Google Git
Sign in
apache / trafodion / refs/tags/2.3.0rc1 / . / core / sql / optimizer / SimpleScanOptimizer.cpp
blob: 4f4d00734818684f2c55f1a79a03c79482d99c86 [file] [log] [blame]
/**********************************************************************
// @@@ START COPYRIGHT @@@
//
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
//
// @@@ END COPYRIGHT @@@
**********************************************************************/
/* -*-C++-*-
*****************************************************************************
*
* File: SimpleScanOptimizer.cpp
* Description: Simplified Costing for Scan operators
* Created: 04/2003
* Language: C++
*
*****************************************************************************
*/
#include "SimpleScanOptimizer.h"
#include "AllRelExpr.h"
#ifdef DEBUG
#define SFSOWARNING(x) fprintf(stdout, "SimpleFileScan optimizer warning: %s\n", x);
#else
#define SFSOWARNING(x)
#endif
// Get any extra key predicates from the partitioning function. The
// partitioning function will provide extra key predicates when it
// represents logical sub-partitioning.
//
const ValueIdSet &
SimpleFileScanOptimizer::getPartitioningKeyPredicates() const
{
const LogPhysPartitioningFunction *logPhysPartFunc =
getContext().getPlan()->getPhysicalProperty()->getPartitioningFunction()->
castToLogPhysPartitioningFunction();
CMPASSERT(logPhysPartFunc);
return logPhysPartFunc->getPartitioningKeyPredicates();
}
// getNumBlocksForRows()
// Compute the number of blocks needed to hold the given number of
// rows for this scan. Assume that the rows are dense (no
// fragmentation or slack). Assume that the subset starts on a block
// boundary resulting in possibly one partial block at the end of the
// subset (This is why there is a 'getCeiling' included). This may
// underestimate the number of blocks by 1, if the subset starts
// mid-block.
//
CostScalar
SimpleFileScanOptimizer::getNumBlocksForRows(const CostScalar &numRows) const
{
// RMW should a '+1' be added to this to account for partial blocks at
// both ends of the subset?
//
return (numRows/getEstimatedRecordsPerBlock()).getCeiling();
}
// getResultSetCardinalityPerScan()
// Compute the estimated number of rows per scan (active partition),
// based on the result set cardinality computed outside of the Scan
// Optimizer and the number of active partitions for this Scan.
//
CostScalar
SimpleFileScanOptimizer::getResultSetCardinalityPerScan() const
{
return (getResultSetCardinality()/getEstNumActivePartitionsAtRuntime()).getCeiling();
}
// getRowsInDP2Buffer()
// Compute the number of rows of this Scan that will fit in a DP2 Buffer.
//
CostScalar
SimpleFileScanOptimizer::getRowsInDP2Buffer() const
{
// Default size of a DP2 Message Buffer (in KBytes).
//
const CostScalar dp2MessageBufferSizeInKb
(CostPrimitives::getBasicCostFactor(DP2_MESSAGE_BUFFER_SIZE));
// rows that fit in dp2 buffer:
return
(dp2MessageBufferSizeInKb / getRecordSizeInKb()).getFloor();
}
SimpleFileScanOptimizer::SimpleFileScanOptimizer(
const FileScan& assocFileScan
,const CostScalar& resultSetCardinality
,const Context& myContext
,const ValueIdSet &externalInputs) :
ScanOptimizer(assocFileScan
,resultSetCardinality
,myContext
,externalInputs),
// Cache some values that are used often
//
recordSizeInKb_(assocFileScan.getIndexDesc()->getRecordSizeInKb()),
blockSizeInKb_(assocFileScan.getIndexDesc()->getBlockSizeInKb()),
estimatedRecordsPerBlock_(assocFileScan.getIndexDesc()->
getEstimatedRecordsPerBlock()),
// Will be calculated during computeSingleSubsetSize()
//
singleSubsetSize_(0),
singleSubsetPreds_(),
// Will be constructed during constructSearchKey()
// This search key is not owned by this object and will therefore
// not be destroyed when the destructor of this object is called.
// This search key will be returned to the caller
//
searchKey_(NULL),
// Will be calculated during categorizeMultiProbes().
//
// For MultiProbe Scans
//
failedProbes_(0),
indexBlocksLowerBound_(0),
blksPerSuccProbe_(0),
keyPrefixUEC_(1),
partialOrderProbes_(FALSE),
dataRows_(0),
effectiveTotalRowCount_(0),
totalRowCount_(0)
{
}
SimpleFileScanOptimizer::~SimpleFileScanOptimizer()
{
// Do not destroy the SearchKey pointed to by the searchKey_
// pointer, it is being returned to the caller.
}
// Optimize
// Construct a Search Key (MDAM or Single Subset)
// Produce a Cost object for this scan and context.
// Compute the number of blocks to read per access.
//
Cost *
SimpleFileScanOptimizer::optimize(SearchKey*& searchKeyPtr /* out */
, MdamKey*& mdamKeyPtr /* out */
)
{
DCMPASSERT(searchKeyPtr == NULL);
DCMPASSERT(mdamKeyPtr == NULL);
// For now, the simple scan optimizer only handles single-subsets
// (not MDAM). When MDAM is supported, will have to add logic here
// to detect the need to do MDAM costing.
// Construct the single subset search key.
//
searchKeyPtr = constructSearchKey();
// Compute the size of this single subset and cache the size and the
// single subset predicates in local datamembers.
//
computeSingleSubsetSize();
// Compute the cost for this single subset
if ( CmpCommon::getDefault(SIMPLE_COST_MODEL) == DF_ON )
return scmComputeCostForSingleSubset();
else
return computeCostForSingleSubset();
} // SimpleFileScanOptimizer::optimize()
// Does the partitioning function associated with this scan represent
// logical sub-partitioning.
//
NABoolean
SimpleFileScanOptimizer::isLogicalSubPartitioned() const
{
const LogPhysPartitioningFunction *logPhysPartFunc =
getContext().getPlan()->getPhysicalProperty()->getPartitioningFunction()->
castToLogPhysPartitioningFunction();
if ( logPhysPartFunc != NULL )
{
LogPhysPartitioningFunction::logPartType
logPartType = logPhysPartFunc->getLogPartType();
if (logPartType == LogPhysPartitioningFunction::LOGICAL_SUBPARTITIONING ||
logPartType == LogPhysPartitioningFunction::HORIZONTAL_PARTITION_SLICING) {
return TRUE;
}
}
return FALSE;
}
// Construct the Search Key (MDAM or Single Subset)
// (for now just the Single subset is considered)
// Return pointer to search key and cache in local datamember.
//
SearchKey *
SimpleFileScanOptimizer::constructSearchKey()
{
// We do not need to incude the flatten version of RANGE SPEC predicates
// to the exePreds because SearchKey is capable of handling such predicates.
// In addition, we do not want to confuse the SearchKey::ini() method
// about what are the original predicates and what are derived predicates,
// since combing these two forms will confuse SearchKey::makeHBaseSearchKey()
// and cause the missing of the end keys (JIRA1449).
ValueIdSet exePreds(getRelExpr().getSelectionPred());
const Disjuncts *curDisjuncts = &(getDisjuncts());
if(isLogicalSubPartitioned()) {
exePreds += getPartitioningKeyPredicates();
curDisjuncts = new(CmpCommon::statementHeap()) MaterialDisjuncts(exePreds);
}
ValueIdSet nonKeyColumnSet;
getIndexDesc()->getNonKeyColumnSet(nonKeyColumnSet);
SearchKey *searchKeyPtr = new(CmpCommon::statementHeap())
SearchKey(getIndexDesc()->getIndexKey()
,getIndexDesc()->getOrderOfKeyValues()
,getExternalInputs()
,isForwardScan()
,exePreds
,*curDisjuncts
,nonKeyColumnSet
,getIndexDesc()
);
searchKey_ = searchKeyPtr;
return searchKeyPtr;
} // SimpleFileScanOptimizer::constructSearchKey()
// Compute the single subset size and predicates and cache the values in
// local datamembers
//
void
SimpleFileScanOptimizer::computeSingleSubsetSize()
{
singleSubsetSize_ = 0;
singleSubsetPreds_ = getSearchKey()->getKeyPredicates();
if (getSearchKey()->isUnique()) {
// If the searchKey is unique, then by definition, the size is 1
//
singleSubsetSize_ = 1;
} else if(getSearchKey()->getExecutorPredicates().entries() == 0) {
// If there are no executor predicates, then the single subset
// will not be reduced any further. Therefore, the single subset
// represents the result of this scan. We have already estimated
// the result set size, so use it here.
//
singleSubsetSize_ = getResultSetCardinality();
} else {
// Otherwise, we must compute the single subset size.
//
ColumnOrderList keyPredsByCol(getIndexDesc()->getIndexKey());
getSearchKey()->getKeyPredicatesByColumn(keyPredsByCol);
CollIndex singleSubsetPrefixColumn;
NABoolean itIsSingleSubset =
keyPredsByCol.getSingleSubsetOrder(singleSubsetPrefixColumn);
if(getSearchKey()->getKeyPredicates().entries() > 0) {
singleSubsetPreds_.clear();
for (CollIndex pred = 0; pred <= singleSubsetPrefixColumn; pred++)
{
const ValueIdSet *preds = keyPredsByCol[pred];
if(preds) {
singleSubsetPreds_ += *preds;
}
}
IndexDescHistograms innerHistograms(*getIndexDesc(),
singleSubsetPrefixColumn + 1);
const SelectivityHint * selHint = getFileScan().getSelectivityHint();
const CardinalityHint * cardHint = getFileScan().getCardinalityHint();
// Exclude the added computed predicates since they do not contribute
// to the cardinality.
ValueIdSet predicatesToUse(singleSubsetPreds_);
predicatesToUse -= getFileScan().getComputedPredicates();
innerHistograms.applyPredicates(predicatesToUse, getRelExpr(), selHint, cardHint, REL_SCAN);
// Now, compute the number of rows:
singleSubsetSize_ = innerHistograms.getRowCount();
} else {
IndexDescHistograms innerHistograms(*getIndexDesc(),
singleSubsetPrefixColumn + 1);
// A full table scan.
singleSubsetSize_ = innerHistograms.getRowCount();
}
}
} // SimpleFileScanOptimizer::computeSingleSubsetSize()
// computeNumberOfBlocksToReadPerAccess()
// Estimate the number of blocks that DP2 needs to read per access for
// this Scan. This value is captured by the FileScan Node and passed
// to DP2 by the executor, DP2 uses it to decide whether it will do
// read ahead or not.
//
// Side-Affects - sets the numberOfBlocksToReadPerAccess_ data member
// of ScanOptimizer.
//
void
SimpleFileScanOptimizer::computeNumberOfBlocksToReadPerAccess(
FileScanBasicCost *basicCostPtr)
{
const CostScalar numKBytes(basicCostPtr->getSingleSubsetNumKBytes());
// KB for index blocks plus data blocks for one partition for all
// probes:
CostScalar blocksToRead
((numKBytes/getBlockSizeInKb()).getCeiling());
Lng32 indexBlocks = MAXOF(getIndexDesc()->getIndexLevels(),1);
// Substract index blocks. minCsOne is another sanity check.
blocksToRead =(blocksToRead - indexBlocks).minCsOne();
// Store in ScanOptimizer object. FileScan will later grab this
// value.
//
setNumberOfBlocksToReadPerAccess(blocksToRead);
} // SimpleFileScanOptimizer::computeNumberOfBlocksToReadPerAccess(...)
// computeCostForSingleSubset()
// Compute the Cost for this single subset Scan.
//
// Attempts to find an existing basic cost object which can be reused.
//
// Computes or reuses the first row and last row cost
// vectors. (IOTime, CPUTime, SeekIOTime, IdleTime, numProbes)
//
// OUTPUTS:
// Return - A NON-NULL Cost* representing the Cost for this scan node
//
// Side-Affects - computes and sets the numberOfBlocksToReadPerAccess_
// data member of ScanOptimizer. This value will be captured by the
// FileScan node and passed to DP2 by the executor, DP2 uses it to
// decide whether it will do read ahead or not.
//
Cost*
SimpleFileScanOptimizer::computeCostForSingleSubset()
{
FileScanBasicCost *basicCostPtr;
// Either get a shared basic cost object or create a new empty one.
//
NABoolean sharedCost = getSharedCostObject(basicCostPtr);
// Determine if this scan is receiving multiple probes. If so,
// use the MultiProbe Scan Optimizer methods. Other wise use the
// single probe methods.
//
CostScalar repeatCount =
getContext().getPlan()->getPhysicalProperty()->
getDP2CostThatDependsOnSPP()->getRepeatCountForOperatorsInDP2();
NABoolean multiProbeScan = repeatCount.isGreaterThanOne() OR
(getContext().getInputLogProp()->getColStats().entries() > 0);
if(!sharedCost) {
// If the basic cost object is newly created (not shared), we must
// recompute the cost vectors. (IOTime, CPUTime, SeekIOTime,
// IdleTime, numProbes)
//
if ( multiProbeScan )
{
computeCostVectorsMultiProbes(basicCostPtr);
} else {
computeCostVectors(basicCostPtr);
}
}
else
{ // Determine whether probes order matches the scan's clustering
// order. For the first time when BasicCost is computed the order
// requirement could be different. There fore in case we reuse
// BasicCost we need to doublecheck the need for synchronous access.
// Synchronous access flag and BasicCost are orthogonal and we
// shouldn't save this flag with BasicCost.
// We need to do this only for multiProbeScan. For single probe
// (in case of merge joins) synchronous access will be
// forced implicitly by using the number of partition of logical
// part.function. (see the logic in computeCostObject() ).
if ( multiProbeScan )
{
const InputPhysicalProperty* ippForMe =
getContext().getInputPhysicalProperty();
NABoolean partiallyInOrderOK = TRUE;
NABoolean probesForceSynchronousAccess = FALSE;
if ((ippForMe != NULL) AND
SimpleFileScanOptimizer::ordersMatch(ippForMe,
getIndexDesc(),
&(getIndexDesc()->getOrderOfKeyValues()),
getRelExpr().getGroupAttr()->getCharacteristicInputs(),
partiallyInOrderOK,
probesForceSynchronousAccess))
{
setInOrderProbesFlag(TRUE);
setProbesForceSynchronousAccessFlag(probesForceSynchronousAccess);
}
else if ((ippForMe != NULL) AND
(ippForMe->getAssumeSortedForCosting()) AND
(!(ippForMe->getExplodedOcbJoinForCosting())) AND
(ippForMe->getNjOuterOrderPartFuncForNonUpdates() == NULL))
{
// assumeSortedForCosting_ flag is set for two cases:
// case 1: when input is rowset.
// case 2: when left child partFunc njOuterPartFuncForNonUpdates_
// is passed for NJ plan 0. This is only for cost estimation
// of exchange operator and not scan operator.
// So we should come here only for case1.
// To avoid case2, we check njOuterPartFuncForNonUpdates_ for NULL.
setInOrderProbesFlag(TRUE);
}
else
{
setInOrderProbesFlag(FALSE);
}
}
}
SimpleCostVector &firstRow = basicCostPtr->getFRBasicCostSingleSubset();
SimpleCostVector &lastRow = basicCostPtr->getLRBasicCostSingleSubset();
// Compute the Cost object for the physical scan from the simple
// cost vectors for the first and last row.
//
if (CURRSTMT_OPTDEFAULTS->incorporateSkewInCosting())
{
// compute multiplicative factor = probesAtBusiestStream/ProbesPerScan_
// Multiply the last row cost by the factor
// Note that the last row cost is per Partition
CostScalar probesAtBusyStream
= getContext().getPlan()->getPhysicalProperty()->
getDP2CostThatDependsOnSPP()->
getProbesAtBusiestStream();
CostScalar probesPerScan = lastRow.getNumProbes();
// probes must be > 0 if not assert.
DCMPASSERT(probesPerScan.isGreaterThanZero());
probesPerScan = MAXOF(probesPerScan, 1);
CostScalar multiplicativeFactor =
(probesAtBusyStream/probesPerScan).minCsOne();
lastRow.setCPUTime(lastRow.getCPUTime() * multiplicativeFactor);
lastRow.setIOTime(lastRow.getIOTime() * multiplicativeFactor);
}
// OCB Cost Adjustment for KEYCOLS_NOT_COVERED
CostScalar ocbAdjFactor = (ActiveSchemaDB()->getDefaults())\
.getAsDouble(OCB_COST_ADJSTFCTR);
DP2CostDataThatDependsOnSPP *dp2CostInfo =
(DP2CostDataThatDependsOnSPP *)(getContext().getPlan()->getPhysicalProperty())-> getDP2CostThatDependsOnSPP();
SimpleCostVector lastRowC = lastRow;
lastRowC.setCPUTime(lastRowC.getCPUTime() * ocbAdjFactor);
lastRowC.setIOTime(lastRowC.getIOTime() * ocbAdjFactor);
if( ( multiProbeScan ) && ( CmpCommon::getDefault(COMP_BOOL_53) == DF_ON )
&& ( getContext().getReqdPhysicalProperty()->getOcbEnabledCostingRequirement() )
&& (dp2CostInfo != NULL)
&& (isLowerBound(firstRow,lastRowC))) {
switch(dp2CostInfo->getRepeatCountState())
{
case DP2CostDataThatDependsOnSPP::KEYCOLS_NOT_COVERED:
lastRow.setCPUTime(lastRowC.getCPUTime());
lastRow.setIOTime(lastRowC.getIOTime());
break;
default:
break;
}
}
Cost *costPtr = computeCostObject(firstRow, lastRow);
DCMPASSERT(costPtr);
// Estimate the number of blocks that DP2 needs to read per access for
// this Scan.
computeNumberOfBlocksToReadPerAccess(basicCostPtr);
// Store estimated dp2 rows accessed, this will be used in generator.
setEstRowsAccessed(basicCostPtr->getEstRowsAccessed());
return costPtr;
} // SimpleFileScanOptimizer::computeCostForSingleSubset()
// getSharedCostObject()
// Determine if a shareable BasicCost object is available and usable.
//
// If a usable shared basic cost object is available, return it as is.
//
// If an unusable shared basic cost object is available, clear it and
// return it as a nonshared basic cost object.
//
// If a shared basic cost object is not available, create a new one
// and return it as a nonshared basic cost object.
//
// OUTPUTS:
// Return - NABoolean - TRUE if a usable shared BasicCost object was available.
// FALSE if a nonusable or new BasicCost object.
// *&basicCostPtr - set to the BasicCost object (shared or new)
//
NABoolean
SimpleFileScanOptimizer::getSharedCostObject(FileScanBasicCost *&basicCostPtr)
{
NABoolean sharedCostFound = FALSE;
// Look for an appropriate shared BasicCost object, if none is
// found, create a new empty one.
//
basicCostPtr = shareBasicCost(sharedCostFound);
SimpleCostVector &firstRow = basicCostPtr->getFRBasicCostSingleSubset();
SimpleCostVector &lastRow = basicCostPtr->getLRBasicCostSingleSubset();
// If a share BasicCost object was found ..
//
if (sharedCostFound) {
// If it is shared and usable, return it as a shared BasicCost
// object.
//
if (firstRow.getCPUTime() > csZero AND
lastRow.getCPUTime() > csZero AND
CURRSTMT_OPTDEFAULTS->reuseBasicCost() )
{
return TRUE;
}
else
// Otherwise, clear it so that it can be reused as an empty
// nonshared BasicCost object.
//
{
firstRow.reset();
lastRow.reset();
return FALSE;
}
}
// Otherwise, a new BasicCost object was created, return it as a
// nonshared BasicCost object.
//
return FALSE;
} // SimpleFileScanOptimizer::getSharedCostObject()
// SimpleFileScanOptimizer::estimateNumberOfSeeksPerScan()
// Estimate the of seeks required for this single subset per scan
// (partition). Include the seeks required for accessing the index
// blocks.
//
// Need to Seek to every index level except the root, plus need
// to seek to first data block.
//
// Even if there are no rows expected in the result set, the
// seeks still need to be performed.
//
// Do we need to seek twice this amount, once for the begin key
// and once for the end key??????
// This is a very good point. From DP2 it is known now that instead
// of using IOBlocksToReadPerAccess as ScanOptimizer presumes
// DP2 ignores it end use end key to search for the end of data blocks
//
// Returns: the number of seeks required for a single subset access to
// a partition (this is equal to the number of index levels plus sone
// extra in case of logical subpartitioning.).
//
CostScalar
SimpleFileScanOptimizer::estimateNumberOfSeeksPerScan(const CostScalar &seqKB) const
{
// indexLevels is the max index levels of all partitions.
// Assume the worst case
//
Lng32 indexBlocks = MAXOF(getIndexDesc()->getIndexLevels(),1);
CostScalar seeks(indexBlocks);
// We need an adjustment to the number of seeks in case of large
// sequential scan. Usually it is not possible to read the whole
// partition without interruption from system or concurrent DP2
// operations. Previously this adjustment was done only for the
// case of logical sub-partitioning when 2 ESPs are accessing
// the same partition.
// Assume that DP2 will not switch after every read but on
// average after every DP2_SEQ_READS_WITHOUT_SEEKS (internal CQD)
// reads. We will ignore adjustment if it is smaller than 1 seek.
NADefaults &defs = ActiveSchemaDB()->getDefaults();
ULng32 maxDp2ReadInKB =
defs.getAsULong(DP2_MAX_READ_PER_ACCESS_IN_KB);
ULng32 seqReadsBeforeSwitch =
defs.getAsULong(DP2_SEQ_READS_WITHOUT_SEEKS);
seeks += (seqKB/maxDp2ReadInKB/seqReadsBeforeSwitch).getFloor();
// temporary patch for solution 10-041104-1450. To make hash join
// look more expensive that nested join if right table is not very
// small we add one seek to the right scan.
if ( seqKB > 8.0 )
{
seeks++;
}
return seeks;
} // SimpleFileScanOptimizer::estimateNumberOfSeeksPerScan(...)
// SimpleFileScanOptimizer::estimateSeqKBReadPerScan()
// Estimate the amount (in KBytes) of data read per scan (partition).
// Include the index blocks which must be read.
//
// Need to read every index level except the root (always in cache?)
//
// Even if there are no rows expected in the result set, the index
// blocks still need to be read.
//
// Do we need to read (some of) the index blocks twice, once for the begin key
// and once for the end key??????
//
// Returns: the number of sequential KB read for a single subset access to
// a partition (this includes index and data blocks)
//
CostScalar
SimpleFileScanOptimizer::estimateSeqKBReadPerScan() const
{
// Compute the index blocks touched:
//
// indexLevels is the max index levels of all partitions.
// Assume at least 1 block (root) needs to be read.
//
Lng32 indexBlocks = MAXOF(getIndexDesc()->getIndexLevels(),1);
// For single subset, all rows are contiguous. Get the number of KB
// required to hold the expected result. Assumes rows are dense (no
// fragmentation or slack)
//
CostScalar blocksRead(getNumBlocksForRows(getSingleSubsetSize()));
// Assume that the blocks are evenly distributed over the active
// partitions.
//
blocksRead = (blocksRead / getNumActivePartitions()).getCeiling();
// Include the indexBlocks in the blocks to be read.
// indexBlocks is already 'per scan' so no need to divide by NumActParts
//
blocksRead = blocksRead + indexBlocks;
// Covert Blocks to KB
//
return blocksRead * getBlockSizeInKb();
} // SimpleFileScanOptimizer::estimateSeqKBReadPerScan()
// SimpleFileScanOptimizer::computeCostVectors()
// Computes the cost vectors (First and Last Row) for this scan.
// Computes:
// - KBytes
// - CPU Instructions
// - Number of seeks
// - Idle time
// - number of probes
// for both cost vectors.
//
// OUTPUTS: firstRow and lastRow SimpleCostVectors of the BasicCost
// object are populated.
//
void
SimpleFileScanOptimizer::computeCostVectors(FileScanBasicCost *basicCostPtr)
const
{
SimpleCostVector &firstRow = basicCostPtr->getFRBasicCostSingleSubset();
SimpleCostVector &lastRow = basicCostPtr->getLRBasicCostSingleSubset();
CostScalar seqKBFR;
CostScalar seqKBLR;
// Compute the sequential KBytes accessed to produce the first and
// last rows.
estimateKB(seqKBFR, seqKBLR);
firstRow.addKBytesToIOTime(seqKBFR);
lastRow.addKBytesToIOTime(seqKBLR);
// Record the total KB accessed in the BasicCost object.
//
basicCostPtr->setSingleSubsetNumKBytes(seqKBLR);
basicCostPtr->setEstRowsAccessed(getSingleSubsetSize());
// Estimate the of seeks required for this single subset per scan
// (partition). Include the seeks required for accessing the index
// blocks.
//
const CostScalar seeksPerScanFR(estimateNumberOfSeeksPerScan(seqKBFR));
const CostScalar seeksPerScan(estimateNumberOfSeeksPerScan(seqKBLR));
firstRow.addSeeksToIOTime(seeksPerScanFR);
lastRow.addSeeksToIOTime(seeksPerScan);
const CostScalar selectedRowsPerScan(getResultSetCardinalityPerScan());
const CostScalar rowsPerScan
((getSingleSubsetSize()/getEstNumActivePartitionsAtRuntime()).getCeiling());
// Estimate minimum number of rows that must be processed in order
// to get the first row. This is typically the number of rows that
// will fit in a DP2 buffer. If there are fewer rows than this in
// the result, then only that many rows need to be processed. This
// will be used to compute the CPU instruction required to produce
// the first row.
//
const CostScalar dp2Rows(getRowsInDP2Buffer());
const CostScalar rowsPerScanFR(MINOF(dp2Rows, rowsPerScan));
const CostScalar selectedRowsPerScanFR (MINOF(dp2Rows, selectedRowsPerScan));
CostScalar cpuInstructionsFR;
CostScalar cpuInstructionsLR;
// Estimate CPU Instructions (includes Per Request, Per Row and Per
// KB number of CPU Instructions) for First and Last row accessed.
//
estimateCPUInstructions(seeksPerScan,
rowsPerScanFR,
rowsPerScan,
selectedRowsPerScanFR,
selectedRowsPerScan,
seqKBFR,
seqKBLR,
cpuInstructionsFR,
cpuInstructionsLR);
firstRow.addInstrToCPUTime(cpuInstructionsFR);
lastRow.addInstrToCPUTime(cpuInstructionsLR);
// Estimate the amount of Idle time used by this Scan. Includes
// time to open secondary partitions (all but the first).
//
const CostScalar idleTime(estimateIdleTime());
firstRow.addToIdleTime(idleTime);
lastRow.addToIdleTime(idleTime);
// For single subset, non-nested join, there is only one probe.
//
firstRow.setNumProbes(csOne);
lastRow.setNumProbes(csOne);
} // SimpleFileScanOptimizer::computeCostVectors(...)
// SimpleFileScanOptimizer::estimateKB()
// Compute the sequential KBytes accessed to produce the first and
// last rows.
//
// OUTPUTS:
// seqKBFR - The number of KBytes to be accessed to produce the first row.
// seqKBLR - The number of KBytes to be accessed to produce the last row.
//
void
SimpleFileScanOptimizer::estimateKB(CostScalar &seqKBFR,
CostScalar &seqKBLR) const
{
// total number of blocks to be read sequentially In order to
// produce the last row, need to read all required blocks.
//
seqKBLR = estimateSeqKBReadPerScan();
// Estimate minimum number of KB that must be processed in order to
// get the first row. This is typically the size in KB of a DP2
// buffer. If the result contains fewer KB than this, then
// only the result needs to be processed.
//
CostScalar dp2BufferSize
(CostPrimitives::getBasicCostFactor(DP2_MESSAGE_BUFFER_SIZE));
// Number of KB required to produce the first row
//
seqKBFR = MINOF(dp2BufferSize, seqKBLR);
} // SimpleFileScanOptimizer::estimateKB()
// SimpleFileScanOptimizer::estimateCPUInstructions()
// Estimate CPU Instructions (includes Per Request, Per Row and Per KB
// number of CPU Instructions) for First and Last row accessed.
//
// Inputs:
//
// seeksPerScan - The number of seeks to be performed by this Scan
// (single partition).
//
// rowsPerScanFR - The number of rows accessed by this scan before
// returning the first row (single partition)
//
// rowsPerScanLR - The number of rows accessed by this scan (single
// partition)
//
// seqKBFR - The number of KBytes to be accessed by this Scan to
// produce the first row (single partition).
//
// seqKBLR - The number of KBytes to be accessed by this Scan to
// produce the last row (single partition).
//
// Outputs:
// cpuInstructionsFR - Number of CPU instructions to access the First row.
// cpuInstructionsLR - Number of CPU instructions to access the Last row.
//
void
SimpleFileScanOptimizer::estimateCPUInstructions(
const CostScalar &seeksPerScan,
const CostScalar &rowsPerScanFR,
const CostScalar &rowsPerScanLR,
const CostScalar &selectedRowsPerScanFR,
const CostScalar &selectedRowsPerScanLR,
const CostScalar &seqKBFR,
const CostScalar &seqKBLR,
CostScalar &cpuInstructionsFR,
CostScalar &cpuInstructionsLR) const
{
// Estimate per-request CPU instructions. This includes
// performing a binary search of each index block to find the
// appropriate entry.
//
CostScalar instrPerReqst(estimateCPUPerReqstInstrs());
// Add in the per data request overhead cost paid by the cpu
//
instrPerReqst += CostPrimitives::getBasicCostFactor(CPUCOST_DATARQST_OVHD);
// Estimate the per-row CPU instructions. This includes the
// instructions necessary to copy the row from DP2 to the EXEINDP2.
//
const CostScalar instrPerRow(estimateCPUPerRowInstrs());
// Estimate per-seek CPU instructions. This is the extra cost
// necessary for each seek. For now this is just a default value.
// Commented out because it was never used
//const CostScalar instrPerSeek(estimateCPUPerSeekInstrs());
// Estimate per-KB CPU instructions. This is the extra cost
// necessary to copy each KB from DP2 to the EXEINDP2. For now this
// is just a default value.
//
const CostScalar instrPerKB(estimateCPUPerKBInstrs());
// Estimate the number of instruction reqquired to evaluate the
// Executor predicates on one row.
//
const CostScalar instrPerExePredPerRow(estimateCPUExePredPerRowInstrs());
// First Row CPU instructions.
//
cpuInstructionsFR =
instrPerReqst
//+ (instrPerSeek * seeksPerScan)
+ (instrPerKB * seqKBFR)
+ (instrPerRow * selectedRowsPerScanFR)
+ (instrPerExePredPerRow * rowsPerScanFR);
// Last Row CPU instructions.
//
cpuInstructionsLR =
instrPerReqst
//+ (instrPerSeek * seeksPerScan)
+ (instrPerKB * seqKBLR)
+ (instrPerRow * selectedRowsPerScanLR)
+ (instrPerExePredPerRow * rowsPerScanLR);
// Sanity Check
//
DCMPASSERT(cpuInstructionsFR <= cpuInstructionsLR);
} // SimpleFileScanOptimizer::estimateCPUInstructions(...)
// estimateCPUPerReqstInstrs()
// Estimate the number of CPU instructions used for each request.
// This includes performing a binary search of each index block,
// comparing the keys (byte compare of encoded keys) to find the
// appropriate entry. And includes the default per request overhead.
//
// Return - the number of CPU instruction required to search the index
// blocks to find the appropriate data block for this request.
//
CostScalar
SimpleFileScanOptimizer::estimateCPUPerReqstInstrs() const
{
// We do a binary search after we get the data block in memory to
// find the matching row. So in worst case scenario it would just be
// ln(recordsPerBlock). log function will give us the natural
// logarithm. So, to get logX = lnX/log2 1.44 * lnX.
// RMW - Shouldn't this use the number of records per index block
// which could be quite different
// RMW - Shouldn't this use the key size in bytes rather than the
// number of key columns.
// RMW - Shouldn't this use the cost of byte compare (not pred compare)
//
/*
CostScalar instrPerReqst =
( 1.44 * log((getEstimatedRecordsPerBlock()).getValue())
* getIndexDesc()->getIndexKey().entries()
* CostPrimitives::getBasicCostFactor(CPUCOST_PREDICATE_COMPARISON)
* getIndexDesc()->getIndexLevels() );
*/
// Change according to Bob's comments. There is a small
// overestimation because of data block having less rows that index
// block but we don't need very high accuracy here.
// Do we need to add syskey processing in case of clustering non-
// unique index? SP. Do we want to keep entriesPerIndexBlocks as
// a separate attribute?
const IndexDesc * index = getIndexDesc();
const double avrgSlack = 0.7;
// Assume 8 bytes per key overhead (pointer to low level block)
// we'' use ceiling because block with K key would have K+1 pointers)
CostScalar entriesPerIndexBlock = ( index->getBlockSizeInKb() *
1024 * avrgSlack / (8 + index->getKeyLength())).getCeiling();
CostScalar instrPerReqst =
( // number of comparisons per index blocks
1.44 * log(entriesPerIndexBlock.getValue()) *
// comparison cost, will need a special CQD later
index->getKeyLength() * CostPrimitives::
getBasicCostFactor(CPUCOST_COMPARE_COMPLEX_DATA_TYPE_PER_BYTE)
// number of index blocks to go through
* index->getIndexLevels()
);
return instrPerReqst;
} // SimpleFileScanOptimizer::estimateCPUPerReqstInstrs()
// estimateCPUPerRowInstrs()
// Estimate the number of CPU instructions used to copy each row from
// DP2 to the EXEINDP2.
//
// Return - the number of CPU instruction required to copy each row from
// DP2 to the EXEINDP2.
//
CostScalar
SimpleFileScanOptimizer::estimateCPUPerRowInstrs() const
{
// Currently we don't copy rows from DP2Cache to EID. We only
// copy selected row columns required by charOutput. Using
// record size in KB which is file table record size could
// cause huge overestimation of CPU cost for Scan.
// Do we want to count only data we really copy? SP.
// ------------------------------------------------------------
// The per row overhead, regardless of the row size
//
CostScalar instrPerRowMovedOutOfTheCache
(CostPrimitives::getBasicCostFactor(CPUCOST_COPY_ROW_OVERHEAD) +
CostPrimitives::getBasicCostFactor(CPUCOST_SCAN_OVH_PER_ROW));
// The number of CPU instructions dependent on the row size.
//
instrPerRowMovedOutOfTheCache +=
// previously recordSizeInKb*(copyCostPerByte*1024+ovhPerKB)
getRelExpr().getGroupAttr()->getRecordLength() *
((CostPrimitives::getBasicCostFactor(CPUCOST_COPY_ROW_PER_BYTE))
+ CostPrimitives::getBasicCostFactor(CPUCOST_SCAN_OVH_PER_KB)/1024);
return instrPerRowMovedOutOfTheCache;
} // SimpleFileScanOptimizer::estimateCPUPerRowInstrs()
// estimateCPUExePredPerRowInstrs()
// Estimate the number of CPU instructions required to evaluate the
// executor predicate on one row.
//
// Return - the number of CPU instruction required to evaluate the
// executor predicate on one row.
//
// The number of CPU instructions is estimated by counting the number
// of operators in the executor predicates.
// The following ItemExprs are counted as operators:
// - The implied AND operators of the ValueIdSet
// - Non-Leaf ItemExprs
// - VEG Predicates
//
CostScalar
SimpleFileScanOptimizer::estimateCPUExePredPerRowInstrs() const
{
// A copy of the executor predicates
//
ValueIdSet exePreds = getSearchKey()->getExecutorPredicates();
NADefaults &defs = ActiveSchemaDB()->getDefaults();
// fsoToUse -
// IF 0 - Use original "Complex" File Scan Optimizer. (Default)
// IF 1 - Use logic below to determine FSO to use.
// IF 2 - Use logic below to determine FSO to use, but also use new
// executor predicate costing.
// IF >2 - Always use new "Simple" File Scan Optimizer.
//
ULng32 fsoToUse = defs.getAsULong(FSO_TO_USE);
// Should we use the old way of costing predicates?
//
if (fsoToUse < 2) {
return (exePreds.entries() *
CostPrimitives::getBasicCostFactor(CPUCOST_PREDICATE_COMPARISON));
}
// Used to keep track of values to examine in next iteration
//
ValueIdSet children;
// Count the number of operators (non-leaf) in the predicate
//
ULng32 numOps = 0;
// Account for the implied AND operators in the ValueIdSet
//
if(exePreds.entries() > 1) {
numOps = (exePreds.entries() - 1) * 2;
}
// Iterate over the levels of the executor predicates. exePreds
// will contain the value Ids for all ItemExprs at a given level of
// the expression tree.
//
while(exePreds.entries()) {
// Look at each expression at this level.
//
for(ValueId valId=exePreds.init();
exePreds.next(valId);
exePreds.advance(valId)) {
ItemExpr *oper = valId.getItemExpr();
Int32 numChildren = oper->getArity();
if(numChildren == 0 &&
(oper->getOperatorType() == ITM_VEG_PREDICATE )) {
// If this is a VEGPred count it as an operator.
//
// This could be simply a NOT NULL test
// or an equality pred
// or a tree of equality preds
// or nothing.
// For a scan (and that is what we have here), this is most likely a
// NOT NULL predicate or nothing.
//
numOps++;
} else if (numChildren > 0) {
// If this is a non-leaf node, count this as an operator
//
numOps += numChildren;
// Schedule all the children for examination
//
for(Int32 i = 0; i < numChildren; i++) {
children += oper->child(i)->getValueId();
}
}
}
exePreds = children;
children.clear();
}
// Compute cost for executor predicates Assume each operator has the
// same cost, the cost of a single predicate comparison
//
return
(CostPrimitives::getBasicCostFactor(CPUCOST_PREDICATE_COMPARISON ) *
numOps) / 2;
} // SimpleFileScanOptimizer::estimateCPUExePredPerRowInstrs()
// estimateCPUPerSeekInstrs()
// Estimate the per-seek CPU instructions. This is the extra CPU cost
// necessary for each seek. For now this is just a default value.
//
//CostScalar
//SimpleFileScanOptimizer::estimateCPUPerSeekInstrs() const
//{
// Add a per seek cost for the cost of moving
// data from disk to DP2:
//
// return CostPrimitives::getBasicCostFactor(CPUCOST_SCAN_DSK_TO_DP2_PER_SEEK);
//}
// estimateCPUPerKBInstrs()
// Estimate per-KB CPU instructions. This is the extra CPU cost
// necessary to copy each KB from DP2 to the EXEINDP2. For now this
// is just a default value.
//
CostScalar
SimpleFileScanOptimizer::estimateCPUPerKBInstrs() const
{
// Add a per kb cost for the cost of moving
// every block from disk to DP2:
return CostPrimitives::getBasicCostFactor(CPUCOST_SCAN_DSK_TO_DP2_PER_KB);
}
// estimateIdleTime()
// Estimate the amount of Idle time used by this Scan.
//
// CPUCOST_SUBSET_OPEN lumps together all the overhead needed to
// set-up the access to each partition. Thus it is a blocking cost,
// nothing can overlap with it. Since scans are not blocking, by
// definition, put the cost in idle time: this is the cost for opening
// all the partitions but the first partition. The first partition is
// opened before the ScanOptimizer is called. During opening the first
// partition, the necessary info on the file is also acquired so it is
// more expensive and cannot be overlaid. Root accounts for the cost
// of opening the first partition of all the tables.
//
CostScalar
SimpleFileScanOptimizer::estimateIdleTime() const
{
// Default cost to open subsequent partitions.
//
CostScalar idleTime
(CostPrimitives::getBasicCostFactor(CPUCOST_SUBSET_OPEN_AFTER_FIRST));
// Open all Partitions, but the first.
//
idleTime = idleTime * MAXOF(0, (getNumActivePartitions() - 1));
// Convert from CPU instructions to seconds.
// (MSCF_ET_CPU is the cpu-to-time multiplier)
//
idleTime = idleTime * CostPrimitives::getBasicCostFactor(MSCF_ET_CPU);
return idleTime;
}
// Return the repeat Count for this scan. The repeat count is an
// estimate of the number of probes that this scan (partition) will
// receive.
// A return value of >= 1.0 indicates that this is a multiprobe scan
// (below a nested join).
// A return value of csZero (0.0) indicates that this is a single
// probe scan.
//
CostScalar
SimpleFileScanOptimizer::getRepeatCount() const
{
CostScalar repeatCount = getContext().getPlan()->getPhysicalProperty()->
getDP2CostThatDependsOnSPP()->getRepeatCountForOperatorsInDP2();
if ((repeatCount > csOne) OR
(getContext().getInputLogProp()->getColStats().entries() > 0)) {
// indicate that it is multiprobe;
return repeatCount;
} else {
// Indicates that this is single-probe.
return csZero;
}
}
CostScalar SimpleFileScanOptimizer::getProbeCacheCostAdjFactor() const
{
CostScalar probeCacheCostAdj = csOne;
Lng32 cacheEntries = (ActiveSchemaDB()->getDefaults()).getAsLong(GEN_PROBE_CACHE_NUM_ENTRIES);
Lng32 downCacheSize = (ActiveSchemaDB()->getDefaults()).getAsLong(GEN_PROBE_CACHE_SIZE_DOWN);
// upper bound cost adjustment as a saftey net
CostScalar ubound = (ActiveSchemaDB()->getDefaults()).getAsDouble(NCM_NJ_PC_THRESHOLD);
// skip probe cache cost adj if threshold is more than 100%
// way of disabling fix for testing purpose
if (ubound > 1.0)
return probeCacheCostAdj;
// ignore cacheEntries if it is smaller than downCacheSize.
cacheEntries = MAXOF(cacheEntries, downCacheSize);
// get number of streams (ESPs)
Lng32 countOfStreams =
getContext().getPlan()->getPhysicalProperty()->getCurrentCountOfCPUs();
CostScalar dupSuccProbes = getDuplicateSuccProbes();
CostScalar totalProbes = getProbes();
CostScalar uniqProbes = getUniqueProbes();
// borderline case, if all probes are duplicate, meaning only one value,
// we still need to make one IO for the first probe.
if (dupSuccProbes == totalProbes)
dupSuccProbes = dupSuccProbes - csOne;
CostScalar probeCacheRatio = dupSuccProbes / totalProbes;
CostScalar probeCacheHitRatio;
// compute cache hit ratio. Full benefit for order probes case.
if ( getInOrderProbesFlag() )
probeCacheHitRatio = csOne;
else // Random order case
{
// determine if we can safely divide probes by DOP.
// when probecache column is part of partition key, we can safely divide.
NABoolean safeToDivide = FALSE;
// get access to probecache column, which is nothing but char.inputs
ValueIdSet myCharInputs = getRelExpr().getGroupAttr()->getCharacteristicInputs();
// get access to partition function of my sibling via IPP.
const InputPhysicalProperty* ippForMe = getContext().getInputPhysicalProperty();
if (ippForMe != NULL)
{
const PartitioningFunction* ch0PartFunc = NULL;
// first try plan0 location, if it's not there try location of other plans
ch0PartFunc = ippForMe->getNjOuterOrderPartFuncForNonUpdates();
if (ch0PartFunc == NULL)
ch0PartFunc = ippForMe->getNjOuterOrderPartFunc();
ValueIdSet ch0PartKey;
if (ch0PartFunc != NULL)
ch0PartKey = ch0PartFunc->getPartitioningKey();
// if either probe cache column or left child partkey is empty, we can't
// divide
if (NOT(myCharInputs.isEmpty() OR ch0PartKey.isEmpty()))
// if my char.input contains child0 PartKey, we can safely divide
if (myCharInputs.contains(ch0PartKey))
safeToDivide = TRUE;
}
// if cache is too small to hold all unique entries,
// compute fraction of probes that will get a cache hit
// (conservative estimate, assuming no ordering of probes
// and no gain from the countOfStream parallel caches)
if (safeToDivide)
uniqProbes = uniqProbes.value() / countOfStreams;
probeCacheHitRatio = (cacheEntries / uniqProbes.value());
probeCacheHitRatio.maxCsOne();
}
// apply upper bound threshold, currently 100% cost adjustment
probeCacheRatio = probeCacheRatio * probeCacheHitRatio * ubound;
probeCacheCostAdj = (1 - probeCacheRatio.value());
return probeCacheCostAdj;
}
// This method is different from NestedJoin::isProbeCacheApplicable
// in a sense that it returns true only if RHS of NJ is file_scan_unique.
// Basically we don't want to apply cost adjustment for semi and anti_semi joins
NABoolean SimpleFileScanOptimizer::isProbeCacheApplicable() const
{
// right child is file scan unique
// probes are duplicate
// NJ probe cache CQD is ON
if ( getSearchKey()->isUnique() &&
(duplicateSuccProbes_ > 0) &&
(CmpCommon::getDefault(NESTED_JOIN_CACHE) == DF_ON) )
return TRUE;
else
return FALSE;
}
// SimpleFileScanOptimizer::categorizeMultiProbes()
// Computes and caches the following metrics regarding probes:
//
// probes_ - the total number of probes for all active partitions.
// this could be different from outer probes - result cardinality
// of InputLogPtoperties. The fan-out of each outer probe -
// the average number of active partitions one outer probe goes
// to should be taken into account when estimating uniqueSuccProbes
// and uniqueFailedProbes
//
// successfulProbes_ - the number of probes (probes_) for all AP
// that produce some data.
//
// uniqueProbes_ - the number of distinct probes (probes_) for all
// AP. Includes successful and failed probes.
//
// duplicateSuccProbes_ - the number of successful probes
// (successfulProbes_) for all AP that are not unique.
// duplicateSuccProbes = successfulProbes - uniqueSuccProbes.
//
// failedProbes_ - the number of probes (probes_) that do not result
// in data. failedProbes = probes - successfulProbes.
//
// dataRows_ - the number of rows accessed by all successful probes.
//
// blksPerSuccProbe_ - the estimated number of blocks produced by
// each successful probe on average.
//
// inOrderProbesFlag - indicates if the probes arrive in order
// relative to the clustering key of the access path.
//
// Return Value: void - Computed values are cached in data members.
//
void
SimpleFileScanOptimizer::categorizeMultiProbes(NABoolean *isAnIndexJoin)
{
// first get the histograms for the probes:
//
Histograms
outerHistograms(getContext().getInputLogProp()->getColStats());
// Costing works by estimating the resources of the whole, unpartitioned
// table, thus getting the total probes by multiplying by the active
// partitions. This is because to estimate successful and failed probes
// we need to use histograms that provide statistics for the table in
// the whole. Then total resources spent will be divided by the number
// of AP, cost vector is estimated for 1 AP only. Finally, depending on
// the level of parallelism, number of CPUs executing DP2s, and
// synchronous/asynchronous access to APs the cost object will be
// computed for the whole Scan. This logic results in lots of confusion
// in the current implementation. It is absolutely critical to understand
// at each step are we calculating something for all APs, for one
// partition only or for the group of partitions providing results for
// the same ESP.
//
if (CmpCommon::getDefault(NCM_HBASE_COSTING) == DF_ON)
probes_ = (getRepeatCount() *
getEstNumActivePartitionsAtRuntimeForHbaseRegions()).minCsOne();
else
probes_ = (getRepeatCount() * getEstNumActivePartitionsAtRuntime()).minCsOne();
// all the probes that don't have duplicates
//
CostScalar uniqueSuccProbes(csZero);
CostScalar uniqueFailedProbes(csZero);
ValueIdSet singleSubsetPreds(getSingleSubsetPreds());
categorizeProbes(successfulProbes_ // out
,uniqueSuccProbes // out
,duplicateSuccProbes_ // out
,failedProbes_ // out
,uniqueFailedProbes // out
,probes_
,singleSubsetPreds
,outerHistograms
,FALSE // this is not MDAM!
,&dataRows_
,isAnIndexJoin
);
uniqueProbes_ = uniqueSuccProbes + uniqueFailedProbes;
// -----------------------------------------------------------
// Compute the rows and blks per successful probe:
// -----------------------------------------------------------
CostScalar rowsPerSuccessfulProbe(csZero);
if (successfulProbes_ >= csOne)
{
rowsPerSuccessfulProbe = dataRows_/successfulProbes_;
}
blksPerSuccProbe_ =
(rowsPerSuccessfulProbe / getEstimatedRecordsPerBlock()).getCeiling();
// No successful probe can grab less than one block:
//
if ((successfulProbes_ >= csOne) && (blksPerSuccProbe_ < csOne))
{
blksPerSuccProbe_ = csOne;
}
// Determine whether probes order matches the scan's clustering
// order:
//
const InputPhysicalProperty* ippForMe =
getContext().getInputPhysicalProperty();
NABoolean partiallyInOrderOK = TRUE;
NABoolean probesForceSynchronousAccess = FALSE;
if ((ippForMe != NULL) AND
SimpleFileScanOptimizer::ordersMatch(ippForMe,
getIndexDesc(),
&(getIndexDesc()->getOrderOfKeyValues()),
getRelExpr().getGroupAttr()->getCharacteristicInputs(),
partiallyInOrderOK,
probesForceSynchronousAccess))
{
setInOrderProbesFlag(TRUE);
setProbesForceSynchronousAccessFlag(probesForceSynchronousAccess);
}
else if ((ippForMe != NULL) AND
(ippForMe->getAssumeSortedForCosting()) AND
(!(ippForMe->getExplodedOcbJoinForCosting())) AND
(ippForMe->getNjOuterOrderPartFuncForNonUpdates() == NULL))
{
// assumeSortedForCosting_ flag is set for two cases:
// case 1: when input is rowset.
// case 2: when left child partFunc njOuterPartFuncForNonUpdates_
// is passed for NJ plan 0. This is only for cost estimation
// of exchange operator and not scan operator.
// So we should come here only for case1.
// To avoid case2, we check njOuterPartFuncForNonUpdates_ for NULL.
setInOrderProbesFlag(TRUE);
}
else
{
setInOrderProbesFlag(FALSE);
}
} // SimpleFileScanOptimizer::categorizeMultiProbes()
// -----------------------------------------------------------------------
// INPUT:
// probes: the total probes coming in
// preds: the predicates to compute the join
//
// OUTPUT:
// successfuleProbes: those probes that match any data
// uniqueSuccProbes: those successful probes that do not have duplicates
// duplicateSuccProbes: successfulProbes - uniqueSuccProbes
// failedProbes: probes - successfulProbes
// -----------------------------------------------------------------------
void
SimpleFileScanOptimizer::categorizeProbes(CostScalar& successfulProbes /* out */
,CostScalar& uniqueSuccProbes /* out */
,CostScalar& duplicateSuccProbes /* out */
,CostScalar& failedProbes /* out */
,CostScalar& uniqueFailedProbes /* out */
,const CostScalar& probes
,const ValueIdSet& preds
,const Histograms& outerHistograms
,const NABoolean isMDAM
,CostScalar * dataRows
,NABoolean *isAnIndexJoin
) const
{
CostScalar numOuterProbes =
(getContext().getInputLogProp())->getResultCardinality();
if (outerHistograms.isEmpty())
{
// In general, this should not happen. Even for cross product outer
// histogram should contain some information about columns in outer
// table characteristic output. One known case is when VEG of equi-join
// predicate contains a const, then outerHistograms are not created.
// Probably, it is worth to revisit this and still generate histograms,
// they could be useful later. Currently, let's have a special case
// here. We'd like to find any other special cases and treat them
// separately. We'll generate assert in debug compiler if the case
// is not known yet. For release compiler we will return all probes
// as successful.
NABoolean allPredHaveConstExpr = TRUE;
for( ValueId predId=preds.init();
preds.next(predId); preds.advance(predId))
{
const ItemExpr *pred = predId.getItemExpr();
if (((VEGPredicate *)pred)->getOperatorType() == ITM_VEG_PREDICATE)
{
const VEG * predVEG = ((VEGPredicate *)pred)->getVEG();
const ValueIdSet & VEGGroup = predVEG->getAllValues();
if (VEGGroup.referencesAConstExpr())
continue;
else
{
allPredHaveConstExpr = FALSE;
break;
}
}
}//end of check for A Constant Expression
if (allPredHaveConstExpr)
{
successfulProbes = probes;
if (CmpCommon::getDefault(NCM_HBASE_COSTING) == DF_ON)
uniqueSuccProbes = getEstNumActivePartitionsAtRuntimeForHbaseRegions();
else
uniqueSuccProbes = getNumActivePartitions();
duplicateSuccProbes = probes - uniqueSuccProbes;
failedProbes = uniqueFailedProbes = csZero;
*dataRows = numOuterProbes * getEffectiveTotalRowCount();
return;
}
// other special cases will be put here
successfulProbes = uniqueSuccProbes = probes;
duplicateSuccProbes = failedProbes = csZero;
// This will assert debug compiler when comp_bool_38 is ON
// to run regression and skip this assert for debug
// compiler this CQD needs to be set to OFF (default)
//
if ( CmpCommon::getDefault(COMP_BOOL_38) == DF_ON )
{
SFSOWARNING("Outer histogram is empty for multiprob scan(NJ)");
DCMPASSERT(0);
}
}
else
{
// If there are input values we are in a nested join case,
// else it must be a cartesian product:
const ValueIdSet& inputValues =
getRelExpr().getGroupAttr()->getCharacteristicInputs();
// get this filescan statistics:
IndexDescHistograms
joinedHistograms(*getIndexDesc(),
getIndexDesc()->getIndexKey().entries() );
// Since there are input values, apply the join preds
// to estimate the failed probes:
// Apply the join predicates to the local statistics:
joinedHistograms.applyPredicatesWhenMultipleProbes(
preds
,*(getContext().getInputLogProp())
,inputValues
,isMDAM
,NULL
,NULL
,isAnIndexJoin
,REL_SCAN);
failedProbes = csZero; // assume no probes will fail
if (NOT inputValues.isEmpty())
{
if (NOT preds.isEmpty())
{
// and compute the failed probes:
const ValueIdSet& operatorValues =
getIndexDesc()->getIndexKey();
failedProbes =
joinedHistograms.computeFailedProbes(outerHistograms
,preds
,inputValues
,operatorValues
);
if(CmpCommon::getDefault(COMP_BOOL_110) == DF_ON)
{
DCMPASSERT(probes >= failedProbes);
}
failedProbes = MINOF(probes,failedProbes);
}
} // if there are input values
if(dataRows) *dataRows = joinedHistograms.getRowCount();
// ------------------------------------------------------------------
// Compute successful probes:
// -------------------------------------------------------------------
successfulProbes = probes - failedProbes;
// --------------------------------------------------------------
// Compute unique probes:
// -------------------------------------------------------------
// Combine unique entry count of the probes:
// (assume independence of columns and compute
// Compute combined UEC of outer histograms):
CostScalar uniqueEntryCountOfProbes = csOne;
CostScalar histUEC;
ValueIdSet columnsOfVegPreds;
for( ValueId predId=preds.init();
preds.next(predId); preds.advance(predId))
{
const VEGPredicate *vegPred =
(VEGPredicate *)predId.getItemExpr();
if ( vegPred->getOperatorType() == ITM_VEG_PREDICATE )
{
columnsOfVegPreds += vegPred->getVEG()->getAllValues();
}
}
for (CollIndex i=0; i < outerHistograms.entries(); i++)
{
if ( columnsOfVegPreds.contains(
outerHistograms[i].getColumn()) OR
columnsOfVegPreds.contains(
outerHistograms[i].getVEGColumn()) OR
inputValues.contains(
outerHistograms[i].getVEGColumn())
)
{
histUEC = outerHistograms[i].getColStats()
->getTotalUec().getCeiling();
uniqueEntryCountOfProbes *= histUEC;
}
}
//should not be more than # of probes
if (uniqueEntryCountOfProbes > probes)
uniqueEntryCountOfProbes = probes;
// should not be less than 1
uniqueEntryCountOfProbes.minCsOne();
// The unique successful probes are the successful
// fraction of their UEC:
uniqueSuccProbes = MINOF(
((successfulProbes/numOuterProbes)*uniqueEntryCountOfProbes),
successfulProbes);
uniqueFailedProbes = MINOF(
((failedProbes/numOuterProbes)*uniqueEntryCountOfProbes),
failedProbes );
// --------------------------------------------------------------
// Compute successful probes that are duplicates of another
// successful probe:
// --------------------------------------------------------------
duplicateSuccProbes = successfulProbes - uniqueSuccProbes;
}
} // SimpleFileScanOptimizer::categorizeProbes(...)
// Estimate the number of different index blocks visited for
// MultiProbe Scan. This method will use the number of unique probes
// and total key length of index columns instead of universal "rule
// of thumb" from IndexDesc::getEstimatedIndexBlocksLowerBound()
void SimpleFileScanOptimizer::estimateIndexBlockUsageMultiProbeScan()
{
const IndexDesc * index = getIndexDesc();
Lng32 curLevel = index->getIndexLevels();
const double avrgSlack = 0.7;
CostScalar leafProbesPerScan =
(uniqueProbes_ / getNumActivePartitions()).getCeiling();
CostScalar innerBlocksPerScan = (getEffectiveTotalRowCount()/
getEstimatedRecordsPerBlock()/getNumActivePartitions()).getCeiling();
// Assume 8 bytes per key overhead (pointer to low level block)
// we'' use ceiling because block with K key would have K+1 pointers)
CostScalar entriesPerIndexBlock = (index->getBlockSizeInKb() * 1024 /
(8 + index->getKeyLength()) * avrgSlack).getCeiling();
// to prevent looping in while
if ( entriesPerIndexBlock.isGreaterThanOne() )
{
// Will not have more pointers than inner blocks per Scan (divided
// by entriesPerIndexBlocs give the number of index leaf blocks).
CostScalar currentLevelIndexBlocks = MINOF(leafProbesPerScan,
innerBlocksPerScan/entriesPerIndexBlock).getCeiling();
CostScalar totalIndexBlocks = currentLevelIndexBlocks;
while( --curLevel > 0 )
{
currentLevelIndexBlocks =
(currentLevelIndexBlocks / entriesPerIndexBlock).getCeiling();
totalIndexBlocks += currentLevelIndexBlocks;
}
indexBlocksLowerBound_ = totalIndexBlocks;
}
else
{
// This shouldn't happen but we need to set some big value for rls
// compiler if it did.
indexBlocksLowerBound_ = innerBlocksPerScan;
DCMPASSERT(0);
}
} // SimpleScanOptimizer::estimateIndexBlockUsageMultiProbeScan()
// SimpleFileScanOptimizer::computeCostVectorsMultiProbe()
// Computes the cost vectors (First and Last Row) for this scan.
// Computes:
// - KBytes
// - CPU Instructions
// - Number of seeks
// - Idle time
// - number of probes
// for both cost vectors.
//
// OUTPUTS: firstRow and lastRow SimpleCostVectors of the BasicCost
// object are populated.
//
void
SimpleFileScanOptimizer::computeCostVectorsMultiProbes(FileScanBasicCost *basicCostPtr)
{
// Effective Total Row Count is the size of the bounding subset of
// all probes. Typically this will be all the rows of the table,
// but if all probes are restricted to a subset of rows (e.g. the
// key predicate contains leading constants) then the effective row
// count will be less than the total row count.
//
estimateEffTotalRowCount(totalRowCount_, effectiveTotalRowCount_);
categorizeMultiProbes();
// Adjustments for Partial order case.
// Divide right child total rows and left child probes by the by the total
// uec of the right child key prefix.
CostScalar oldEffectiveTotalRowCount;;
CostScalar oldSuccessfulProbes;
CostScalar oldDuplicateSuccProbes;
CostScalar oldFailedProbes;
CostScalar oldUniqueProbes;
CostScalar oldProbes;
if (getPartialOrderProbesFlag())
{
oldEffectiveTotalRowCount = effectiveTotalRowCount_;
effectiveTotalRowCount_ =
(effectiveTotalRowCount_/keyPrefixUEC_).getCeiling();
oldSuccessfulProbes = successfulProbes_;
successfulProbes_ = (successfulProbes_/keyPrefixUEC_).getCeiling();
oldDuplicateSuccProbes = duplicateSuccProbes_;
duplicateSuccProbes_ = (duplicateSuccProbes_/keyPrefixUEC_).getCeiling();
oldFailedProbes = failedProbes_;
failedProbes_ = (failedProbes_/keyPrefixUEC_).getCeiling();
oldUniqueProbes = uniqueProbes_;
uniqueProbes_ = (uniqueProbes_/keyPrefixUEC_).getCeiling();
oldProbes = probes_;
probes_ = (probes_/keyPrefixUEC_).getCeiling();
}
estimateIndexBlockUsageMultiProbeScan();
SimpleCostVector &firstRow = basicCostPtr->getFRBasicCostSingleSubset();
SimpleCostVector &lastRow = basicCostPtr->getLRBasicCostSingleSubset();
CostScalar seqKBFR;
CostScalar seqKBLR;
// Compute the sequential KBytes accessed to produce the first and
// last rows.
estimateKBMultiProbe(seqKBFR, seqKBLR);
seqKBLR = seqKBLR * keyPrefixUEC_;
firstRow.addKBytesToIOTime(seqKBFR);
lastRow.addKBytesToIOTime(seqKBLR);
// Record the total KB accessed in the BasicCost object.
//
basicCostPtr->setSingleSubsetNumKBytes(seqKBLR);
basicCostPtr->setEstRowsAccessed(getDataRows());
// Estimate the of seeks required for this single subset per scan
// (partition). Include the seeks required for accessing the index
// blocks.
//
CostScalar seeksLR(estimateNumberOfSeeksPerScanMultiProbe());
CostScalar seeksFR(seeksLR/getProbes());
seeksFR = seeksFR.getCeiling();
firstRow.addSeeksToIOTime(seeksFR);
lastRow.addSeeksToIOTime(seeksLR);
// Restore old values.
if (getPartialOrderProbesFlag())
{
effectiveTotalRowCount_ = oldEffectiveTotalRowCount;
successfulProbes_ = oldSuccessfulProbes;
duplicateSuccProbes_ = oldDuplicateSuccProbes;
failedProbes_ = oldFailedProbes;
uniqueProbes_ = oldUniqueProbes;
probes_ = oldProbes;
}
DCMPASSERT(seeksFR <= seeksLR);
CostScalar selectedRowsPerScan(getResultSetCardinalityPerScan());
const CostScalar rowsPerScan
((getDataRows()/getNumActivePartitions()).getCeiling());
// Patch for Sol.10-031024-0755 (Case 10-031024-9406).
selectedRowsPerScan = MINOF(selectedRowsPerScan, rowsPerScan);
// Estimate minimum number of rows that must be processed in order
// to get the first row. This is typically the number of rows that
// will fit in a DP2 buffer. If there are fewer rows than this in
// the result, then only that many rows need to be processed. This
// will be used to compute the CPU instruction required to produce
// the first row.
//
const CostScalar dp2Rows(getRowsInDP2Buffer());
const CostScalar rowsPerScanFR(MINOF(dp2Rows, rowsPerScan));
const CostScalar selectedRowsPerScanFR(MINOF(dp2Rows, selectedRowsPerScan));
CostScalar cpuInstructionsFR;
CostScalar cpuInstructionsLR;
// Estimate CPU Instructions (includes Per Request, Per Row and Per
// KB number of CPU Instructions) for First and Last row accessed.
//
estimateCPUInstructionsMultiProbe(seeksLR,
rowsPerScanFR,
rowsPerScan,
selectedRowsPerScanFR,
selectedRowsPerScan,
seqKBFR,
seqKBLR,
cpuInstructionsFR,
cpuInstructionsLR);
firstRow.addInstrToCPUTime(cpuInstructionsFR);
lastRow.addInstrToCPUTime(cpuInstructionsLR);
// Estimate the amount of Idle time used by this Scan. Includes
// time to open secondary partitions (all but the first).
//
const CostScalar idleTime(estimateIdleTime());
firstRow.addToIdleTime(idleTime);
lastRow.addToIdleTime(idleTime);
const CostScalar probesPerScan
((getProbes()/getNumActivePartitions()).getCeiling());
firstRow.setNumProbes(probesPerScan);
lastRow.setNumProbes(probesPerScan);
} // SimpleFileScanOptimizer::computeCostVectorsMultiProbes(...)
// SimpleFileScanOptimizer::estimateCPUInstructionsMultiprobe()
// Estimate CPU Instructions (includes Per Request, Per Row and Per KB
// number of CPU Instructions) for First and Last row accessed.
//
// Inputs:
//
// seeksPerScan - The number of seeks to be performed by this Scan
// (single partition).
//
// rowsPerScanFR - The number of rows accessed by this scan before
// returning the first row (single partition).
//
// rowsPerScanLR - The number of rows accessed by this scan (single
// partition).
//
// seqKBFR - The number of KBytes to be accessed by this Scan to
// produce the first row (single partition).
//
// seqKBLR - The number of KBytes to be accessed by this Scan to
// produce the last row (single partition).
//
// Outputs:
// cpuInstructionsFR - Number of CPU instructions to access the First row.
// cpuInstructionsLR - Number of CPU instructions to access the Last row.
//
void
SimpleFileScanOptimizer::estimateCPUInstructionsMultiProbe(
const CostScalar &seeksPerScan,
const CostScalar &rowsPerScanFR,
const CostScalar &rowsPerScanLR,
const CostScalar &selectedRowsPerScanFR,
const CostScalar &selectedRowsPerScanLR,
const CostScalar &seqKBFR,
const CostScalar &seqKBLR,
CostScalar &cpuInstructionsFR,
CostScalar &cpuInstructionsLR) const
{
const CostScalar probesPerScan
((getProbes()/getNumActivePartitions()).getCeiling());
const CostScalar
succProbesPerScan
((getSuccessfulProbes()/getNumActivePartitions()).getCeiling());
cpuInstructionsFR = 0.0;
cpuInstructionsLR = 0.0;
if (probesPerScan > csZero)
{
// Estimate the per-request CPU instructions. This includes
// performing a binary search of each index block to find the
// appropriate entry.
//
// Instructions to traverse the B-Tree for successful requests
// +
// Instructions to traverse the B-Tree for unsuccessful requests
//
CostScalar instrPerReqst(csZero);
// number of instructions to traverse btree for one probe.
//
const CostScalar cpuForBTreeComparisons(estimateCPUPerReqstInstrs());
// Successful requests: We assume that the cpu cost for *each*
// successful request is a constant overhead plus a function of the
// number of index blocks traversed and the length of key columns
if(succProbesPerScan > csZero)
{
instrPerReqst +=
(succProbesPerScan *
(cpuForBTreeComparisons +
CostPrimitives::getBasicCostFactor(CPUCOST_DATARQST_OVHD)));
}
const CostScalar failedRequestsPerScan =
probesPerScan - succProbesPerScan;
if (failedRequestsPerScan > csZero)
{
instrPerReqst +=
(failedRequestsPerScan * (cpuForBTreeComparisons +
CostPrimitives::getBasicCostFactor(CPUCOST_DATARQST_OVHD))
);
}
// Estimate the per-row CPU instructions. This includes the
// instructions necessary to copy the row from DP2 to the EXEINDP2.
//
const CostScalar instrPerRow(estimateCPUPerRowInstrs());
// Estimate per-seek CPU instructions. This is the extra cost
// necessary for each seek. For now this is just a default value.
// Commented out because it was never used. SP.
//const CostScalar instrPerSeek(estimateCPUPerSeekInstrs());
// Estimate the per-KB CPU instructions. This is the extra cost
// necessary to copy each KB from volume to DP2 cache. For now this
// is just a default value.
//
const CostScalar instrPerKB(estimateCPUPerKBInstrs());
// Estimate the number of instruction required to evaluate the
// Executor predicates on one row.
//
const CostScalar instrPerExePredPerRow(estimateCPUExePredPerRowInstrs());
// First Row CPU instructions.
//
cpuInstructionsFR =
// assume that the first row gets its data in the successful request:
(instrPerReqst/probesPerScan)
// + (instrPerSeek * seeksPerScan)
+ (instrPerKB * seqKBFR)
// Only rows that are selected are moved out of the cache:
+ (instrPerRow * selectedRowsPerScanFR)
+ (instrPerExePredPerRow * rowsPerScanFR);
// Last Row CPU instructions.
//
cpuInstructionsLR =
instrPerReqst
//+ (instrPerSeek * seeksPerScan)
+ (instrPerKB * seqKBLR)
+ (instrPerRow * selectedRowsPerScanLR)
+ (instrPerExePredPerRow * rowsPerScanLR);
// Sanity Check
//
DCMPASSERT(cpuInstructionsFR <= cpuInstructionsLR);
}
} // SimpleFileScanOptimizer::estimateCPUInstructionsMultiProbe(...)
// SimpleFileScanOptimizer::estimateNumberOfSeeksPerScanMultiProbe()
// Estimate the of seeks required for this all probes per scan
// (partition). Include the seeks required for accessing the index
// blocks.
//
// Need to Seek to every index level except the root, plus need
// to seek to first data block.
//
// Do we need to seek twice this amount, once for the begin key
// and once for the end key??????
// We, probably, do. Dp2 does it instead of using
// IOBlocksToReadPerAccess when deciding about prefetch.
//
// Returns: the number of seeks required for all probes to access
// a partition
//
CostScalar
SimpleFileScanOptimizer::estimateNumberOfSeeksPerScanMultiProbe() const
{
// -------------------------------------------------------------
// Compute the blocks in the inner table:
// -------------------------------------------------------------
const CostScalar
totalRowsInInnerTable = getEffectiveTotalRowCount();
CostScalar innerBlocksUpperBound
(totalRowsInInnerTable/getEstimatedRecordsPerBlock());
innerBlocksUpperBound = innerBlocksUpperBound.getCeiling();
NADefaults &defs = ActiveSchemaDB()->getDefaults();
const CostScalar uniqueProbes = getUniqueProbes();
///////////////////////////////////////////////////////////////////////
// fix the
// estimation for seeks beginBlocksLowerBound = no. of unique
// entries WHICH ARE SUCCESSFUL in matching with inner table
// totalBlocksLowerBound = no. of blocks returning from the OUTER
// TABLE after applying the predicate
//
// innerBlocksUpperBound = Total size of inner table
// DBB = Distance between the blocks
// MSD = Distance for average seek
// CostScalar(0.025) = (seek time is between .2ms and 8ms ignore
// latency, so fixed is .2ms/8ms=0.025)
// (0.008) Limiting the rate of increase upto twice that of inner
// blocks and then using a step function
//
// STEP FUNCTION so as to decrease the rate of increase after twice
// of inner blocks cost for fixed seeks is the, % of probes which
// can be read with minimum seek time finalRows are the final
// resulting rows, matched with outertable and qualified with <
// predicate To the readAhead cost the Mrows * finalRows is added,
// this is done in order to calculate the cost of moving the rows to
// the executor. Once the rows from the outer table come in and they
// are compared with the innertable rows, the successful rows will
// be returned to the executor, so the more the number of rows
// returned to the executor the more the cost.
///////////////////////////////////////////////////////////////////////
CostScalar seekComputedWithDp2ReadAhead(csZero);
CostScalar indexBlocksLowerBound = getIndexBlocksLowerBound();
CostScalar extraIndexBlocks(csZero);
if(getInOrderProbesFlag())
{
// uniqueProbes calculated from CategorizeProbes should be at
// least one.
//
CMPASSERT(uniqueProbes > csZero);
const CostScalar DBB=(innerBlocksUpperBound / uniqueProbes);
const CostScalar MSD = defs.getAsULong(NJ_MAX_SEEK_DISTANCE);
const CostScalar Ulimit = defs.getAsDouble(NJ_INC_UPTOLIMIT);
const CostScalar Mrows = defs.getAsDouble(NJ_INC_MOVEROWS);
CostScalar fixedSeeks;
CostScalar Limit = csTwo * innerBlocksUpperBound;
if (uniqueProbes > Limit)
{
const CostScalar Alimit = defs.getAsDouble(NJ_INC_AFTERLIMIT);
CostScalar extraProbes = uniqueProbes - Limit;
fixedSeeks = Ulimit * Limit + Alimit * extraProbes;
}
else
{
fixedSeeks = Ulimit * (uniqueProbes-1);
}
CostScalar variableSeeks = uniqueProbes - fixedSeeks;
CostScalar finalRows=getResultSetCardinality();
seekComputedWithDp2ReadAhead =
csOne
+ fixedSeeks
+ (variableSeeks * (DBB/MSD))
+ (Mrows * finalRows);
}
else
{
// It is not clear why are we talking about DP2 readAhead
// in the case of random probes. It makes sense only if the
// whole table/index partition fits into
// DP2_MAX_READ_PER_ACCESS. SP.
// This has to use DP2Cache in the way similar to estimateSeqKB.
// Duplicate probes may require lots of extra seeks.
const CostScalar uniqueSuccProbes = MINOF(uniqueProbes,
getSuccessfulProbes() - getDuplicateSuccProbes());
// The begin blocks denote the starting data
// block for the subset that each succ. probe generates
//
const CostScalar beginBlocksLowerBound =
MINOF(uniqueSuccProbes, innerBlocksUpperBound);
// Assume that even if the whole index doesn't fit into cache
// we will have to read all index blocks (1 seek per block)
// plus 1 seek per extraIndexBlock if index does not fit in
// DP2Cache (only for the index leaf blocks - rest will stay
// in cache).
CostScalar
cacheSize(getDP2CacheSizeInBlocks(getBlockSizeInKb()));
if ( indexBlocksLowerBound >= cacheSize )
{
CostScalar cacheMissProbabilityForIndexBlock =
(indexBlocksLowerBound - cacheSize)/indexBlocksLowerBound;
extraIndexBlocks = (getProbes()/getNumActivePartitions() -
cacheSize) * cacheMissProbabilityForIndexBlock;
// each probe will cause a seek at data block level
seekComputedWithDp2ReadAhead = getProbes();
}
else
{
cacheSize -= indexBlocksLowerBound;
const CostScalar
cacheSizeForAll(cacheSize * getNumActiveDP2Volumes());
if (getSearchKey()->isUnique())
{
// In this case for each probe no more than one data
// block is accessed. Readahead won't be used.
CostScalar estBlocksAccessedByUniqueProbes =
MINOF(uniqueProbes, innerBlocksUpperBound);
if ( estBlocksAccessedByUniqueProbes <= cacheSizeForAll )
{
seekComputedWithDp2ReadAhead =
estBlocksAccessedByUniqueProbes;
}
else
{
// Assume random unique probes never cause read ahead
CostScalar cacheMissProbabilityForUniqueProbe =
((estBlocksAccessedByUniqueProbes - cacheSizeForAll)
/estBlocksAccessedByUniqueProbes).minCsZero();
seekComputedWithDp2ReadAhead =
cacheSizeForAll + // First probes to fill the cache
(getProbes() - cacheSizeForAll) * // rest could miss
cacheMissProbabilityForUniqueProbe;
}
}
else
{
// SearchKey is not unique. In this case each
// successful probe could bring several (or even all)
// blocks of inner table. One unique failed probe would
// access one data block. Readahead could be used.
CostScalar uniqueFailedProbes =
(uniqueProbes - uniqueSuccProbes).minCsZero();
CostScalar innerBlocksAccessedBySuccProbes =
(uniqueSuccProbes * getBlksPerSuccProbe());
CostScalar innerBlocksAccessedByAllProbes =
innerBlocksAccessedBySuccProbes + uniqueFailedProbes;
if(innerBlocksAccessedByAllProbes <= cacheSizeForAll)
{
// inner table completely fits in cache
const CostScalar maxDp2ReadInKB
(defs.getAsULong(DP2_MAX_READ_PER_ACCESS_IN_KB));
const CostScalar
maxDp2ReadInBlocks(maxDp2ReadInKB/getBlockSizeInKb());
if ( innerBlocksAccessedByAllProbes <
maxDp2ReadInKB * getNumActivePartitions())
{
// first read will read the whole table/index
// partition
seekComputedWithDp2ReadAhead =
getNumActivePartitions();
}
else
{
/*
seekComputedWithDp2ReadAhead =
MINOF(beginBlocksLowerBound,
innerBlocksUpperBound/maxDp2ReadInBlocks);
*/
// The formula above is too optimistic, this needs
// a separate investigation, Use conservative estimate
// when only the first read causes read ahead.
seekComputedWithDp2ReadAhead =
(beginBlocksLowerBound - maxDp2ReadInBlocks).minCsOne();
}
}
else
{
if ( getBlksPerSuccProbe() > cacheSizeForAll )
{
// the result of successful probe does not fit in cache
// therefore each duplicate probe will have to reread
// all necessary blocks and have an extra seek. The
// number of seeks is the same as the number of probes
seekComputedWithDp2ReadAhead = getProbes();
}
else
{
// table does not fit in cache extra data block seeks
// needed, there is a chance that successful or failed
// probe hits the cache. Assume that successful and
// failed probes share the cache proportionally the
// number of blocks they accessed.
// Part of cache to keep successful probe blocks
CostScalar partOfSuccProbes =
cacheSizeForAll * innerBlocksAccessedBySuccProbes
/ innerBlocksAccessedByAllProbes;
// Part of cache to keep failed probe blocks
CostScalar partOfFailedProbes =
(cacheSizeForAll - partOfSuccProbes).minCsZero();
// part of succ probes that fits in its part of cache
CostScalar cacheHitProbForSucc = (partOfSuccProbes /
innerBlocksAccessedBySuccProbes).maxCsOne();
// part of failed probes that fits in its part of cache
CostScalar cacheHitProbForFail(csZero);
if ( uniqueFailedProbes.isGreaterThanZero() )
cacheHitProbForFail =
(partOfFailedProbes / uniqueFailedProbes).maxCsOne();
// assume that each random unique probe causes 1 seek
seekComputedWithDp2ReadAhead = getUniqueProbes() +
getDuplicateSuccProbes() *
(csOne - cacheHitProbForSucc) +
(getFailedProbes() - uniqueFailedProbes) *
(csOne - cacheHitProbForFail);
}
}
}
}
}
// Assuming indexBlocksLowerBound is for one partition only
return (seekComputedWithDp2ReadAhead/getNumActivePartitions() +
indexBlocksLowerBound + extraIndexBlocks).getCeiling();
}//SimpleFileScanOptimizer::estimateNumberOfSeeksPerScanMultiProbe()
// SimpleFileScanOptimizer::estimateTotalRowCount()
// Estimate the real and effective total row count for the table.
// The Effective Total Row Count is the size of the bounding subset
// of all probes. Typically this will be all the rows of the table,
// but if all probes are restricted to a subset of rows (e.g. the
// key predicate contains leading constants) the effective row
// count will be less than the total row count.
//
// Returns: The estimated real and effective row count.
//
void
SimpleFileScanOptimizer::estimateEffTotalRowCount(
CostScalar &realRowCount,
CostScalar &effRowCount) const
{
// CR 10-010822-4815
// Query 04 in OBI was producing a bad plan as the seeks for Nested
// Join were computed without taking into account if the blocks of
// inner table are consecutive or not. This is fixed by computing
// the seeks after getting the right count of blocks by getting the
// rows for the key predicates on the leading columns with a Value
// Equality Group referencing a constant. "totalPreds" is the set
// of all predicates of all key columns with a constant for all the
// index values find the keyPredicates, for every predicate check if
// it is a VEG and that VEG has a constant, if so then add the
// predicate to the "totalPreds". Repeat this only for the leading
// columns, if the column doesn't have a constant then stop
// searching. Apply the "totalPreds" to the histograms to get the
// rowcount, divide the rowcount by recordsperblock to get the
// blocks. If no constant then pass the total blocks for inner table
// Determine if all probes will be contained within a subset of the
// whole table. (Key predicate contains leading constants. e.g. k1
// = 3 and k2 = 4 and k3 = fk1)
//
// The set of leading constant equality predicates.
//
ValueIdSet totalPreds;
// Did we find at least one leading column with a constant equality
// predicate.
//
NABoolean hasAtleastOneConstExpr = FALSE;
// Organize the predicates by key columns.
//
ColumnOrderList keyPredsByCol(getIndexDesc()->getIndexKey());
getSearchKey()->getKeyPredicatesByColumn(keyPredsByCol);
// How many of the leading predicates represent a single subset.
//
CollIndex singleSubsetPrefixColumn;
keyPredsByCol.getSingleSubsetOrder(singleSubsetPrefixColumn);
// Examine the predicates on the single subset key columns.
// RMW - BUG - Could this code break out of the inner loop too early?
//
for (CollIndex Indx=0; Indx <= singleSubsetPrefixColumn; Indx++)
{
// predicate on column which is part of key
//
const ValueIdSet *keyPreds = keyPredsByCol[Indx];
// Is there an constant equality predicate on this column
//
NABoolean curFound = FALSE;
if (keyPreds AND NOT keyPreds->isEmpty())
{
ValueId predId;
for(predId=keyPreds->init(); //Inner for loop for all the
keyPreds->next(predId); //the predicates of the column
keyPreds->advance(predId))
{
const ItemExpr *pred = predId.getItemExpr();
if (pred->getOperatorType() == ITM_VEG_PREDICATE)
{
const VEG *predVEG = ((VEGPredicate *)pred)->getVEG();
const ValueIdSet &VEGGroup = predVEG->getAllValues();
if (VEGGroup.referencesAConstExpr())
{
totalPreds += predId;
hasAtleastOneConstExpr = TRUE;
curFound = TRUE;
} //end of check for A Constant Expression
else
break; // break out of inner loop. May also
// break out of outer loop
} //end of "if" Operator to be VEG predicate
} //end of inner loop "for(predId=partPreds->init();...)"
} //end of "if" predicates to be non empty
if (NOT curFound)
break;
} //end of outer loop "for (CollIndex i=0; i <= singleSubsetPre...)"
// Get the histograms for the inner table (this scan)
IndexDescHistograms
innerHistograms(*getIndexDesc(),
getIndexDesc()->getIndexKey().entries());
//GET ROW count
realRowCount = innerHistograms.getRowCount();
// If there are leading constant equality predicate, then use these
// predicates to determine the effective row count. Otherwise use
// the whole table row count.
//
if( hasAtleastOneConstExpr )
{
const SelectivityHint * selHint = getFileScan().getSelectivityHint();
const CardinalityHint * cardHint = getFileScan().getCardinalityHint();
innerHistograms.applyPredicates(totalPreds, getRelExpr(), selHint, cardHint, REL_SCAN);
//GET ROW count
effRowCount = innerHistograms.getRowCount();
}
else
{
effRowCount = realRowCount;
}
// This is a simple patch until we figure out the way to properly
// estimate effRowCount. When we know the number of active
// this estimate cannot exceed the ratio of actPart over total
// of part multiplied by realRowCount. This could be done by using
// more predicates in totalPreds variable.
// All this casting away is because mutable is not supported
NodeMap * nodeMapPtr = (NodeMap *)(getContext().getPlan()->
getPhysicalProperty()->getPartitioningFunction()->getNodeMap());
CostScalar actPart = getEstNumActivePartitionsAtRuntimeForHbaseRegions();
if (CmpCommon::getDefault(NCM_HBASE_COSTING) == DF_OFF)
actPart = getNumActivePartitions();
effRowCount = MINOF( effRowCount,
realRowCount * actPart/nodeMapPtr->getNumEntries() );
return;
} // SimpleFileScanOptimizer::estimateEffTotalRowCount()
// SimpleFileScanOptimizer::estimateKBMultiProbe()
// Compute the sequential KBytes accessed to produce the first and
// last rows.
//
// OUTPUTS:
// seqKBFR - The number of KBytes to be accessed to produce the first row.
// seqKBLR - The number of KBytes to be accessed to produce the last row.
//
void
SimpleFileScanOptimizer::estimateKBMultiProbe(CostScalar &seqKBFR,
CostScalar &seqKBLR) const
{
// total number of blocks to be read sequentially In order to
// produce the last row, need to read all required blocks.
//
seqKBLR = estimateSeqKBReadPerScanMultiProbe();
// Estimate minimum number of KB that must be processed in order to
// get the first row. This is typically the size in KB of a DP2
// buffer. If the result contains fewer KB than this, then
// only the result needs to be processed.
//
CostScalar dp2BufferSize
(CostPrimitives::getBasicCostFactor(DP2_MESSAGE_BUFFER_SIZE));
// Number of KB required to produce the first row
//
seqKBFR = MINOF(dp2BufferSize, seqKBLR);
} // SimpleFileScanOptimizer::estimateKBMultiProbe(...)
// SimpleFileScanOptimizer::estimateSeqKBReadPerScanMultiProbe()
// Estimate the amount (in KBytes) of data read per scan (partition).
// Include the index blocks which must be read.
//
// Consider these cases:
// Full Cache Benefit - Duplicate probes always get a cache hit.
// No Cache Benefit - Duplicate probes never get a cache hit.
// Random access - Duplicate probes may get a cache hit.
//
// Returns: the number of sequential KB read for access to
// a partition (this includes index and data blocks)
//
CostScalar
SimpleFileScanOptimizer::estimateSeqKBReadPerScanMultiProbe() const
{
CostScalar seqBlocks;
// Compute the blocks in the inner table:
//
const CostScalar
totalRowsInInnerTable = getEffectiveTotalRowCount();
const CostScalar innerBlocks
(totalRowsInInnerTable/getEstimatedRecordsPerBlock());
CostScalar innerBlocksPerScan
(innerBlocks/getNumActivePartitions());
innerBlocksPerScan = innerBlocksPerScan.getCeiling();
// Number of distinct blocks accessed
// SP. This is a misnomer, if each probe - assume unique - access 1 block
// it may exceed the number of total blocks in inner table. It could be
// called innerBlocksAccessedByProbes
//
const CostScalar uniqBlocks
((getSuccessfulProbes() - getDuplicateSuccProbes()) * getBlksPerSuccProbe());
CostScalar uniqBlocksPerScan
(uniqBlocks/getNumActivePartitions());
uniqBlocksPerScan = uniqBlocksPerScan.getCeiling();
// Cache size for one DP2 process for this Block size
//
CostScalar cacheSize(getDP2CacheSizeInBlocks(getBlockSizeInKb()));
CostScalar indexBlocksLowerBound = getIndexBlocksLowerBound();
CostScalar extraIndexBlocks(csZero);
CostScalar maxBlocksToRead =
// all blocks for successful probes
(getSuccessfulProbes()*getBlksPerSuccProbe() +
// all blocks for failed probes
getFailedProbes());
// We will assume that index blocks will stay in DP2 cache whenever possible
// meaning that data block cannot force index block out of cache. Therefore,
// data blocks can use only the rest of DP2cache after index blocks. If index
// block lower bound cannot fit in cache completely we will add extra index
// blocks to read and there will be no cache hits for data blocks.
if ( indexBlocksLowerBound >= cacheSize )
{
if ( getInOrderProbesFlag() )
{
// It is hardly possible that duplicate probe won't find any of
// index blocks in DP2cache, so there won't be extra index blocks to read,
// each unique probe will read, if necessary, only blocks counted in
// indexBloksLowerBound. Because each duplicate probe will need the same
// data blocks there will be no extra data blocks if all blocks per
// successful probe fit in the cache
// We would ignore 2-3 blocks at most of index path to these data blocks.
if ( getBlksPerSuccProbe() <= cacheSize )
{
if (CmpCommon::getDefault(COMP_BOOL_28) == DF_OFF)
{
seqBlocks =
(getUniqueProbes()/getProbes())* // ratio of unique probes
maxBlocksToRead;
}
else
{
seqBlocks = MINOF(innerBlocks,
(getUniqueProbes()/getProbes())* // ratio of unique probes
maxBlocksToRead);
}
}
else
// duplicate probes require reread of the table because beginning
// of the previous probe was already lost
{
seqBlocks = maxBlocksToRead;
}
}
else
// this is the worst case, even index blocks need new read
{
CostScalar cacheMissProbabilityForIndexBlock =
(indexBlocksLowerBound - cacheSize)/indexBlocksLowerBound;
extraIndexBlocks = ( getProbes()/getNumActivePartitions() -
cacheSize) * cacheMissProbabilityForIndexBlock;
seqBlocks = maxBlocksToRead;
}
}
else
{
cacheSize -= indexBlocksLowerBound;
// Total cache size for all active DP2s. Note that some partitions
// may share the same DP2 and thus will share the same cache.
//
const CostScalar totCacheSize(cacheSize * getNumActiveDP2Volumes());
if ( (innerBlocks <= totCacheSize) // Whole table fits in cache
OR
(uniqBlocks + getFailedProbes() <= totCacheSize) // all blocks accessed
OR
// Duplicate probe finds data in cache
( (getBlksPerSuccProbe() <= cacheSize) AND getInOrderProbesFlag() )
)
{
// Full cache benefit. Duplicate probes always get a cache hit.
seqBlocks = MINOF(innerBlocks, uniqBlocks + getFailedProbes());
} else if ((getBlksPerSuccProbe() > cacheSize) &&
getInOrderProbesFlag()) {
// No benefit from cache, Duplicate probes always must reread
// blocks. Probes come in order, so duplicate probes will be
// together, but the cache cannot hold the whole request. The
// first block(s) has already been flushed from the cache. So
// must reread all these blocks again.
//
seqBlocks = maxBlocksToRead;
} else {
// orders don't match, and cache can hold only a portion of the result.
// RANDOM case:
//
// Must read the uniqBlocks
// Must read some of the duplicate blocks.
// Calculate these extraDataBlocks based on an estimate cache hit rate.
//
// the hit probability is the fraction of the cache size over the
// total blocks to be read:
//
CostScalar cacheHitProbability(csOne);
CostScalar lowerBoundBlockCount = uniqBlocks + getFailedProbes();
cacheHitProbability = (totCacheSize/lowerBoundBlockCount).maxCsOne();
// Duplicate Probes get a cache hit with a probability of
// cacheHitProbability
//
// Duplicate probes that result in extra block reads (cache miss)
const CostScalar
extraDataBlocks = (getDuplicateSuccProbes() * getBlksPerSuccProbe())
* (csOne - cacheHitProbability);
seqBlocks = lowerBoundBlockCount // unique blocks, need to read each one
+ extraDataBlocks; // Duplicate blocks may get a cache hit.
}
}
// Assume that the blocks are evenly distributed over the active
// partitions.
//
seqBlocks = (seqBlocks / getNumActivePartitions() +
indexBlocksLowerBound + extraIndexBlocks).getCeiling();
// Convert from Blocks to KB
//
return (seqBlocks * getBlockSizeInKb());
} // SimpleFileScanOptimizer::estimateSeqKBReadPerScanMultiProbe()
// -----------------------------------------------------------------------
// SimpleFileScanOptimizer::ordersMatch method
// This method determines if the inner table (right child) sort key is in
// the same order as the outer table (left child)sort key. Only applicable
// when processing right child of a nested join operator.
//
// INPUT PARAMS:
// ipp: The input physical properties of the right child.
// indexDesc: The index descriptor of the right child.
// innerOrder : the sort key for the right child.
// charInputs: The characteristic inputs for this operator
// partiallyInOrderOK: TRUE if the order can be used even if the probes
// are not completely in order. This will be TRUE
// for read, update and delete, and FALSE for insert.
// OUTPUT PARAMS:
// probesForceSynchronousAccess: TRUE if the probes are completely
// in order across multiple partitions and the clustering key is
// the same as the partitioning key. FALSE otherwise.
// RETURN VALUE:
// TRUE if there is a match complete between the probes order and the
// table's (or index's) order. FALSE otherwise.
// -----------------------------------------------------------------------
NABoolean
SimpleFileScanOptimizer::ordersMatch(
const InputPhysicalProperty* ipp,
const IndexDesc* indexDesc,
const ValueIdList* innerOrder,
const ValueIdSet& charInputs,
NABoolean partiallyInOrderOK,
NABoolean& probesForceSynchronousAccess)
{
ValueIdList innerOrderProbeCols;
ValueIdList uncoveredCols;
// temporary var to keep column Ids without inverse for possible
// use to get UEC for this column from histograms
ValueIdList innerOrderProbeColsNoInv;
CollIndex numInOrderCols = 0;
NABoolean partiallyInOrder = FALSE;
NABoolean fullyInOrder = FALSE;
probesForceSynchronousAccess = FALSE;
if ((ipp != NULL) AND (!(ipp->getAssumeSortedForCosting())) AND (!(ipp->getExplodedOcbJoinForCosting())))
{
// Shouldn't have an ipp if there are no outer order columns!
if ((ipp->getNjOuterOrder() == NULL) OR
ipp->getNjOuterOrder()->isEmpty())
{
CCMPASSERT(FALSE);
return FALSE;
}
// An ipp should also have the outer expression partitioning function!
if (ipp->getNjOuterOrderPartFunc() == NULL)
{
CCMPASSERT(FALSE);
return FALSE;
}
// Should not have passed the ipp if this access path could not
// use the outer order, so there MUST be at least one sort key column!
if (innerOrder->isEmpty())
{
CCMPASSERT(FALSE);
return FALSE;
}
// Get the physical partitioning function for the access path
const PartitioningFunction* physicalPartFunc =
indexDesc->getPartitioningFunction();
// If the outer order is not a DP2 sort order, and the access path
// is range partitioned, then need to check if the probes will force
// synchronous access to the partitions of this access path. The
// probes will force synchronous access if the leading partitioning
// key column is the same as the leading clustering key column.
if ((ipp->getNjDp2OuterOrderPartFunc() == NULL) AND
(physicalPartFunc != NULL)) // will be NULL for unpart tables
{
const RangePartitioningFunction* rpf =
physicalPartFunc->castToRangePartitioningFunction();
if (rpf != NULL)
{
ValueIdList partKeyAsList;
// Get the partitioning key as a list
partKeyAsList = rpf->getKeyColumnList();
CCMPASSERT(NOT partKeyAsList.isEmpty());
// Get the leading partitioning key column
ValueId leadingPartKeyCol = partKeyAsList[0];
// Get the leading clustering key column - remove any INVERSE node
ValueId leadingClustKeyCol = (*innerOrder)[0];
leadingClustKeyCol =
leadingClustKeyCol.getItemExpr()->removeInverseOrder()->getValueId();
if (leadingClustKeyCol == leadingPartKeyCol)
probesForceSynchronousAccess = TRUE;
} // end if a range partitioned table
} // end if not a DP2 sort order and a partitioned table
// Determine which columns of the index sort key are probe columns,
// up to the first column not covered by a constant or probe column.
// To do this we call the "findNJEquiJoinCols" method. The equijoin
// cols are the probe columns, i.e. the values coming from the outer
// child. For read these will be the equijoin columns, for write
// they are the key values of the records that need to be written.
//
// Note that for write, all the call to this method really does
// is eliminate any columns that are covered by constants or
// params/host vars. This is because all key columns will
// always be probe columns, since we always need all the key cols
// to accurately determine the record to insert/update/delete. So,
// we could just call the method "removeCoveredExprs" for write.
// This would require adding another parameter to the ordersMatch
// method to distinguish write from read. This is considered
// undesirable, and so is not done.
innerOrderProbeCols =
innerOrder->findNJEquiJoinCols(
ipp->getNjOuterCharOutputs(),
charInputs,
uncoveredCols);
// There MUST be some probe columns, otherwise there should not
// have been an ipp!
if (innerOrderProbeCols.isEmpty())
{
CCMPASSERT(FALSE);
return FALSE;
}
if ( CmpCommon::getDefault(COMP_BOOL_96) == DF_OFF )
{
// Before doing any processing, must eliminate any duplicates from
// the innerOrder key. Index sort keys can have
// duplicate valueid's referring to base columns,
// we need to remove them to check for partial order.
innerOrderProbeCols.removeDuplicateValueIds();
}
ValueIdList njOuterOrder = *(ipp->getNjOuterOrder());
// Sol 10-040303-3781. The number of entries of innerOrderProbCols(5)
// could be greater than of njOuterOrder(3). In this case we hit ABORT
// in Collections.cpp for unused element of njOuterOrder. The
// following restriction on loop iteration prevents it. We need to
// investigate details why we got innerOredrProbCols bigger than
// njOuetrOredr iin the first place. That corresponding case will
// be created.
CollIndex maxInOrderCols =
MINOF(njOuterOrder.entries(), innerOrderProbeCols.entries());
fullyInOrder = TRUE;
// Determine if the leading inner order column and the leading
// column of the outer order are the same.
ValueId innerOrderCol;
ValueId noInverseInnerOrderCol;
NABoolean innerOrderColIsDesc = FALSE;
ValueId outerOrderCol;
ValueId noInverseOuterOrderCol;
NABoolean outerOrderColIsDesc = FALSE;
do
{
// Remove any inverse node on the inner order column
// and remember if there was one.
innerOrderCol = innerOrderProbeCols[numInOrderCols];
noInverseInnerOrderCol =
innerOrderCol.getItemExpr()->removeInverseOrder()->getValueId();
innerOrderProbeColsNoInv.insert(noInverseInnerOrderCol);
innerOrderColIsDesc = FALSE;
if (noInverseInnerOrderCol != innerOrderCol)
innerOrderColIsDesc = TRUE;
// Remove any inverse node on the leading outer order column
// and remember if there was one.
outerOrderCol = njOuterOrder[numInOrderCols];
noInverseOuterOrderCol =
outerOrderCol.getItemExpr()->removeInverseOrder()->getValueId();
outerOrderColIsDesc = FALSE;
if (noInverseOuterOrderCol != outerOrderCol)
outerOrderColIsDesc = TRUE;
// The column of the inner table sort key and the
// the column of the outer table sort key must be
// the same. If one is DESC, they must both be DESC.
if ((noInverseInnerOrderCol != noInverseOuterOrderCol) OR
(innerOrderColIsDesc != outerOrderColIsDesc))
fullyInOrder = FALSE;
else if (numInOrderCols == 0)
{
// The leading inner order column is in the same order as the
// leading outer order column, so the probes are at least
// partially in order. If all remaining inner order columns
// are in order then the probes will be completely in order.
partiallyInOrder = TRUE;
numInOrderCols++;
}
else
{
numInOrderCols++;
}
} while ((numInOrderCols < maxInOrderCols) AND fullyInOrder);
// Since there is an ipp, the probes must be at least partially in the
// same order, because we checked this before passing the ipp!
if (NOT partiallyInOrder)
{
// The following line has been commented as it exposed a bug, after
// the value of CQD HIST_NO_STATS_UEC was changed from 100 to 2.
// Commented this after discussions, created a solution
// 10-071217-9517.
// CCMPASSERT(FALSE);
return FALSE;
}
} // end if ipp exists
// For the probes to be fully order, numInOrderCols must be same as
// equi join key predicates and uncoveredCols should be empty
if (fullyInOrder && (numInOrderCols==innerOrderProbeCols.entries()) &&
uncoveredCols.isEmpty())
{
return TRUE;
}
else if (partiallyInOrder AND partiallyInOrderOK)
{
// Get a list of colstats. Each colstats contains the
// histogram data for one column of the table.
const ColStatDescList& csdl =
indexDesc->getPrimaryTableDesc()->getTableColStats();
const MultiColumnUecList * uecList = csdl.getUecList();
CostScalar multiColUec = csZero;
CostScalar rowcount = getRelExpr().getGroupAttr()
->getResultCardinalityForEmptyInput();
ValueIdList inOrderbaseColProbes;
CollIndex baseTableColIndex;
// get base column valueIds for elements in innerOrderProbeCols
for (CollIndex keyColIndex = 0; keyColIndex < numInOrderCols; keyColIndex++)
{
// Sol 10-040303-3781. Previously we were retrieving columns from
// innerOrderProbeCols list. The column 0 of innerOrderProbCols had the
// type ITM_INVERSE. This type was not processed in
// getColStatDescForColumn,
// as a result baseTableColIndex was left uninitialized and we hit
// ABORT in Collections.cpp. Another change - if column cannot be found
// and getColStatDescIndexForColumn returns FALSE we should consider it
// as situation with m=not matching columns and return FALSE as we did
// already many times in this method.
if (NOT csdl.getColStatDescIndexForColumn(
baseTableColIndex, innerOrderProbeColsNoInv[keyColIndex])
)
{
CCMPASSERT(FALSE);
return FALSE;
}
inOrderbaseColProbes.insert(csdl[baseTableColIndex]->getColumn());
}
if (uecList)
multiColUec = uecList->lookup(inOrderbaseColProbes);
// If partial order keys have skew meaning for each key prefix
// join returns more rows than NESTED_JOINS_LEADING_KEY_SKEW_THRESHOLD,
// then treat it as random order and cost it very high.
// This indirect way of accounting data skew on partitions
// The direct way of accounting partition skew would be to divide the
// accessed rows by number of skewed partitions instead of total partitions.
Lng32 nj_leading_key_skew_threshold =
(Lng32)(ActiveSchemaDB()->getDefaults()).
getAsLong(NESTED_JOINS_LEADING_KEY_SKEW_THRESHOLD);
if ( multiColUec.isGreaterThanZero() &&
rowcount/multiColUec < CostScalar(nj_leading_key_skew_threshold) )
partialOrderProbes_ = TRUE;
} // end if partially in order
return FALSE;
} // SimpleFileScanOptimizer::ordersMatch()
NABoolean SimpleFileScanOptimizer::isLeadingKeyColCovered()
{
ValueIdSet allPreds;
allPreds = getSingleSubsetPreds();
allPreds += getRelExpr().getGroupAttr()->getCharacteristicInputs();
NABoolean foundKey = FALSE;
ValueIdSet allReferencedBaseCols;
allPreds.findAllReferencedBaseCols(allReferencedBaseCols);
CollIndex x = 0;
const IndexDesc *iDesc = getIndexDesc();
const ValueIdList *currentIndexSortKey = &(iDesc->getOrderOfKeyValues());
for (x = 0; x < (*currentIndexSortKey).entries() && !foundKey; x++)
{
ValueId firstkey = (*currentIndexSortKey)[x];
ItemExpr *cv;
NABoolean isaConstant = FALSE;
ValueId firstkeyCol;
ColAnalysis *colA = firstkey.baseColAnalysis(&isaConstant, firstkeyCol);
if (!colA) // no column analysis
break; // we can't go further, break out of the loop.
// skip computed columns and constant predicates
if (isaConstant || firstkeyCol.isSaltColumn() ||
firstkeyCol.isDivisioningColumn())
continue; // try next prefix column
if (colA->getConstValue(cv,FALSE/*useRefAConstExpr*/))
continue; // try next prefix column
// any predicate on first nonconstant prefix key column?
if (allReferencedBaseCols.containsTheGivenValue(firstkeyCol))
// nonconstant prefix key matches predicate
foundKey = TRUE;
else
break; // predicate is not a key predicate, cost it high
}
return foundKey;
}