def import_model(path):
# We need to use both: the metadata and the created model, as we do not
# want to reproduce the model creation here
global MODEL
logger = get_logger()
logger.setLevel("INFO")
logger.info("importing model from %s" % path)
metadata = read_model_metadata(path)
cube_list = metadata.pop("cubes", [])
for i, cube in enumerate(cube_list):
cube_id = i + 1
cube["id"] = cube_id
CUBES[str(cube_id)] = cube
dim_list = metadata.pop("dimensions", [])
for i, dim in enumerate(dim_list):
dim = fix_dimension_metadata(dim)
dim_id = i + 1
dim["id"] = dim_id
DIMENSIONS[str(dim_id)] = dim
MODEL = metadata
def create_workspace(config_file):
global WORKSPACE
global ENGINE
logger = get_logger()
logger.setLevel("INFO")
logger.info("cretating workspace from %s" % config_file)
WORKSPACE = Workspace(config=config_file)
ENGINE = engine(WORKSPACE)
def create_workspace(config_file):
global WORKSPACE
global ENGINE
logger = get_logger()
logger.setLevel("INFO")
logger.info("cretating workspace from %s" % config_file)
WORKSPACE = Workspace(config=config_file)
ENGINE = engine(WORKSPACE)
def import_model(path):
# We need to use both: the metadata and the created model, as we do not
# want to reproduce the model creation here
global MODEL
cube_id_sequence = count(1)
dimension_id_sequence = count(1)
logger = get_logger()
logger.setLevel("INFO")
logger.info("importing model from %s" % path)
metadata = read_model_metadata(path)
cube_list = metadata.pop("cubes", [])
for i, cube in enumerate(cube_list):
cube_id = cube_id_sequence.next()
cube["id"] = cube_id
CUBES[str(cube_id)] = cube
dim_list = metadata.pop("dimensions", [])
for i, dim in enumerate(dim_list):
dim = expand_dimension_metadata(dim)
dim_id = dimension_id_sequence.next()
dim["id"] = dim_id
DIMENSIONS[str(dim_id)] = dim
MODEL = metadata
# Convert joins (of known types)
# TODO: currently we assume that all JOINS are SQL joins as we have no way
# to determine actual store and therefore the backend used for
# interpreting this model
joins = metadata.pop("joins", [])
for join in joins:
if "detail" in join:
join["detail"] = _fix_sql_join_value(join["detail"])
if "master" in join:
join["master"] = _fix_sql_join_value(join["master"])
join["__type__"] = "sql"
MODEL["joins"] = joins
def import_model(path):
# We need to use both: the metadata and the created model, as we do not
# want to reproduce the model creation here
global MODEL
cube_id_sequence = count(1)
dimension_id_sequence = count(1)
logger = get_logger()
logger.setLevel("INFO")
logger.info("importing model from %s" % path)
metadata = read_model_metadata(path)
cube_list = metadata.pop("cubes", [])
for i, cube in enumerate(cube_list):
cube_id = cube_id_sequence.next()
cube["id"] = cube_id
CUBES[str(cube_id)] = cube
dim_list = metadata.pop("dimensions", [])
for i, dim in enumerate(dim_list):
dim = expand_dimension_metadata(dim)
dim_id = dimension_id_sequence.next()
dim["id"] = dim_id
DIMENSIONS[str(dim_id)] = dim
MODEL = metadata
# Convert joins (of known types)
# TODO: currently we assume that all JOINS are SQL joins as we have no way
# to determine actual store and therefore the backend used for
# interpreting this model
joins = metadata.pop("joins", [])
for join in joins:
if "detail" in join:
join["detail"] = _fix_sql_join_value(join["detail"])
if "master" in join:
join["master"] = _fix_sql_join_value(join["master"])
join["__type__"] = "sql"
MODEL["joins"] = joins
from flask import Flask, render_template, request, g
import cubes
import os.path
import sqlalchemy
import logging
logger = cubes.get_logger()
logger.setLevel(logging.DEBUG)
app = Flask(__name__)
#
# Data we aregoing to browse and logical model of the data
#
APP_ROOT = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(APP_ROOT, "vvo_model.json")
DB_PATH = os.path.join(APP_ROOT, "vvo_data.sqlite")
DB_URL = "sqlite:///" + DB_PATH
CUBE_NAME = "contracts"
# Some global variables. We do not have to care about Flask provided thread
# safety here, as they are non-mutable.
workspace = None
model = None
@app.route("/")