def setUp(self):
MongoDBTestCase.setUp(self)
random.seed(0) # Needed for TPC-C code
self.rng = random.Random(0)
self.timestamp = time.time()
self.query_id = 0l
self.resp_id = 0l
sp = scaleparameters.makeWithScaleFactor(TPCCTestCase.NUM_WAREHOUSES,
TPCCTestCase.SCALEFACTOR)
executor = Executor(sp)
# WORKLOAD
for i in xrange(TPCCTestCase.NUM_SESSIONS):
sess = self.metadata_db.Session()
sess['session_id'] = i
sess['ip_client'] = "client:%d" % (1234 + i)
sess['ip_server'] = "server:5678"
sess['start_time'] = self.timestamp
txn, params = executor.doOne()
if tpccConstants.TransactionTypes.DELIVERY == txn:
sess['operations'] = self.createDelivery(params)
elif tpccConstants.TransactionTypes.NEW_ORDER == txn:
sess['operations'] = self.createNewOrder(params)
elif tpccConstants.TransactionTypes.ORDER_STATUS == txn:
sess['operations'] = self.createOrderStatus(params)
elif tpccConstants.TransactionTypes.PAYMENT == txn:
sess['operations'] = self.createPayment(params)
elif tpccConstants.TransactionTypes.STOCK_LEVEL == txn:
sess['operations'] = self.createStockLevel(params)
else:
assert False, "Unexpected TransactionType: " + txn
sess['end_time'] = self.nextTimestamp(2)
sess.save()
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(TPCCTestCase.NUM_SESSIONS,
self.metadata_db.Session.find().count())
self.collections = dict([(c['name'], c)
for c in self.metadata_db.Collection.fetch()])
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = 10000
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info[
'avg_doc_size'] / (4 * 1024)
for k, v in col_info['fields'].iteritems():
if col_name == tpccConstants.TABLENAME_ORDER_LINE:
v['parent_col'] = tpccConstants.TABLENAME_ORDERS
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': 10,
'address_size': 64,
'nodes': 10,
'window_size': 10
}
self.state = State(self.collections, populated_workload,
self.costModelConfig)
def setUp(self):
MongoDBTestCase.setUp(self)
# WORKLOAD
timestamp = time.time()
for i in xrange(CostModelTestCase.NUM_SESSIONS):
sess = self.metadata_db.Session()
sess['session_id'] = i
sess['ip_client'] = "client:%d" % (1234 + i)
sess['ip_server'] = "server:5678"
sess['start_time'] = timestamp
for j in xrange(0, len(CostModelTestCase.COLLECTION_NAMES)):
_id = str(random.random())
queryId = long((i << 16) + j)
queryContent = {}
queryPredicates = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
for f in xrange(0, CostModelTestCase.NUM_FIELDS):
if j == 0:
f_name = "field%02d" % 0
elif j == 1:
f_name = "field%02d" % 1
else:
f_name = "field%02d" % 2
responseContent[f_name] = random.randint(0, 100)
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAMES[j]
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
## FOR (ops)
sess['end_time'] = timestamp
timestamp += 2
sess.save()
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(CostModelTestCase.NUM_SESSIONS,
self.metadata_db.Session.find().count())
self.collections = dict([(c['name'], c)
for c in self.metadata_db.Collection.fetch()])
self.assertEqual(len(CostModelTestCase.COLLECTION_NAMES),
len(self.collections))
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = CostModelTestCase.NUM_DOCUMENTS
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info[
'avg_doc_size'] / (4 * 1024)
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': CostModelTestCase.NUM_INTERVALS,
'address_size': 64,
'nodes': CostModelTestCase.NUM_NODES,
'window_size': 3
}
self.state = State(self.collections, populated_workload,
self.costModelConfig)
def setUp(self):
MongoDBTestCase.setUp(self)
field00_value = 0
field01_value = 0
field02_value = 9999999
# WORKLOAD
timestamp = time.time()
for i in xrange(CostModelTestCase.NUM_SESSIONS):
sess = self.metadata_db.Session()
sess['session_id'] = i
sess['ip_client'] = "client:%d" % (1234+i)
sess['ip_server'] = "server:5678"
sess['start_time'] = timestamp
for j in xrange(0, len(CostModelTestCase.COLLECTION_NAMES)):
_id = str(random.random())
queryId = long((i<<16) + j)
queryContent = { }
queryPredicates = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
for f in xrange(0, CostModelTestCase.NUM_FIELDS):
f_name = "field%02d" % f
if f == 0:
responseContent[f_name] = field00_value
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
field00_value += 1
elif f == 1:
responseContent[f_name] = field01_value
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
field01_value += 1
else:
responseContent[f_name] = field02_value
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
field02_value -= 1
## ELSE
## FOR
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAMES[j]
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
## FOR (ops)
sess['end_time'] = timestamp
timestamp += 2
sess.save()
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(CostModelTestCase.NUM_SESSIONS, self.metadata_db.Session.find().count())
self.collections = dict([ (c['name'], c) for c in self.metadata_db.Collection.fetch()])
self.assertEqual(len(CostModelTestCase.COLLECTION_NAMES), len(self.collections))
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = CostModelTestCase.NUM_DOCUMENTS
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info['avg_doc_size'] / (4 * 1024)
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': CostModelTestCase.NUM_INTERVALS,
'address_size': 64,
'nodes': CostModelTestCase.NUM_NODES,
'window_size': 1024
}
self.state = State(self.collections, populated_workload, self.costModelConfig)
## DEF
## CLASS
def setUp(self):
MongoDBTestCase.setUp(self)
# WORKLOAD
self.workload = []
timestamp = time.time()
for i in xrange(0, NUM_SESSIONS):
sess = self.metadata_db.Session()
sess['session_id'] = i
sess['ip_client'] = "client:%d" % (1234 + i)
sess['ip_server'] = "server:5678"
sess['start_time'] = timestamp
for j in xrange(0, len(COLLECTION_NAMES)):
_id = str(random.random())
queryId = long((i << 16) + j)
queryContent = {}
queryPredicates = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
for f in xrange(0, NUM_FIELDS):
f_name = "field%02d" % f
if f % 2 == 0:
responseContent[f_name] = random.randint(0, 100)
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
else:
responseContent[f_name] = str(
random.randint(1000, 100000))
## FOR
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = COLLECTION_NAMES[j]
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
## FOR (ops)
sess['end_time'] = timestamp
timestamp += 2
sess.save()
self.workload.append(sess)
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(NUM_SESSIONS, self.metadata_db.Session.find().count())
self.collections = dict([(c['name'], c)
for c in self.metadata_db.Collection.fetch()])
self.assertEqual(len(COLLECTION_NAMES), len(self.collections))
self.estimator = NodeEstimator(self.collections, NUM_NODES)
def setUp(self):
MongoDBTestCase.setUp(self)
random.seed(0) # Needed for TPC-C code
self.rng = random.Random(0)
self.timestamp = time.time()
self.query_id = 0l
self.resp_id = 0l
sp = scaleparameters.makeWithScaleFactor(TPCCTestCase.NUM_WAREHOUSES, TPCCTestCase.SCALEFACTOR)
executor = Executor(sp)
# WORKLOAD
for i in xrange(TPCCTestCase.NUM_SESSIONS):
sess = self.metadata_db.Session()
sess['session_id'] = i
sess['ip_client'] = "client:%d" % (1234+i)
sess['ip_server'] = "server:5678"
sess['start_time'] = self.timestamp
txn, params = executor.doOne()
if tpccConstants.TransactionTypes.DELIVERY == txn:
sess['operations'] = self.createDelivery(params)
elif tpccConstants.TransactionTypes.NEW_ORDER == txn:
sess['operations'] = self.createNewOrder(params)
elif tpccConstants.TransactionTypes.ORDER_STATUS == txn:
sess['operations'] = self.createOrderStatus(params)
elif tpccConstants.TransactionTypes.PAYMENT == txn:
sess['operations'] = self.createPayment(params)
elif tpccConstants.TransactionTypes.STOCK_LEVEL == txn:
sess['operations'] = self.createStockLevel(params)
else:
assert False, "Unexpected TransactionType: " + txn
sess['end_time'] = self.nextTimestamp(2)
sess.save()
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(TPCCTestCase.NUM_SESSIONS, self.metadata_db.Session.find().count())
self.collections = dict([ (c['name'], c) for c in self.metadata_db.Collection.fetch()])
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = 10000
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info['avg_doc_size'] / (4 * 1024)
for k,v in col_info['fields'].iteritems():
if col_name == tpccConstants.TABLENAME_ORDER_LINE:
v['parent_col'] = tpccConstants.TABLENAME_ORDERS
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': 10,
'address_size': 64,
'nodes': 10,
'window_size': 10
}
self.state = State(self.collections, populated_workload, self.costModelConfig)
def setUp(self):
MongoDBTestCase.setUp(self)
# WORKLOAD
self.workload = []
timestamp = time.time()
for i in xrange(0, NUM_SESSIONS):
sess = self.metadata_db.Session()
sess["session_id"] = i
sess["ip_client"] = "client:%d" % (1234 + i)
sess["ip_server"] = "server:5678"
sess["start_time"] = timestamp
for j in xrange(0, len(COLLECTION_NAMES)):
_id = str(random.random())
queryId = long((i << 16) + j)
queryContent = {}
queryPredicates = {}
responseContent = {"_id": _id}
responseId = queryId << 8
for f in xrange(0, NUM_FIELDS):
f_name = "field%02d" % f
if f % 2 == 0:
responseContent[f_name] = random.randint(0, 100)
queryContent[f_name] = responseContent[f_name]
queryPredicates[f_name] = constants.PRED_TYPE_EQUALITY
else:
responseContent[f_name] = str(random.randint(1000, 100000))
## FOR
queryContent = {constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent}
op = Session.operationFactory()
op["collection"] = COLLECTION_NAMES[j]
op["type"] = constants.OP_TYPE_QUERY
op["query_id"] = queryId
op["query_content"] = [queryContent]
op["resp_content"] = [responseContent]
op["resp_id"] = responseId
op["predicates"] = queryPredicates
op["query_time"] = timestamp
timestamp += 1
op["resp_time"] = timestamp
sess["operations"].append(op)
## FOR (ops)
sess["end_time"] = timestamp
timestamp += 2
sess.save()
self.workload.append(sess)
## FOR (sess)
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(NUM_SESSIONS, self.metadata_db.Session.find().count())
self.collections = dict([(c["name"], c) for c in self.metadata_db.Collection.fetch()])
self.assertEqual(len(COLLECTION_NAMES), len(self.collections))
self.estimator = NodeEstimator(self.collections, NUM_NODES)
def setUp(self):
MongoDBTestCase.setUp(self)
# WORKLOAD
timestamp = time.time()
sess = self.metadata_db.Session()
sess['session_id'] = 0
sess['ip_client'] = "client:%d" % (1234+0)
sess['ip_server'] = "server:5678"
sess['start_time'] = timestamp
# generate query 0 querying field00
_id = str(random.random())
queryId = long((0<<16) + 0)
queryContent = { }
queryPredicates = { }
projectionField = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
# generate query 1 querying field01
_id = str(random.random())
queryId = long((1<<16) + 1)
queryContent = { }
queryPredicates = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
projectionField = { }
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
# generate query 2 querying field00, field01
_id = str(random.random())
queryId = long((2<<16) + 2)
queryContent = { }
queryPredicates = { }
projectionField = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 3 querying field00, field01 but without projection field
_id = str(random.random())
queryId = long((2<<16) + 3)
queryContent = { }
queryPredicates = { }
projectionField = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 4 querying field00, field01 but it goes to collection 2
_id = str(random.random())
queryId = long((2<<16) + 4)
queryContent = { }
queryPredicates = { }
projectionField = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME_2
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 5 querying field00 but it goes to collection 3
_id = str(random.random())
queryId = long((2<<16) + 5)
queryContent = { }
queryPredicates = { }
projectionField = { }
responseContent = {"_id": _id}
responseId = (queryId<<8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = { constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent }
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME_3
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [ queryContent ]
op['resp_content'] = [ responseContent ]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
sess.save()
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(CostModelTestCase.NUM_SESSIONS, self.metadata_db.Session.find().count())
self.collections = dict([ (c['name'], c) for c in self.metadata_db.Collection.fetch()])
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = CostModelTestCase.NUM_DOCUMENTS
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info['avg_doc_size'] / (4 * 1024)
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': CostModelTestCase.NUM_INTERVALS,
'address_size': 64,
'nodes': CostModelTestCase.NUM_NODES,
'window_size': 10
}
self.state = State(self.collections, populated_workload, self.costModelConfig)
def setUp(self):
MongoDBTestCase.setUp(self)
# WORKLOAD
timestamp = time.time()
sess = self.metadata_db.Session()
sess['session_id'] = 0
sess['ip_client'] = "client:%d" % (1234 + 0)
sess['ip_server'] = "server:5678"
sess['start_time'] = timestamp
# generate query 0 querying field00
_id = str(random.random())
queryId = long((0 << 16) + 0)
queryContent = {}
queryPredicates = {}
projectionField = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
# generate query 1 querying field01
_id = str(random.random())
queryId = long((1 << 16) + 1)
queryContent = {}
queryPredicates = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
projectionField = {}
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
# generate query 2 querying field00, field01
_id = str(random.random())
queryId = long((2 << 16) + 2)
queryContent = {}
queryPredicates = {}
projectionField = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
projectionField['field02'] = random.randint(0, 100)
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 3 querying field00, field01 but without projection field
_id = str(random.random())
queryId = long((2 << 16) + 3)
queryContent = {}
queryPredicates = {}
projectionField = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
responseContent['field01'] = random.randint(0, 100)
queryContent['field01'] = responseContent['field01']
queryPredicates['field01'] = constants.PRED_TYPE_EQUALITY
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 4 querying field00, field01 but it goes to collection 2
_id = str(random.random())
queryId = long((2 << 16) + 4)
queryContent = {}
queryPredicates = {}
projectionField = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME_2
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
# generate query 5 querying field00 but it goes to collection 3
_id = str(random.random())
queryId = long((2 << 16) + 5)
queryContent = {}
queryPredicates = {}
projectionField = {}
responseContent = {"_id": _id}
responseId = (queryId << 8)
responseContent['field00'] = random.randint(0, 100)
queryContent['field00'] = responseContent['field00']
queryPredicates['field00'] = constants.PRED_TYPE_EQUALITY
queryContent = {
constants.REPLACE_KEY_DOLLAR_PREFIX + "query": queryContent
}
op = Session.operationFactory()
op['collection'] = CostModelTestCase.COLLECTION_NAME_3
op['type'] = constants.OP_TYPE_QUERY
op['query_id'] = queryId
op['query_content'] = [queryContent]
op['resp_content'] = [responseContent]
op['resp_id'] = responseId
op['predicates'] = queryPredicates
op['query_time'] = timestamp
op['query_fields'] = projectionField
timestamp += 1
op['resp_time'] = timestamp
sess['operations'].append(op)
sess['end_time'] = timestamp
timestamp += 1
sess.save()
# Use the MongoSniffConverter to populate our metadata
converter = MongoSniffConverter(self.metadata_db, self.dataset_db)
converter.no_mongo_parse = True
converter.no_mongo_sessionizer = True
converter.process()
self.assertEqual(CostModelTestCase.NUM_SESSIONS,
self.metadata_db.Session.find().count())
self.collections = dict([(c['name'], c)
for c in self.metadata_db.Collection.fetch()])
populated_workload = list(c for c in self.metadata_db.Session.fetch())
self.workload = populated_workload
# Increase the database size beyond what the converter derived from the workload
for col_name, col_info in self.collections.iteritems():
col_info['doc_count'] = CostModelTestCase.NUM_DOCUMENTS
col_info['avg_doc_size'] = 1024 # bytes
col_info['max_pages'] = col_info['doc_count'] * col_info[
'avg_doc_size'] / (4 * 1024)
col_info.save()
# print pformat(col_info)
self.costModelConfig = {
'max_memory': 1024, # MB
'skew_intervals': CostModelTestCase.NUM_INTERVALS,
'address_size': 64,
'nodes': CostModelTestCase.NUM_NODES,
'window_size': 10
}
self.state = State(self.collections, populated_workload,
self.costModelConfig)
