def pickJoinOrder(self, plan):
# Extract all base relations, along with any unary operators immediately above.
base_relations = set(plan.root.inputs())
# Extract all joins in original plan, they serve as the set of joins actually necessary.
joins = plan.root.joins()
# Define the dynamic programming table.
optimal_plans = {}
# Establish optimal access paths.
for relation in base_relations:
optimal_plans[frozenset((relation,))] = relation
# Fill in the table.
for i in range(2, len(base_relations) + 1):
for subset in itertools.combinations(base_relations, i):
# Build the set of candidate joins.
candidate_joins = set()
for candidate_relation in subset:
candidate_joins.add((
optimal_plans[frozenset(tuple_without(subset, candidate_relation))],
optimal_plans[frozenset((candidate_relation,))]
))
# Find the best of the candidate joins.
optimal_plans[frozenset(subset)] = self.get_best_join(candidate_joins, joins)
# Reconstruct the best plan, prepare and return.
final_plan = Plan(root = optimal_plans[frozenset(base_relations)])
final_plan.prepare(self.db)
plan = final_plan
return final_plan
def pickJoinOrder(self, plan):
# Extract all base relations, along with any unary operators immediately above.
base_relations = set(plan.sources)
# Extract all joins in original plan, they serve as the set of joins actually necessary.
joins = set(plan.joins)
# Define the dynamic programming table.
optimal_plans = {}
# Establish optimal access paths.
for relation in base_relations:
optimal_plans[frozenset((relation,))] = relation
# Fill in the table.
for i in range(2, len(base_relations) + 1):
for subset in itertools.combinations(base_relations, i):
# Build the set of candidate joins.
candidate_joins = set()
for candidate_relation in subset:
candidate_joins.add((
optimal_plans[frozenset(tuple_without(subset, candidate_relation))],
optimal_plans[frozenset((candidate_relation,))]
))
# Find the best of the candidate joins.
optimal_plans[frozenset(subset)] = self.get_best_join(candidate_joins, joins)
# Reconstruct the best plan, prepare and return.
final_plan = Plan(root = optimal_plans[frozenset(base_relations)])
final_plan.prepare(self.db)
return final_plan
def get_best_join(self, candidates, required_joins): best_plan_cost = None best_plan = None for left, right in candidates: relevant_expr = None # Find the joinExpr corresponding to the current join candidate. If there is none, it's a # cartesian product. for join in required_joins: names = ExpressionInfo(join.joinExpr).getAttributes() if set(join.rhsSchema.fields).intersection(names) and set(join.lhsSchema.fields).intersection(names): relevant_expr = join.joinExpr break else: relevant_expr = 'True' # Construct a join plan for the current candidate, for each possible join algorithm. # TODO: Evaluate more than just nested loop joins, and determine feasibility of those methods. for algorithm in ["nested-loops", "block-nested-loops", "hash"]: test_plan = Plan(root = Join( lhsPlan = left, rhsPlan = right, method = algorithm, expr = relevant_expr )) # Prepare and run the plan in sampling mode, and get the estimated cost. test_plan.prepare(self.db) test_plan.sample(5.0) cost = test_plan.cost(estimated = True) # Update running best. if best_plan_cost is None or cost < best_plan_cost: best_plan_cost = cost best_plan = test_plan # Need to return the root operator rather than the plan itself, since it's going back into the # table. return best_plan.root
def get_best_join(self, candidates, required_joins):
best_plan_cost = None
best_plan = None
for left, right in candidates:
relevant_expr = None
# Find the joinExpr corresponding to the current join candidate. If there is none, it's a
# cartesian product.
for join in required_joins:
names = ExpressionInfo(join.joinExpr).getAttributes()
if set(join.rhsSchema.fields).intersection(names) and set(join.lhsSchema.fields).intersection(names):
relevant_expr = join.joinExpr
break
else:
relevant_expr = 'True'
# Construct a join plan for the current candidate, for each possible join algorithm.
# TODO: Evaluate more than just nested loop joins, and determine feasibility of those methods.
for algorithm in ["nested-loops", "block-nested-loops"]:
test_plan = Plan(root = Join(
lhsPlan = left,
rhsPlan = right,
method = algorithm,
expr = relevant_expr
))
# Prepare and run the plan in sampling mode, and get the estimated cost.
test_plan.prepare(self.db)
test_plan.sample(1.0)
cost = test_plan.cost(estimated = True)
# Update running best.
if best_plan_cost is None or cost < best_plan_cost:
best_plan_cost = cost
best_plan = test_plan
# Need to return the root operator rather than the plan itself, since it's going back into the
# table.
return best_plan.root
def pickJoinOrder(self, plan):
joins, tableIDs, optimalSubPlans, fields, nubPlan = self.optimizerSetup(
plan)
# Joins is a list of joins
# TableIDs is a list of the operator on top of a tableScan or the scan itself (Select, Projcect)
# optimalSubPlan is a dictionary where the key is the top operator ID (from TableID) and val is the operator
# fields is a dictionary where key is top operator ID, val is the dictionary of fields
if len(joins) == 0:
return plan
numTables = 2
while numTables <= len(tableIDs):
print('NumTables: ', numTables)
joinOrderings = itertools.combinations(tableIDs, numTables)
# Check each ordering, check each join method
# Start with two tables total
# pick one as the LHS, one as the RHS
for joinOrdering in joinOrderings: # This iterates through subsets of size numTables
bestCost = 1e99
bestPlan = None
for rhsID in joinOrdering: # Eventually we'll even iterate through swapping 2-joins
lhsIDs = list(joinOrdering)
lhsIDs.remove(rhsID) # Make this one the right side join
lhsKey = frozenset(lhsIDs) # Key for optimalSubPlan dict
rhsKey = frozenset([rhsID]) # Key for optimalSubPlan dict
cachedLHS = optimalSubPlans[
lhsKey] if lhsKey in optimalSubPlans else None # Get the optimal subPlan
cachedRHS = optimalSubPlans[
rhsKey] # Get the optimal subPlan
if cachedLHS is None or cachedRHS is None:
continue
# Do we even care about doing this join?
allAttributes = []
for lhsID in lhsIDs:
allAttributes.extend(fields[frozenset([
lhsID
])]) # These are all the attributes in the join
allAttributes.extend(fields[rhsKey])
contains, planDict = self.checkJoins(
joins, allAttributes, cachedLHS, cachedRHS)
# print('Got to Contains')
if contains:
# print('PlanDict: ', planDict)
for joinMethod in [
"hash", "nested-loops", "block-nested-loops"
]:
if joinMethod == "hash":
# print('HashMethod')
lhsPlan = cachedLHS
rhsPlan = cachedRHS
lhsHashFn = planDict['lhsHashFn']
rhsHashFn = planDict['rhsHashFn']
lhsKeySchema = planDict['lhsKeySchema']
rhsKeySchema = planDict['rhsKeySchema']
tryPlan = Plan(
root=Join(method=joinMethod,
lhsPlan=cachedLHS,
lhsHashFn=lhsHashFn,
lhsKeySchema=lhsKeySchema,
rhsPlan=cachedRHS,
rhsHashFn=rhsHashFn,
rhsKeySchema=rhsKeySchema))
self.checkPlan = tryPlan
tryPlan.prepare(self.db)
tryPlan.sample(1.0)
cost = tryPlan.cost(estimated=True)
# print('HashCost: ', cost)
else:
# print(joinMethod)
joinExpr = planDict['joinExpr']
tryPlan = Plan(root=Join(lhsPlan=cachedLHS,
rhsPlan=cachedRHS,
method=joinMethod,
expr=joinExpr))
self.checkPlan = tryPlan
tryPlan.prepare(self.db)
tryPlan.sample(1.0)
cost = tryPlan.cost(estimated=True)
# print(joinMethod + ' Cost: ', cost)
if cost < bestCost:
bestCost = cost
bestPlan = tryPlan
newKey = frozenset(joinOrdering)
self.addPlanCost(newKey, bestCost, bestPlan)
optimalSubPlans[
newKey] = bestPlan.root if bestPlan is not None else None
numTables += 1
nubPlan.subPlan = self.statsCache[frozenset(tableIDs)][1].root
plan.prepare(self.db)
return plan
def pickJoinOrder(self, plan):
relations = plan.relations()
fieldDict = self.obtainFieldDict(plan)
(joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)
# makes dicts that maps a list of relations to exprs involving that list
# then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C
# and on top of it the select exprs that involve 2 tables A,C or B,C
isGroupBy = True if plan.root.operatorType() == "GroupBy" else False
outputSchema = plan.schema()
self.reportPlanCount = 0
worklist = []
for r in relations:
table = TableScan(r,self.db.relationSchema(r))
table.prepare(self.db)
if (r,) in selectTablesDict:
selectExprs = selectTablesDict[(r,)]
selectString = self.combineSelects(selectExprs)
select = Select(table,selectString)
select.prepare(self.db)
worklist.append(Plan(root=select))
else:
worklist.append(Plan(root=table))
while(len(worklist) > 1):
combos = itertools.combinations(worklist,2)
bestJoin = None
sourcePair = None
for pair in combos:
op1 = pair[0].root
op2 = pair[1].root
selectExpr = self.createExpression(pair[0].relations(), pair[1].relations(), selectTablesDict)
joinExpr = self.createExpression(pair[0].relations(), pair[1].relations(), joinTablesDict)
join1BnljOp = Join(op1, op2, expr=joinExpr, method="block-nested-loops" )
join2BnljOp = Join(op2, op1, expr=joinExpr, method="block-nested-loops" )
join1NljOp = Join(op1, op2, expr=joinExpr, method="nested-loops" )
join2NljOp = Join(op2, op1, expr=joinExpr, method="nested-loops" )
if selectExpr == "True":
full1BnljOp = join1BnljOp
full2BnljOp = join2BnljOp
full1NljOp = join1NljOp
full2NljOp = join2NljOp
else:
full1BnljOp = Select(join1BnljOp, selectExpr)
full2BnljOp = Select(join2BnljOp, selectExpr)
full1NljOp = Select(join1NljOp, selectExpr)
full2NljOp = Select(join2NljOp, selectExpr)
joinList = [full1BnljOp, full2BnljOp, full1NljOp, full2NljOp]
for j in joinList:
joinplan = Plan(root=j)
joinplan.prepare(self.db)
joinplan.sample(100)
if bestJoin == None or joinplan.cost(True) < bestJoin.cost(True):
bestJoin = joinplan
sourcePair = pair
self.reportPlanCount += 4
self.clearSampleFiles()
worklist.remove(sourcePair[0])
worklist.remove(sourcePair[1])
worklist.append(bestJoin)
# after System R algorithm
newPlan = worklist[0]
if isGroupBy:
newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \
aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \
aggExprs=plan.root.aggExprs, \
groupHashFn=plan.root.groupHashFn)
newGroupBy.prepare(self.db)
newPlan = Plan(root=newGroupBy)
if set(outputSchema.schema()) != set(newPlan.schema().schema()):
projectDict = {}
for f, t in outputSchema.schema():
projectDict[f] = (f, t)
currRoot = newPlan.root
project = Project(currRoot, projectDict)
project.prepare(self.db)
newPlan = Plan(root=project)
return newPlan
def pickJoinOrder(self, plan):
relations = plan.relations()
fieldDict = self.obtainFieldDict(plan)
(joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)
# makes dicts that maps a list of relations to exprs involving that list
# then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C
# and on top of it the select exprs that involve 2 tables A,C or B,C
isGroupBy = True if plan.root.operatorType() == "GroupBy" else False
outputSchema = plan.schema()
optDict = {}
self.reportPlanCount = 0
for npass in range(1, len(relations) + 1):
if npass == 1:
for r in relations:
table = TableScan(r,self.db.relationSchema(r))
if (r,) in selectTablesDict:
selectExprs = selectTablesDict[(r,)]
selectString = self.combineSelects(selectExprs)
select = Select(table,selectString)
optDict[(r,)] = Plan(root=select)
else:
optDict[(r,)] = Plan(root=table)
self.reportPlanCount += 1
else:
combinations = itertools.combinations(relations,npass)
for c in combinations:
fullList = sorted(c)
clist = self.getCombos(fullList)
bestJoin = None
for subcombo in clist:
complement = self.getComplement(fullList, subcombo)
leftOps = optDict[tuple(complement)].root
rightOps = optDict[tuple(subcombo)].root
selectExpr = self.createExpression(complement, subcombo, selectTablesDict)
joinExpr = self.createExpression(complement, subcombo, joinTablesDict)
joinBnljOp = Join(leftOps, rightOps, expr=joinExpr, method="block-nested-loops" )
fullBnljOp = Select(joinBnljOp, selectExpr)
if selectExpr == "True":
joinBnlj = Plan(root=joinBnljOp)
else:
joinBnlj = Plan(root=fullBnljOp)
joinBnlj.prepare(self.db)
joinBnlj.sample(100)
joinNljOp = Join(leftOps, rightOps, expr=joinExpr, method="nested-loops" )
fullNljOp = Select(joinNljOp, selectExpr)
if selectExpr == "True":
joinNlj = Plan(root=joinNljOp)
else:
joinNlj = Plan(root=fullNljOp)
joinNlj.prepare(self.db)
joinNlj.sample(100)
if joinBnlj.cost(True) < joinNlj.cost(True):
if bestJoin == None or joinBnlj.cost(True) < bestJoin.cost(True):
bestJoin = joinBnlj
else:
if bestJoin == None or joinNlj.cost(True) < bestJoin.cost(True):
bestJoin = joinNlj
self.reportPlanCount += 2
self.clearSampleFiles()
optDict[tuple(fullList)] = bestJoin
# after System R algorithm
newPlan = optDict[tuple(sorted(relations))]
if isGroupBy:
newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \
aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \
aggExprs=plan.root.aggExprs, \
groupHashFn=plan.root.groupHashFn)
newGroupBy.prepare(self.db)
newPlan = Plan(root=newGroupBy)
if set(outputSchema.schema()) != set(newPlan.schema().schema()):
projectDict = {}
for f, t in outputSchema.schema():
projectDict[f] = (f, t)
currRoot = newPlan.root
project = Project(currRoot, projectDict)
project.prepare(self.db)
newPlan = Plan(root=project)
return newPlan
def pickJoinOrder(self, plan):
self.numPlansConsidered = 0
tableIds = list()
joinOps = list()
optPlans = dict()
fields = dict()
self.extractJoinInfo(plan, tableIds, joinOps, optPlans, fields)
# Create worklist consisting of table IDs
worklist = [str(tableId) for tableId in tableIds]
# Now work our way down the 'worklist' greedily
numTables = len(worklist)
while numTables >= 2:
# Choose the cheapest join that can be made over the remaining sub-plans
minCost = None
optPlan = None
optLhsId = None
optRhsId = None
possibleJoinOrders = itertools.combinations(worklist, 2)
for possibleJoinOrder in possibleJoinOrders:
# Start examining each possible plan
lhsId = possibleJoinOrder[0]
rhsId = possibleJoinOrder[1]
lhsOpt = optPlans[lhsId] if lhsId in optPlans else None
rhsOpt = optPlans[rhsId] if rhsId in optPlans else None
if lhsOpt is None or rhsOpt is None:
continue # Skip irrelevant joins
# This is to take care of multi-way joins added to worklist
lhsIds = lhsId.split(",")
rhsIds = rhsId.split(",")
# Form a list of available attributes of this join
allAttrs = list()
for lId in lhsIds:
allAttrs.extend(fields[int(lId)])
for rId in rhsIds:
allAttrs.extend(fields[int(rId)])
currJoinExpr = None
# Check whether any join expression can be satisfied with this join
for join in joinOps:
if join.joinExpr:
joinAttrs = ExpressionInfo(join.joinExpr).getAttributes()
if self.contains(allAttrs, joinAttrs):
currJoinExpr = join.joinExpr
break
if currJoinExpr is None:
continue # Skip irrelevant joins
self.numPlansConsidered += 2
# Compare costs of different type of joins
for joinMethod in ["nested-loops", "block-nested-loops"]:
possiblePlan = Plan(root=Join(lhsPlan=lhsOpt, rhsPlan=rhsOpt, method=joinMethod, expr=currJoinExpr))
possiblePlan.prepare(self.db)
possiblePlan.sample(1.0) # Sampling causes too much overhead!
cost = self.getPlanCost(plan)
cost = possiblePlan.cost(estimated=True) if cost is None else cost
self.addPlanCost(plan, cost)
if minCost is None or cost < minCost:
minCost = cost
optPlan = possiblePlan
optLhsId = lhsId
optRhsId = rhsId
# Switch left and right and compare again
for joinMethod in ["nested-loops", "block-nested-loops"]:
possiblePlan = Plan(root=Join(lhsPlan=rhsOpt, rhsPlan=lhsOpt, method=joinMethod, expr=currJoinExpr))
possiblePlan.prepare(self.db)
possiblePlan.sample(1.0) # Sampling causes too much overhead!
cost = self.getPlanCost(plan)
cost = possiblePlan.cost(estimated=True) if cost is None else cost
self.addPlanCost(plan, cost)
if minCost is None or cost < minCost:
minCost = cost
optPlan = possiblePlan
optLhsId = rhsId
optRhsId = lhsId
if optPlan is not None:
# Update optimal plan
joinKey = optLhsId + "," + optRhsId
optPlans[joinKey] = optPlan.root
# Update worklist
worklist.remove(optLhsId)
worklist.remove(optRhsId)
worklist.append(joinKey)
numTables = numTables - 1
# Return single plan left in worklist
return optPlans[worklist[0]]
def pickJoinOrder(self, plan):
relations = plan.relations()
fieldDict = self.obtainFieldDict(plan)
(joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)
# makes dicts that maps a list of relations to exprs involving that list
# then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C
# and on top of it the select exprs that involve 2 tables A,C or B,C
isGroupBy = True if plan.root.operatorType() == "GroupBy" else False
outputSchema = plan.schema()
self.reportPlanCount = 0
worklist = []
for r in relations:
table = TableScan(r, self.db.relationSchema(r))
table.prepare(self.db)
if (r, ) in selectTablesDict:
selectExprs = selectTablesDict[(r, )]
selectString = self.combineSelects(selectExprs)
select = Select(table, selectString)
select.prepare(self.db)
worklist.append(Plan(root=select))
else:
worklist.append(Plan(root=table))
while (len(worklist) > 1):
combos = itertools.combinations(worklist, 2)
bestJoin = None
sourcePair = None
for pair in combos:
op1 = pair[0].root
op2 = pair[1].root
selectExpr = self.createExpression(pair[0].relations(),
pair[1].relations(),
selectTablesDict)
joinExpr = self.createExpression(pair[0].relations(),
pair[1].relations(),
joinTablesDict)
join1BnljOp = Join(op1,
op2,
expr=joinExpr,
method="block-nested-loops")
join2BnljOp = Join(op2,
op1,
expr=joinExpr,
method="block-nested-loops")
join1NljOp = Join(op1,
op2,
expr=joinExpr,
method="nested-loops")
join2NljOp = Join(op2,
op1,
expr=joinExpr,
method="nested-loops")
if selectExpr == "True":
full1BnljOp = join1BnljOp
full2BnljOp = join2BnljOp
full1NljOp = join1NljOp
full2NljOp = join2NljOp
else:
full1BnljOp = Select(join1BnljOp, selectExpr)
full2BnljOp = Select(join2BnljOp, selectExpr)
full1NljOp = Select(join1NljOp, selectExpr)
full2NljOp = Select(join2NljOp, selectExpr)
joinList = [full1BnljOp, full2BnljOp, full1NljOp, full2NljOp]
for j in joinList:
joinplan = Plan(root=j)
joinplan.prepare(self.db)
joinplan.sample(100)
if bestJoin == None or joinplan.cost(True) < bestJoin.cost(
True):
bestJoin = joinplan
sourcePair = pair
self.reportPlanCount += 4
self.clearSampleFiles()
worklist.remove(sourcePair[0])
worklist.remove(sourcePair[1])
worklist.append(bestJoin)
# after System R algorithm
newPlan = worklist[0]
if isGroupBy:
newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \
aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \
aggExprs=plan.root.aggExprs, \
groupHashFn=plan.root.groupHashFn)
newGroupBy.prepare(self.db)
newPlan = Plan(root=newGroupBy)
if set(outputSchema.schema()) != set(newPlan.schema().schema()):
projectDict = {}
for f, t in outputSchema.schema():
projectDict[f] = (f, t)
currRoot = newPlan.root
project = Project(currRoot, projectDict)
project.prepare(self.db)
newPlan = Plan(root=project)
return newPlan
def pickJoinOrder(self, plan):
relations = plan.relations()
fieldDict = self.obtainFieldDict(plan)
(joinTablesDict, selectTablesDict) = self.getExprDicts(plan, fieldDict)
# makes dicts that maps a list of relations to exprs involving that list
# then in system R we will build opt(A,B) Join C using join exprs involving A,C and B,C
# and on top of it the select exprs that involve 2 tables A,C or B,C
isGroupBy = True if plan.root.operatorType() == "GroupBy" else False
outputSchema = plan.schema()
optDict = {}
self.reportPlanCount = 0
for npass in range(1, len(relations) + 1):
if npass == 1:
for r in relations:
table = TableScan(r, self.db.relationSchema(r))
if (r, ) in selectTablesDict:
selectExprs = selectTablesDict[(r, )]
selectString = self.combineSelects(selectExprs)
select = Select(table, selectString)
optDict[(r, )] = Plan(root=select)
else:
optDict[(r, )] = Plan(root=table)
self.reportPlanCount += 1
else:
combinations = itertools.combinations(relations, npass)
for c in combinations:
fullList = sorted(c)
clist = self.getCombos(fullList)
bestJoin = None
for subcombo in clist:
complement = self.getComplement(fullList, subcombo)
leftOps = optDict[tuple(complement)].root
rightOps = optDict[tuple(subcombo)].root
selectExpr = self.createExpression(
complement, subcombo, selectTablesDict)
joinExpr = self.createExpression(
complement, subcombo, joinTablesDict)
joinBnljOp = Join(leftOps,
rightOps,
expr=joinExpr,
method="block-nested-loops")
fullBnljOp = Select(joinBnljOp, selectExpr)
if selectExpr == "True":
joinBnlj = Plan(root=joinBnljOp)
else:
joinBnlj = Plan(root=fullBnljOp)
joinBnlj.prepare(self.db)
joinBnlj.sample(100)
joinNljOp = Join(leftOps,
rightOps,
expr=joinExpr,
method="nested-loops")
fullNljOp = Select(joinNljOp, selectExpr)
if selectExpr == "True":
joinNlj = Plan(root=joinNljOp)
else:
joinNlj = Plan(root=fullNljOp)
joinNlj.prepare(self.db)
joinNlj.sample(100)
if joinBnlj.cost(True) < joinNlj.cost(True):
if bestJoin == None or joinBnlj.cost(
True) < bestJoin.cost(True):
bestJoin = joinBnlj
else:
if bestJoin == None or joinNlj.cost(
True) < bestJoin.cost(True):
bestJoin = joinNlj
self.reportPlanCount += 2
self.clearSampleFiles()
optDict[tuple(fullList)] = bestJoin
# after System R algorithm
newPlan = optDict[tuple(sorted(relations))]
if isGroupBy:
newGroupBy = GroupBy(newPlan.root, groupSchema=plan.root.groupSchema, \
aggSchema=plan.root.aggSchema, groupExpr=plan.root.groupExpr, \
aggExprs=plan.root.aggExprs, \
groupHashFn=plan.root.groupHashFn)
newGroupBy.prepare(self.db)
newPlan = Plan(root=newGroupBy)
if set(outputSchema.schema()) != set(newPlan.schema().schema()):
projectDict = {}
for f, t in outputSchema.schema():
projectDict[f] = (f, t)
currRoot = newPlan.root
project = Project(currRoot, projectDict)
project.prepare(self.db)
newPlan = Plan(root=project)
return newPlan
def pickJoinOrder(self, plan):
# Some restrictions apply:
# 1. Cannot involve hash-joins or index-joins
# 2. Only join operations beyond certain point (i.e. cannot have join -> aggregation -> join, etc.)
tableIds = list()
joinOps = list()
optPlans = dict()
fields = dict()
firstOpWithJoins = self.extractJoinInfo(plan, tableIds, joinOps, optPlans, fields)
if len(joinOps) == 0:
return plan
numTables = 2
while numTables <= len(tableIds):
possibleJoinOrders = itertools.combinations(tableIds, numTables)
for possibleJoinOrder in possibleJoinOrders:
minCost = None
optPlan = None
for tableId in possibleJoinOrder:
# Left-deep-only optimizer (i.e. rhs operand is a base relation)
lhsIds = list(possibleJoinOrder)
lhsIds.remove(tableId)
lhsJoinKey = self.getJoinKey(lhsIds)
rhsJoinKey = str(tableId)
lhsOpt = optPlans[lhsJoinKey] if lhsJoinKey in optPlans else None
rhsOpt = optPlans[rhsJoinKey] if rhsJoinKey in optPlans else None
if lhsOpt is None or rhsOpt is None:
continue # Skip irrelevant joins
# Form a list of available attributes of this join
allAttrs = list()
for lhsId in lhsIds:
allAttrs.extend(fields[lhsId])
allAttrs.extend(fields[tableId])
currJoinExpr = None
# Check whether any join expression can be satisfied with this join
for join in joinOps:
if join.joinExpr:
joinAttrs = ExpressionInfo(join.joinExpr).getAttributes()
if self.contains(allAttrs, joinAttrs):
currJoinExpr = join.joinExpr
break
else:
# Should not involve hash-joins or index-joins (limitation)
return plan
if currJoinExpr is None:
continue # Irrelevant join
for joinMethod in ["nested-loops", "block-nested-loops"]:
possiblePlan = Plan(root=Join(lhsPlan=lhsOpt, rhsPlan=rhsOpt, method=joinMethod, expr=currJoinExpr))
possiblePlan.prepare(self.db)
possiblePlan.sample(1.0) # Sampling causes too much overhead!
cost = self.getPlanCost(plan)
cost = possiblePlan.cost(estimated=True) if cost is None else cost
self.addPlanCost(plan, cost)
if minCost is None or cost < minCost:
minCost = cost
optPlan = possiblePlan
optPlans[self.getJoinKey(possibleJoinOrder)] = None if optPlan is None else optPlan.root
numTables = numTables + 1
firstOpWithJoins.subPlan = optPlans[self.getJoinKey(tableIds)]
plan.prepare(self.db)
return plan