def conn():
engine = sqlalchemy.create_engine("sqlite://")
create_insert(engine)
executor = MdxEngine(sqla_engine=engine, source_type="db")
executor.load_cube(cube_name="main", fact_table_name="facts")
discover_request_hanlder = XmlaDiscoverReqHandler(executor)
execute_request_hanlder = XmlaExecuteReqHandler(executor)
print("spawning server")
application = Application(
[XmlaProviderService],
"urn:schemas-microsoft-com:xml-analysis",
in_protocol=Soap11(validator="soft"),
out_protocol=Soap11(validator="soft"),
config={
"discover_request_hanlder": discover_request_hanlder,
"execute_request_hanlder": execute_request_hanlder,
},
)
wsgi_application = WsgiApplication(application)
server = WSGIServer(application=wsgi_application, host=HOST, port=PORT)
server.start()
provider = xmla.XMLAProvider()
yield provider.connect(location=server.url)
print("stopping server")
drop_tables(engine)
server.stop()
def main():
file = open(
"DATABASES_bench_result" +
str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")),
"w",
)
file.write("Benchmarks are made with cpu :\n")
file.write(cpuinfo.get_cpu_info()["brand"] + "\n\n")
for idx, query in enumerate([query1, query6, query7, query9]):
file.write(
"Query {0} :\n".format(str(idx + 1)) + query +
"\n----------------------------------------------------------\n\n")
sqlalchemy_uri = os.environ.get("SQLALCHEMY_DATABASE_URI", "sqlite://")
db = urlparse(sqlalchemy_uri).path.replace("/", "")
engine = sqlalchemy.create_engine(sqlalchemy_uri)
mdx_engine = MdxEngine(sqla_engine=engine, source_type="db")
mdx_engine.load_cube(cube_name=db if db else "main",
fact_table_name="facts")
wsgi_application = get_wsgi_application(mdx_engine)
server = WSGIServer(application=wsgi_application, host=HOST, port=PORT)
server.start()
for config_file in os.listdir(os.path.join(os.getcwd(),
"db_config_files")):
dbms = str(config_file.split("_")[0])
try:
copy_2_olapy_dir(config_file)
# to refresh cubes from database
provider = xmla.XMLAProvider()
conn = provider.connect(location=server.url)
mbench = MicBench()
t = PrettyTable(
["Query", "{0} - olapy execution time".format(dbms)])
for idx, query in enumerate([query1, query6, query7, query9]):
if dbms.upper() in ["POSTGRES", "ORACLE"]:
query = fix_query_lowercase_db(query)
t.add_row([
"Query" + str(idx + 1),
str(mbench.bench(conn, query, CUBE_NAME))
])
file.write(str(t) + "\n\n")
except XMLAException:
type, value, traceback = sys.exc_info()
print("Error opening %s" % (value))
print("Can't connect to {0} database".format(dbms))
server.stop()
def benchmark():
mdx_executor = MdxEngine(cubes_folder="cubes_templates",
olapy_data_location="../../../olapy")
mdx_executor.load_cube(BENCH_CUBE)
xmla_discover_request_handler = XmlaDiscoverReqHandler(mdx_executor)
xmla_execute_request_handler = XmlaExecuteReqHandler(mdx_executor)
return run_benchmark(mdx_executor, xmla_discover_request_handler,
xmla_execute_request_handler)
def _get_xmla_tools():
olapy_data = os.path.join(expanduser("~"), "olapy-data")
mdx_executor = MdxEngine()
mdx_executor.load_cube(CUBE_NAME)
xmla_tools = XmlaDiscoverReqHandler(
executor=mdx_executor,
olapy_data=olapy_data,
source_type="csv",
db_config=None,
cubes_config=None,
)
return xmla_tools
def executor(request):
config = None
custom = False
if hasattr(request, "param"):
custom = request.param
config = cube_config
sqlalchemy_uri = "sqlite://"
else:
sqlalchemy_uri = os.environ.get("SQLALCHEMY_DATABASE_URI", "")
if not sqlalchemy_uri:
sqlalchemy_uri = "sqlite://"
db_test = urlparse(sqlalchemy_uri).path.replace("/", "")
engine = sqlalchemy.create_engine(sqlalchemy_uri)
create_insert(engine, custom)
mdx_engine = MdxEngine(sqla_engine=engine, source_type="db", cube_config=config)
mdx_engine.load_cube(
cube_name=db_test if db_test else "main", fact_table_name="facts"
)
yield mdx_engine
drop_tables(engine, custom)
def main():
file = open(
"bench_result" +
str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")), "w")
gen = CubeGen(number_dimensions=3, rows_length=1000, columns_length=5)
gen.generate_csv(gen.generate_cube(3, 1000))
mbench = MicBench()
file.write("Benchmarks are made with cpu :\n")
file.write(cpuinfo.get_cpu_info()["brand"] + "\n\n")
mdx_engine = MdxEngine()
mdx_engine.load_cube(CUBE_NAME)
wsgi_application = get_wsgi_application(mdx_engine)
server = WSGIServer(application=wsgi_application, host=HOST, port=PORT)
server.start()
provider = xmla.XMLAProvider()
conn = provider.connect(location=server.url)
olapy_query_execution_bench(file, mbench, conn, mdx_engine)
olapy_vs_mondrian(file, mbench, conn)
olapy_vs_iccube(file, mbench, conn)
olapy_profile(file)
gen.remove_temp_cube()
file.close()
server.stop()
def main():
executor = MdxEngine()
executor.load_cube("sales")
execution_result = executor.execute_mdx(mdx_query)
print(execution_result["result"])
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 1 14:35:13 2019
@author: patemi
"""
from olapy.core.mdx.executor import MdxEngine
executor = MdxEngine() # instantiate the MdxEngine
executor.load_cube('sales') # load sales cube