||
self.prepareTestEnv()
|
||||
self.test_interval_limit_offset()
|
||||
self.test_interval_partition_by_slimit_limit()
|
||||
|
||||
def stop(self):
|
||||
tdSql.close()
|
||||
tdLog.success(f"{__file__} successfully executed")
|
||||
|
||||
event = threading.Event()
|
||||
|
||||
tdCases.addLinux(__file__, TDTestCase())
|
||||
tdCases.addWindows(__file__, TDTestCase())
|
||||
Loading…
Reference in New Issue