def convert_struct_field(column: StructField) -> TableDefinition.Column:
"""Converts a Spark StructField to a Tableau Hyper SqlType"""
if column.dataType == IntegerType():
sql_type = SqlType.int()
elif column.dataType == LongType():
sql_type = SqlType.big_int()
elif column.dataType == ShortType():
sql_type = SqlType.small_int()
elif column.dataType == DoubleType():
sql_type = SqlType.double()
elif column.dataType == FloatType():
sql_type = SqlType.double()
elif column.dataType == BooleanType():
sql_type = SqlType.bool()
elif column.dataType == DateType():
sql_type = SqlType.date()
elif column.dataType == TimestampType():
sql_type = SqlType.timestamp()
elif column.dataType == StringType():
sql_type = SqlType.text()
else:
# Trap the DecimalType case
if str(column.dataType).startswith("DecimalType"):
# Max precision is only up to 18 decimal places in Tableau Hyper API
precision = column.dataType.precision if column.dataType.precision <= 18 else 18
scale = column.dataType.scale
sql_type = SqlType.numeric(precision, scale)
else:
raise ValueError(f'Invalid StructField datatype for column `{column.name}` : {column.dataType}')
nullable = NULLABLE if column.nullable else NOT_NULLABLE
return TableDefinition.Column(name=column.name, type=sql_type, nullability=nullable)
def test_insert_data_into_hyper_file(self):
data = [(1001, "Jane", "Doe"), (1002, "John", "Doe"),
(2201, "Elonzo", "Smith")]
name = "output"
table_def = TableDefinition(
table_name=TableName("Extract", "Extract"),
columns=[
TableDefinition.Column(name=Name("id"),
type=SqlType.big_int(),
nullability=NULLABLE),
TableDefinition.Column(name=Name("first_name"),
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name=Name("last_name"),
type=SqlType.text(),
nullability=NULLABLE)
])
path = insert_data_into_hyper_file(data, name, table_def)
print(f'Database Path : {path}')
tables = TestUtils.get_tables("Extract",
"/tmp/hyperleaup/output/output.hyper")
assert (len(tables) == 1)
num_rows = TestUtils.get_row_count(
"Extract", "Extract", "/tmp/hyperleaup/output/output.hyper")
assert (num_rows == 3)
def convert_datatype(coldatatype):
"""
[summary]
This converts the datatype of the column of a given dataframe.
Args:
Datatype of the column in string format
Returns:
The tableau hyper extract compatible datatype after converting the dataframe datatype and a default value for NaN cases
"""
datatype = SqlType.text()
def_value = ''
if 'datetime' in coldatatype.lower():
datatype = SqlType.timestamp()
elif 'str' in coldatatype.lower():
datatype = SqlType.text()
elif 'boolean' in coldatatype.lower():
datatype = SqlType.bool()
elif 'int' in coldatatype.lower():
datatype = SqlType.int()
def_value = 0
elif 'float' in coldatatype.lower():
datatype = SqlType.double()
def_value = 0
elif 'period' in coldatatype.lower():
datatype = SqlType.interval()
elif 'object' in coldatatype.lower():
datatype = SqlType.text()
else:
datatype = SqlType.text()
return (datatype, def_value)
def createHyperTable():
columns = getConfig()
page_table = TableDefinition(table_name="Page")
for i in range(0, len(columns)):
if columns[i]['DataType'] == "String":
dt = SqlType.text()
elif columns[i]['DataType'] == "Date":
dt = SqlType.date()
else:
dt = SqlType.big_int()
page_table.add_column(columns[i]['ColumnName'], dt)
return page_table
def create():
with HyperProcess(Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
request_data = request.get_json()
print(request_data)
print("The HyperProcess has started.")
object_name = "mealprep.hyper"
file_name = os.environ.get('bucket_name')
with Connection(
endpoint=hyper.endpoint,
database=path_to_database,
create_mode=CreateMode.CREATE_AND_REPLACE) as connection:
print("The connection to the Hyper file is open.")
connection.catalog.create_schema('Extract')
example_table = TableDefinition(TableName('Extract', 'Extract'), [
TableDefinition.Column('Breakfast', SqlType.text()),
TableDefinition.Column('Lunch', SqlType.text()),
TableDefinition.Column('Dinner', SqlType.text()),
])
print("The table is defined.")
connection.catalog.create_table(example_table)
print(example_table)
print(type(example_table))
with Inserter(connection, example_table) as inserter:
for i in request_data['data']:
inserter.add_row([i['breakfast'], i['lunch'], i['dinner']])
inserter.execute()
print("The data was added to the table.")
print("The connection to the Hyper extract file is closed.")
print("The HyperProcess has shut down.")
with open('mealprep.hyper', 'rb') as reader:
if object_name is None:
object_name = file_name
s3_client = boto3.client(
's3',
aws_access_key_id=os.environ.get('aws_access_key_id'),
aws_secret_access_key=os.environ.get('aws_secret_access_key'))
try:
response = s3_client.upload_fileobj(reader, file_name,
object_name)
except ClientError as e:
logging.error(e)
return False
return redirect(url_for('index'))
def test_get_table_def(self):
data = [
(1001, 1, "Jane", "Doe", "2000-05-01", 29.0, False),
(1002, 2, "John", "Doe", "1988-05-03", 33.0, False),
(2201, 3, "Elonzo", "Smith", "1990-05-03", 21.0, True),
(None, None, None, None, None, None, None) # Test Nulls
]
df = get_spark_session()\
.createDataFrame(data, ["id", "dept_id", "first_name", "last_name", "dob", "age", "is_temp"])\
.createOrReplaceTempView("employees")
df = get_spark_session().sql(
"select id, cast(dept_id as short), first_name, "
"last_name, dob, age, is_temp from employees")
table_def = get_table_def(df, "Extract", "Extract")
# Ensure that the Table Name matches
assert (table_def.table_name.name == Name("Extract"))
# Ensure that the the TableDefinition column names match
assert (table_def.get_column(0).name == Name("id"))
assert (table_def.get_column(1).name == Name("dept_id"))
assert (table_def.get_column(2).name == Name("first_name"))
assert (table_def.get_column(3).name == Name("last_name"))
assert (table_def.get_column(4).name == Name("dob"))
assert (table_def.get_column(5).name == Name("age"))
assert (table_def.get_column(6).name == Name("is_temp"))
# Ensure that the column data types were converted correctly
assert (table_def.get_column(0).type == SqlType.big_int())
assert (table_def.get_column(1).type == SqlType.small_int())
assert (table_def.get_column(2).type == SqlType.text())
assert (table_def.get_column(3).type == SqlType.text())
assert (table_def.get_column(4).type == SqlType.text())
assert (table_def.get_column(5).type == SqlType.double())
assert (table_def.get_column(6).type == SqlType.bool())
def test_convert_struct_field(self):
# ensure strings can be converted correctly
first_name_col = StructField('first_name', StringType(), False)
converted_col = convert_struct_field(first_name_col)
assert (converted_col.name == Name('first_name'))
assert (converted_col.nullability is NOT_NULLABLE)
assert (converted_col.type == SqlType.text())
# ensure dates can be converted correctly
date_col = StructField('update_date', DateType(), True)
converted_col = convert_struct_field(date_col)
assert (converted_col.name == Name('update_date'))
assert (converted_col.nullability is NULLABLE)
assert (converted_col.type == SqlType.date())
# ensure timestamps can be converted correctly
timestamp_col = StructField('created_at', TimestampType(), False)
converted_col = convert_struct_field(timestamp_col)
assert (converted_col.name == Name('created_at'))
assert (converted_col.nullability is NOT_NULLABLE)
assert (converted_col.type == SqlType.timestamp())
def df_to_extract(df, output_path):
'''
Converts a Pandas dataframe to a Tableau Extract.
Parameters
----------
df (pandas dataframe): Dataframe to turn into a Tableau extract
output_path (str): Where to create the Tableau extract
'''
# Replace nan's with 0
df = df.replace(np.nan, 0.0, regex=True)
print('Creating Tableau data extract...')
with HyperProcess(telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyper:
with Connection(hyper.endpoint, output_path, CreateMode.CREATE_AND_REPLACE) as connection:
# Create schema
connection.catalog.create_schema('Extract')
# Create list of column definitions, based on the datatypes in pandas dataframe
dtype_map = {
'int32': SqlType.int(),
'int64': SqlType.big_int(),
'float32': SqlType.double(),
'float64': SqlType.double(),
'datetime64[ns]': SqlType.date(),
'object': SqlType.text()
}
table_def = []
# Get column headers to loop through them
df_columns = list(df)
for col_header in df_columns:
dtype_str = str(df[col_header].dtype)
# Use dtype_str to lookup appropiate SqlType from dtype_map and append new column to table definition
table_def.append(TableDefinition.Column(col_header, dtype_map[dtype_str]))
# Define table
extract_table = TableDefinition(TableName('Extract', 'Extract'), table_def)
# Create table
connection.catalog.create_table(extract_table)
# Insert data
with Inserter(connection, extract_table) as inserter:
for idx, row in df.iterrows():
inserter.add_row(row)
inserter.execute()
def createHyperFile():
dict = parseData()
file = "/Users/jharris/Desktop/workbookUsers.hyper"
cols = dict['cols']
data = dict['data']
with HyperProcess(
telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyper:
with Connection(hyper.endpoint, file,
CreateMode.CREATE_AND_REPLACE) as connection:
connection.catalog.create_schema('Extract')
table = TableDefinition(
TableName('Extract', 'Extract'),
[TableDefinition.Column(col, SqlType.text()) for col in cols])
connection.catalog.create_table(table)
with Inserter(connection, table) as inserter:
inserter.add_rows(rows=data)
inserter.execute()
def hyper_value_to_dss(self, value, tag=SqlType.text().tag):
"""
Convert the value `value` stored in a Hyper File under the storage type
`hyper_type_tag` to the apropriate DSS type.
>>> from tableauhyperapi import TypeTag, SqlType
>>> assert TypeTag.INT == SqlType.int().tag
:param value: Value from the Hyper Dataset
:param tag: Storage type under which the value is stored in Hyper
:return: output_value : Value compliant to Hyper
"""
try: # try to retrieve the conversion function
conversion_function = self.mapping_hyper_to_dss[tag][1]
except Exception as err:
logger.warning("Failed to retrieve the conversion function {}: {}".format(tag, err))
raise err
try:
output_value = conversion_function(value)
except Exception as err:
logger.warning("Failed to convert value {} of type {} stored as {}".format(value, type(value), tag))
raise err
return output_value
def run(self, args):
""" Runs the command
:param args: Arguments from argparse.Namespace
"""
input_file = Path(args.input_file)
print("Listing tables with spatial columns")
# Starts the Hyper Process with telemetry enabled to send data to Tableau.
# To opt out, simply set telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU.
with HyperProcess(
telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
# Connects to existing Hyper file
with Connection(endpoint=hyper.endpoint,
database=input_file) as connection:
catalog = connection.catalog
# Iterates over all schemas in the input file
for schema_name in catalog.get_schema_names():
# Iterates over all tables in the current schema
for table in catalog.get_table_names(schema=schema_name):
table_definition = catalog.get_table_definition(
name=table)
rows_in_table = connection.execute_scalar_query(
query=f"SELECT COUNT(*) FROM {table}")
spatial_columns = [
c.name for c in table_definition.columns
if c.type == SqlType.geography()
]
if spatial_columns:
print(
f"Table {table} with {rows_in_table} rows has"
f" {len(spatial_columns)} spatial columns: {spatial_columns}"
)
else:
print(
f"Table {table} with {rows_in_table} rows has no spatial columns"
)
import numpy as np
import pandas as pd
from tqdm import tqdm
from tableauhyperapi import (
HyperProcess,
Telemetry,
Connection,
SqlType,
TableDefinition,
CreateMode,
TableName,
Inserter,
)
dtype_mapper = {
"string": SqlType.text(),
"str": SqlType.text(),
"object": SqlType.text(),
"O": SqlType.text(),
"int64": SqlType.big_int(),
"float64": SqlType.double(),
"bool": SqlType.bool(),
"datetime64[ns]": SqlType.timestamp(),
"timedelta[ns]": SqlType.interval(),
"category": SqlType.text(),
}
def read_hyper(path_to_hyper_file, custom_schema="Extract"):
"""Read a Tableau Hyper file and turn it into a Pandas DataFrame.
def fn_convert_to_hyper_types(given_type):
switcher = {
'empty': SqlType.text(),
'bool': SqlType.bool(),
'int': SqlType.big_int(),
'float-dot': SqlType.double(),
'date-YMD': SqlType.date(),
'date-MDY': SqlType.date(),
'date-DMY': SqlType.date(),
'time-24': SqlType.time(),
'time-12': SqlType.time(),
'datetime-24-YMD': SqlType.timestamp(),
'datetime-12-MDY': SqlType.timestamp(),
'datetime-24-DMY': SqlType.timestamp(),
'str': SqlType.text()
}
identified_type = switcher.get(given_type)
if identified_type is None:
identified_type = SqlType.text()
return identified_type
def insert_box_events():
# Hyper file instantiation
path_to_database = Path(box_hyper_file)
hyper_file_exists = Path.exists(path_to_database)
# Start the Hyper API pricess
with HyperProcess(telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
# Check if the Hyper file exists or not. CreateMode.NONE will append. CreateMode.CREATE_AND_REPLACE will create a net new file
create_mode = None
if hyper_file_exists:
create_mode = CreateMode.NONE
else:
create_mode = CreateMode.CREATE_AND_REPLACE
# Open a new connection
with Connection(endpoint=hyper.endpoint,
database=path_to_database,
create_mode=create_mode) as connection:
# Check a new schema if it does not exist
connection.catalog.create_schema_if_not_exists(schema=box_schema)
# Instantiate the table schema
box_events_table_def = TableDefinition(
table_name=TableName(box_schema, box_events_table),
columns=[
TableDefinition.Column(name='event_id',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='event_type',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='created_at',
type=SqlType.timestamp_tz(),
nullability=NULLABLE),
TableDefinition.Column(name='created_by_id',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='created_by_name',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='created_by_login',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='source',
type=SqlType.json(),
nullability=NULLABLE),
TableDefinition.Column(name='ip_address',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='additional_details',
type=SqlType.json(),
nullability=NULLABLE)
])
print('Found schema: {0} and table def: {1}'.format(
box_events_table_def.table_name.schema_name,
box_events_table_def.table_name))
# Create the table if it does not exist and get the Box events table
connection.catalog.create_table_if_not_exists(
table_definition=box_events_table_def)
table_name = TableName(box_schema, box_events_table)
# Get the MAX row by created_at
last_event_created_at = connection.execute_scalar_query(
query=
f"SELECT MAX(created_at) FROM {box_events_table_def.table_name}"
)
if last_event_created_at is not None:
print('Found last event in hyper file: {0}'.format(
last_event_created_at.to_datetime()))
# Get the Box service account client
auth = JWTAuth.from_settings_file(box_config)
box_client = Client(auth)
service_account = box_client.user().get()
print(
'Found Service Account with name: {0}, id: {1}, and login: {2}'
.format(service_account.name, service_account.id,
service_account.login))
# Get the current date and the date for one month ago if there is not lastest event
today = datetime.utcnow()
if last_event_created_at is None:
last_event_created_at = today - relativedelta.relativedelta(
months=month_lookback)
else:
last_event_created_at = last_event_created_at.to_datetime(
).replace(tzinfo=timezone.utc).astimezone(tz=None)
# Get the Box enterprise events for a given date range
print(
'Using date range for events today: {0} and starting datetime: {1}'
.format(today, last_event_created_at))
get_box_events(box_client, 0, last_event_created_at, today)
# Insert the Box enteprise events into the Hyper file
with Inserter(connection, box_events_table_def) as inserter:
inserter.add_rows(rows=box_events)
inserter.execute()
# Number of rows in the "Box"."Events" table.
row_count = connection.execute_scalar_query(
query=f"SELECT COUNT(*) FROM {table_name}")
print(f"The number of rows in table {table_name} is {row_count}.")
print("The connection to the Hyper file has been closed.")
print("The Hyper process has been shut down.")
query_res['column_ex'] = query_res['column_ex'].astype(str)
query_res['column_ex2'] = query_res['column_ex2'].astype(str)
query_res['column_ex3'] = query_res['column_ex3'].astype(str)
query_res['column_ex4'] = query_res['column_ex4'].astype(str)
query_res['column_ex5'] = query_res['column_ex5'].astype(str)
query_res['column_ex6'] = query_res['column_ex6'].astype(str)
with HyperProcess(telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
with Connection(endpoint=hyper.endpoint,
database=path_to_database,
create_mode=CreateMode.CREATE_AND_REPLACE) as connection:
connection.catalog.create_schema('Extract')
# Define the hyper table (Same definition as the full refresh)
example_table = TableDefinition(TableName('Extract', 'Extract'), [
TableDefinition.Column('column_ex', SqlType.varchar(500)),
TableDefinition.Column('column_ex2', SqlType.varchar(500)),
TableDefinition.Column('column_ex_int', SqlType.int()),
TableDefinition.Column('column_ex3', SqlType.varchar(500)),
TableDefinition.Column('column_ex_int2', SqlType.int()),
TableDefinition.Column('column_ex4', SqlType.varchar(500)),
TableDefinition.Column('column_ex5', SqlType.varchar(500)),
TableDefinition.Column('column_ex_int3', SqlType.int()),
TableDefinition.Column('column_ex6', SqlType.varchar(500)),
])
print("The table is defined.")
connection.catalog.create_table(table_definition=example_table)
# Insert data from dataframe to hyper table
with Inserter(connection, example_table) as inserter:
for i in range(len(query_res)):
inserter.add_row(
def Full_refresh(result):
LogFileWrite("Running Full refresh")
try:
with HyperProcess(telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyperprocess:
print("The HyperProcess has started.")
LogFileWrite("The HyperProcess has started.")
print(hyperprocess.is_open)
if hyperprocess.is_open==True:
with Connection(hyperprocess.endpoint, 'Facebook_campaigns.hyper', CreateMode.CREATE_AND_REPLACE,) as connection:
if connection.is_open==True:
print("The connection to the Hyper file is open.")
LogFileWrite("The connection to the Hyper file is open.")
connection.catalog.create_schema('Extract')
DataTable = TableDefinition(TableName('Extract','Campaign_data'),[
############Below Columns are extracted from Report data API
TableDefinition.Column('Row_ID', SqlType.big_int()),
TableDefinition.Column('Inserted Date', SqlType.date()),
TableDefinition.Column('Date', SqlType.date()),
TableDefinition.Column('Account Id', SqlType.varchar(50)),
TableDefinition.Column('Account Name', SqlType.text()),
TableDefinition.Column('Campaign Id', SqlType.varchar(50)),
TableDefinition.Column('Campaign Name', SqlType.text()),
TableDefinition.Column('Impressions', SqlType.big_int()),
TableDefinition.Column('Clicks', SqlType.big_int()),
TableDefinition.Column('Reach', SqlType.big_int()),
TableDefinition.Column('Spend', SqlType.double()),
TableDefinition.Column('Frequency', SqlType.double()),
])
print("The table is defined.")
LogFileWrite("Successfully Facebook Campaign Table is defined")
connection.catalog.create_table(DataTable)
# print(Campaign_df["Id"].dtype)
#print(range(len(Campaign_df["Id"])))
with Inserter(connection, TableName('Extract','Campaign_data')) as inserter:
inserted_rows=1
row_id=1
for i in range(0,len(result["Campaign Id"])):
#print(str(result.loc[i,"CampaignId"]))
#print(result.loc[i,"Date"])
inserter.add_row([
int(row_id),
datetime.today(),
(datetime.strptime(result.loc[i,"Date"], '%Y-%m-%d')),
#(datetime.date(result.loc[i,"Date"])),#, "%Y-%m-%d")),
str(result.loc[i,"Account Id"]),
str(result.loc[i,"Account Name"]),
str(result.loc[i,"Campaign Id"]),
str(result.loc[i,"Campaign Name"]),
int(result.loc[i,"Impressions"]),
int(result.loc[i,"Clicks"]),
int(result.loc[i,"Reach"]),
float(result.loc[i,"Spend"]),
float(result.loc[i,"Frequency"])
])
#print("instered")
row_id=row_id+1
inserted_rows=inserted_rows+1
inserter.execute()
print("Instered Rows are " +str(inserted_rows))
LogFileWrite("Instered Rows are " +str(inserted_rows))
table_name=TableName('Extract','Campaign_data')
Delet_query=f"DELETE FROM {table_name} WHERE " +'"'+ 'Row_ID'+'"'+" NOT IN("
Delet_query+="SELECT MAX("+'"'+'Row_ID'+'"'+f") FROM {table_name} "
Delet_query+="GROUP BY " +'"'+'Date'+'",'+'"'+'Campaign Id'+'",'+'"'+'Campaign Name'+'",'
Delet_query+='"'+'Account Id'+'",'+'"'+'Impressions'+'",'
Delet_query+='"'+'Clicks'+'",'+'"'+'Account Name'+'",'+'"'+'Reach'+'",'+'"'+'Spend'+'",'
Delet_query+='"'+'Frequency'+'")'
#print(Delet_query)
connection.execute_command(Delet_query)
print("Deleted Duplicate rows")
LogFileWrite("Successfully deleted Duplicate rows")
else:
print("unable to open connection to hyper file")
LogFileWrite("unable to open connection to hyper file")
if connection.is_open==True:
connection.close()
print("Connection to Hyper file closed")
LogFileWrite("Connection to Hyper file closed")
else:
print("Connection to Hyper file closed")
LogFileWrite("Connection to Hyper file closed")
print("Connection is open or closed" + str(connection.is_open))
else:
print("Unable to start the Hyper process ")
LogFileWrite("Unable to start the Hyper process ")
if hyperprocess.is_open==True:
hyperprocess.close()
print("Forcefully shutted down the Hyper Process")
LogFileWrite("Forcefully shutted down the Hyper Process")
else:
print("Hyper process is shutted down")
LogFileWrite("Hyper process is shutted down")
print("Connection is open or closed" + str(connection.is_open))
print("process is open or closed" + str(hyperprocess.is_open))
except HyperException as ex:
LogFileWrite("There is exception in starting Tableau Hyper Process. Exiting...")
LogFileWrite(str(ex))
connection.close()
hyperprocess.close()
SendEmailMessage()
sys.exit()
def __init__(self):
self.location = "./output/meli_challenge_result.hyper"
self.test_location = "../output/meli_challenge_result.hyper"
self.searchResult_table = TableDefinition('results', [
TableDefinition.Column('id', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('site_id', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('title', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('seller', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('price', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('prices', SqlType.json(), Nullability.NOT_NULLABLE),
TableDefinition.Column('sale_price', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('currency_id', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('available_quantity', SqlType.int(), Nullability.NULLABLE),
TableDefinition.Column('sold_quantity', SqlType.int(), Nullability.NULLABLE),
TableDefinition.Column('buying_mode', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('listing_type_id', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('stop_time', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('condition', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('permalink', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('thumbnail', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('accepts_mercadopago', SqlType.bool(), Nullability.NULLABLE),
TableDefinition.Column('installments', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('address', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('shipping', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('seller_address', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('attributes', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('original_price', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('category_id', SqlType.text(), Nullability.NOT_NULLABLE),
TableDefinition.Column('official_store_id', SqlType.int(), Nullability.NULLABLE),
TableDefinition.Column('domain_id', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('catalog_product_id', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('tags', SqlType.text(), Nullability.NULLABLE),
TableDefinition.Column('catalog_listing', SqlType.bool(), Nullability.NULLABLE),
TableDefinition.Column('order_backend', SqlType.int(), Nullability.NULLABLE),
])
from tableauhyperapi import HyperProcess, Telemetry, \
Connection, CreateMode, \
NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \
Inserter, \
escape_name, escape_string_literal, \
TableName, Name, \
HyperException
# The table is called "Extract" and will be created in the "Extract" schema.
# This has historically been the default table name and schema for extracts created by Tableau
extract_table = TableDefinition(
table_name=TableName("Extract", "Extract"),
columns=[
TableDefinition.Column(name='Name',
type=SqlType.text(),
nullability=NOT_NULLABLE),
TableDefinition.Column(name='Location',
type=SqlType.geography(),
nullability=NOT_NULLABLE)
])
def run_insert_spatial_data_to_a_hyper_file():
"""
An example of how to add spatial data to a Hyper file.
"""
print("EXAMPLE - Add spatial data to a Hyper file ")
path_to_database = Path("spatial_data.hyper")
# Starts the Hyper Process with telemetry enabled to send data to Tableau.
import csv
import tableauserverclient as TSC
import yaml
from pathlib import Path
from tableauhyperapi import HyperProcess, Telemetry, \
Connection, CreateMode, \
NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \
Inserter, \
escape_name, escape_string_literal, \
HyperException
#Create table definition to be used in Hyper file
workbook_table = TableDefinition(
table_name="Workbooks",
columns=[
TableDefinition.Column("Project Id", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Content URL", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Created At", SqlType.date(), NOT_NULLABLE),
TableDefinition.Column("Id", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Project Name", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("size", SqlType.big_int(), NOT_NULLABLE),
TableDefinition.Column("Updated At", SqlType.date(), NOT_NULLABLE),
TableDefinition.Column("Name", SqlType.text(), NOT_NULLABLE)
])
#Name of hyper file that will be generated locally and deployed to Tableau
path_to_database = Path("workbooks.hyper")
#Name of project where Hyper file will be deployed
#This project must exist on your server
PROJECT = 'HyperTest'
def run(self, args):
""" Runs the command
:param args: Arguments from argparse.Namespace
"""
input_file = Path(args.input_file)
output_file = Path(args.output_file)
if args.mode == AdjustVertexOrderMode.INVERT:
print("Reversing vertex order of polygons in spatial columns")
else:
print(
"Adjusting vertex order of polygons assuming data source with "
"flat - earth topology in spatial columns")
# Starts the Hyper Process with telemetry enabled to send data to Tableau.
# To opt out, simply set telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU.
with HyperProcess(
telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
with Connection(endpoint=hyper.endpoint) as connection:
catalog = connection.catalog
# Create output database file, fails if the file already exists
catalog.create_database(output_file)
# Attach input and output database files
catalog.attach_database(input_file, "input")
catalog.attach_database(output_file, "output")
# Iterate over all schemas in the input file
for schema_name in catalog.get_schema_names("input"):
# Create the schema in the output file
catalog.create_schema_if_not_exists(
SchemaName("output", schema_name.name))
# Iterate over all tables in the input schema
for in_table in catalog.get_table_names(schema_name):
table_definition = catalog.get_table_definition(
name=in_table)
columns = table_definition.columns
out_table = TableName("output", schema_name.name,
in_table.name)
out_table_definition = TableDefinition(
out_table, columns)
# Create the table in the output file with the same table definition.
# Note that any constraints on the table in the input file
# will not be present in the table in the output file
catalog.create_table(out_table_definition)
spatial_columns = [
c.name for c in columns
if c.type == SqlType.geography()
]
in_column_modifiers = [
f"geo_{args.mode.value}_vertex_order({c.name})"
if c.type == SqlType.geography() else f"{c.name}"
for c in columns
]
if len(spatial_columns) > 0:
print(
f"Copying table {in_table} with {len(spatial_columns)} "
f"spatial columns: {spatial_columns}...")
else:
print(
f"Copying table {in_table} with no spatial columns..."
)
row_count = connection.execute_command(
f"INSERT INTO {out_table} SELECT {','.join(in_column_modifiers)} FROM {in_table}"
)
print(f" {row_count} rows copied")
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 21:35:47 2020
@author: Manish Chauhan || +91-9774083186
"""
from tableauhyperapi import HyperProcess, Inserter, Telemetry, Connection, CreateMode, NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, escape_name, escape_string_literal, HyperException, TableName
# Starting local hyper instance
extract_table = TableDefinition(
table_name=TableName('extract', 'extract'),
columns=[
TableDefinition.Column("Sno", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Name", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Sale", SqlType.big_int()),
TableDefinition.Column("Profit", SqlType.big_int()),
#TableDefinition.Column("Date", SqlType.date())
])
data_to_insert = [["1", "Vishakha", 99999, 80]]
def run_create_hyper_file_from_csv():
print("Inside Fucntion to pick data from CSV into table in new Hyper file")
with HyperProcess(telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
# Replaces file with CreateMode.CREATE_AND_REPLACE if it already exists
with Connection(
endpoint=hyper.endpoint,
x.append(workbook.name)
y.append(workbook.owner_id)
for users in y:
z.append(server.users.get_by_id(users))
with open('workbook_info.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerows(zip(x, y, z))
# HYPER API TO CREATE HYPER FILE
Workbook_Info_table = TableDefinition(
table_name="Workbook_Info",
columns=[
TableDefinition.Column("Workbook Name", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Workbook LUID", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Owner Info", SqlType.text(), NOT_NULLABLE)
]
)
def run_create_hyper_file_from_csv():
path_to_database = Path("Workbook_Info.hyper")
process_parameters = {
"log_file_max_count": "2",
"log_file_size_limit": "100M"
}
with HyperProcess(telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU, parameters=process_parameters) as hyper:
"""
# Change these to match your use case.
hyper_file_path = "hyper_for_csv.hyper"
table_name = TableName("Extract", "Extract")
output_name = "output.csv"
path_to_database = Path(hyper_file_path)
# The table is called "Extract" and will be created in the "Extract" schema
# and contains four columns.
extract_table = TableDefinition(
table_name=table_name,
columns=[
TableDefinition.Column(name='Customer ID',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='Customer Name',
type=SqlType.text(),
nullability=NULLABLE),
TableDefinition.Column(name='Loyalty Reward Points',
type=SqlType.big_int(),
nullability=NOT_NULLABLE),
TableDefinition.Column(name='Segment',
type=SqlType.text(),
nullability=NULLABLE)
])
def insert_data():
"""
# Step 1: Start Hyper instance
with HyperProcess(Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
#step 2: Start hyper process
with Connection(endpoint=hyper.endpoint,
create_mode=CreateMode.CREATE_AND_REPLACE,
database=PATH_TO_HYPER) as connection:
print(
connection.execute_scalar_query(
query="SELECT'securing connections with endpoint!'"))
#create table def with Table name
empdetails = TableDefinition(
table_name=TableName('Extract', 'empdetails'),
columns=[
TableDefinition.Column('EMP_ID', SqlType.int()),
TableDefinition.Column('EMP_NAME', SqlType.text()),
TableDefinition.Column('MONTHY_SALARY', SqlType.int()),
TableDefinition.Column('UNI_FLAG', SqlType.int())
])
#Create the table in the connection catalog
connection.catalog.create_schema('Extract')
connection.catalog.create_table(empdetails)
print('Validating the data...')
cmd = f"COPY{empdetails.table_name} from {escape_string_literal(PATH_TO_CSV)}\
with " "(format csv , NULL 'NULL', delimiter ',' , HEADER)"
table_count = connection.execute_command(cmd)
total_time = int((time.time() - start))
total_time = "{:.2f}".format(total_time)
def run_insert_spatial_data_to_a_hyper_file():
"""
An example of how to add spatial data to a Hyper file.
"""
print("EXAMPLE - Add spatial data to a Hyper file ")
path_to_database = Path("spatial_data.hyper")
# Starts the Hyper Process with telemetry enabled to send data to Tableau.
# To opt out, simply set telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU.
with HyperProcess(telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
# Creates new Hyper file "spatial_data.hyper".
# Replaces file with CreateMode.CREATE_AND_REPLACE if it already exists.
with Connection(
endpoint=hyper.endpoint,
database=path_to_database,
create_mode=CreateMode.CREATE_AND_REPLACE) as connection:
connection.catalog.create_schema(
schema=extract_table.table_name.schema_name)
connection.catalog.create_table(table_definition=extract_table)
# Hyper API's Inserter allows users to transform data during insertion.
# To make use of data transformation during insertion, the inserter requires the following inputs
# 1. The connection to the Hyper instance containing the table.
# 2. The table name or table defintion into which data is inserted.
# 3. List of Inserter.ColumnMapping.
# This list informs the inserter how each column in the target table is tranformed.
# The list must contain all the columns into which data is inserted.
# "Inserter.ColumnMapping" maps a valid SQL expression (if any) to a column in the target table.
# For example Inserter.ColumnMapping('target_column_name', f'{escape_name("colA")}*{escape_name("colB")}')
# The column "target_column" contains the product of "colA" and "colB" after successful insertion.
# SQL expression string is optional in Inserter.ColumnMapping.
# For a column without any transformation only the column name is required.
# For example Inserter.ColumnMapping('no_data_transformation_column')
# 4. The Column Definition of all input values provided to the Inserter
# Inserter definition contains the column definition for the values that are inserted
# The data input has two text values Name and Location_as_text
inserter_definition = [
TableDefinition.Column(name='Name',
type=SqlType.text(),
nullability=NOT_NULLABLE),
TableDefinition.Column(name='Location_as_text',
type=SqlType.text(),
nullability=NOT_NULLABLE)
]
# Column 'Name' is inserted into "Extract"."Extract" as-is.
# Column 'Location' in "Extract"."Extract" of geography type is computed from Column 'Location_as_text' of text type
# using the expression 'CAST("Location_as_text") AS GEOGRAPHY'.
# Inserter.ColumnMapping is used for mapping the CAST expression to Column 'Location'.
column_mappings = [
'Name',
Inserter.ColumnMapping(
'Location',
f'CAST({escape_name("Location_as_text")} AS GEOGRAPHY)')
]
# Data to be inserted.
data_to_insert = [['Seattle', "point(-122.338083 47.647528)"],
['Munich', "point(11.584329 48.139257)"]]
# Insert data into "Extract"."Extract" table with CAST expression.
with Inserter(connection,
extract_table,
column_mappings,
inserter_definition=inserter_definition) as inserter:
inserter.add_rows(rows=data_to_insert)
inserter.execute()
print("The data was added to the table.")
print("The connection to the Hyper file has been closed.")
print("The Hyper process has been shut down.")
# -----------------------------------------------------------------------------
from pathlib import Path
from tableauhyperapi import HyperProcess, Telemetry, \
Connection, CreateMode, \
NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \
Inserter, \
escape_name, escape_string_literal, \
HyperException
customer_table = TableDefinition(
# Since the table name is not prefixed with an explicit schema name, the table will reside in the default "public" namespace.
table_name="test02",
columns=[
TableDefinition.Column("ID", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Mathematics", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Science", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("English", SqlType.big_int(), NOT_NULLABLE),
TableDefinition.Column("Japanese", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("History", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Geography", SqlType.text(), NOT_NULLABLE),
]
)
def run_create_hyper_file_from_csv():
"""
An example demonstrating loading data from a csv into a new Hyper file
"""
print("EXAMPLE - Load data from CSV into table in new Hyper file")
with HyperProcess(
telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyper:
# Creates a new Hyper file or
# replaces the file if it already exists.
with Connection(endpoint=hyper.endpoint,
database="drinks.hyper",
create_mode=CreateMode.CREATE_AND_REPLACE) as connection:
# default schema name is "public"
# define table
drinks_table = TableDefinition(
table_name="drinks",
columns=[
TableDefinition.Column("country", SqlType.text(),
NOT_NULLABLE),
TableDefinition.Column("beer_servings", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("spirit_servings", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("wine_servings", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("total_litres_of_pure_alcohol",
SqlType.double(), NOT_NULLABLE),
TableDefinition.Column("continent", SqlType.text(),
NOT_NULLABLE)
])
# create tables
connection.catalog.create_table(drinks_table)
#
# -----------------------------------------------------------------------------
from pathlib import Path
from tableauhyperapi import HyperProcess, Telemetry, \
Connection, CreateMode, \
NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \
Inserter, \
escape_name, escape_string_literal, \
HyperException
customer_table = TableDefinition(
# Since the table name is not prefixed with an explicit schema name, the table will reside in the default "public" namespace.
table_name="Customer",
columns=[
TableDefinition.Column("Customer ID", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Customer Name", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("Loyalty Reward Points", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("Segment", SqlType.text(), NOT_NULLABLE)
])
def run_create_hyper_file_from_csv():
"""
An example demonstrating loading data from a csv into a new Hyper file
"""
print("EXAMPLE - Load data from CSV into table in new Hyper file")
path_to_database = Path("customer.hyper")
def _hyper_sql_type(self, source_column):
"""
Finds the correct Hyper column type for source_column
source_column (obj): Source column (Instance of google.cloud.bigquery.schema.SchemaField)
Returns a tableauhyperapi.SqlType Object
"""
source_column_type = source_column.field_type
return_sql_type = {
"BOOL": SqlType.bool(),
"BYTES": SqlType.bytes(),
"DATE": SqlType.date(),
"DATETIME": SqlType.timestamp(),
"INT64": SqlType.big_int(),
"INTEGER": SqlType.int(),
"NUMERIC": SqlType.numeric(18, 9),
"FLOAT64": SqlType.double(),
"STRING": SqlType.text(),
"TIME": SqlType.time(),
"TIMESTAMP": SqlType.timestamp_tz(),
}.get(source_column_type)
if return_sql_type is None:
error_message = "No Hyper SqlType defined for BigQuery source type: {}".format(
source_column_type
)
logger.error(error_message)
raise LookupError(error_message)
logger.debug(
"Translated source column type {} to Hyper SqlType {}".format(
source_column_type, return_sql_type
)
)
return return_sql_type
import sys
from pathlib import Path
from tableauhyperapi import HyperProcess, Telemetry, \
Connection, CreateMode, \
NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \
Inserter, \
escape_name, escape_string_literal, \
HyperException
product_table = TableDefinition(
# Since the table name is not prefixed with an explicit schema name, the table will reside in the default "public" namespace.
table_name="Products",
columns=[
TableDefinition.Column("category", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("title", SqlType.text(), NOT_NULLABLE),
TableDefinition.Column("price", SqlType.double(), NOT_NULLABLE),
TableDefinition.Column("available_quantity", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("sold_quantity", SqlType.big_int(),
NOT_NULLABLE),
TableDefinition.Column("permalink", SqlType.text(), NOT_NULLABLE)
])
def call_mlapi_to_dict(SearchCategory):
print("Start call_mlapi_to_dict.")
try: