def testfindExpectedDesign(self):
"""Perform the actual search for a design"""
# Generate all the design candidates
# Instantiate cost model
cmConfig = {
'weight_network': 4,
'weight_disk': 1,
'weight_skew': 1,
'nodes': 10,
'max_memory': 1024,
'skew_intervals': 10,
'address_size': 64,
'window_size': 500
}
cm = CostModel(self.collections, self.workload, cmConfig)
d0 = self.getManMadeDesign()
print d0
output_design = d0.toJSON()
cost0 = cm.overallCost(d0)
ds = Deserializer(output_design)
d1 = ds.Deserialize()
print d1
cost1 = cm.overallCost(d1)
self.assertEqual(cost1, cost0)
def outtestfindExpectedDesign(self):
"""Perform the actual search for a design"""
# Generate all the design candidates
# Instantiate cost model
cmConfig = {
'weight_network': 4,
'weight_disk': 1,
'weight_skew': 1,
'nodes': 10,
'max_memory': 1024,
'skew_intervals': 10,
'address_size': 64,
'window_size': 500
}
cm = CostModel(self.collections, self.workload, cmConfig)
initialDesign = InitialDesigner(self.collections, self.workload, None).generate()
upper_bound = cm.overallCost(initialDesign)
print "init solution: ", initialDesign
print "init solution cost: ", upper_bound
collectionNames = [c for c in self.collections]
dc = self.dc.getCandidates(collectionNames)
print "candidates: ", dc
ln = LNSDesigner(self.collections, \
self.dc, \
self.workload, \
None, \
cm, \
initialDesign, \
upper_bound, \
LNS_RUN_TIME)
solution = ln.solve()
print "Best cost: ", ln.bestCost
print "solution: ", solution
def load(self, replay=False, replay_design=None, init=False):
"""Perform the actual search for a design"""
isShardingEnabled = self.config.getboolean(configutil.SECT_DESIGNER, 'enable_sharding')
isIndexesEnabled = self.config.getboolean(configutil.SECT_DESIGNER, 'enable_indexes')
isDenormalizationEnabled = self.config.getboolean(configutil.SECT_DESIGNER, 'enable_denormalization')
self.collections = self.loadCollections()
self.workload = self.loadWorkload(self.collections)
# Generate all the design candidates
self.designCandidates = self.generateDesignCandidates(self.collections, isShardingEnabled, isIndexesEnabled, isDenormalizationEnabled)
#LOG.info("candidates: %s\n", self.designCandidates)
# Instantiate cost model
cmConfig = {
'weight_network': self.config.getfloat(configutil.SECT_COSTMODEL, 'weight_network'),
'weight_disk': self.config.getfloat(configutil.SECT_COSTMODEL, 'weight_disk'),
'weight_skew': self.config.getfloat(configutil.SECT_COSTMODEL, 'weight_skew'),
'nodes': self.config.getint(configutil.SECT_CLUSTER, 'nodes'),
'max_memory': self.config.getint(configutil.SECT_CLUSTER, 'node_memory'),
'skew_intervals': self.config.getint(configutil.SECT_COSTMODEL, 'time_intervals'),
'address_size': self.config.getint(configutil.SECT_COSTMODEL, 'address_size'),
'window_size': self.config.getint(configutil.SECT_COSTMODEL, 'window_size')
}
self.cm = CostModel(self.collections, self.workload, cmConfig)
# if self.debug:
# state.debug = True
# costmodel.LOG.setLevel(logging.DEBUG)
# Compute initial solution and calculate its cost
# This will be the upper bound from starting design
if not replay:
initialDesign = InitialDesigner(self.collections, self.workload, self.config).generate()
if init:
print initialDesign.toJSON()
#import pycallgraph
#pycallgraph.start_trace()
initialCost = self.cm.overallCost(initialDesign)
#pycallgraph.make_dot_graph('d4.png')
return initialCost, initialDesign
else:
self.cm.overallCost(replay_design)
return None
def testfindExpectedDesign(self):
"""Perform the actual search for a design"""
# Generate all the design candidates
# Instantiate cost model
cmConfig = {
'weight_network': 4,
'weight_disk': 1,
'weight_skew': 1,
'nodes': 10,
'max_memory': 1024,
'skew_intervals': 10,
'address_size': 64,
'window_size': 500
}
cm = CostModel(self.collections, self.workload, cmConfig)
d0 = self.getManMadeDesign()
cost0 = cm.overallCost(d0)
d1 = d0.copy()
d1.setDenormalizationParent(tpccConstants.TABLENAME_ORDER_LINE,
tpccConstants.TABLENAME_ORDERS)
cost1 = cm.overallCost(d1)
self.assertLess(cost1, cost0)
