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 _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
TableDefinition.Column('market_normalized_name', SqlType.varchar(100), NULLABLE),
TableDefinition.Column('brand_normalized_name', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('form_strength_normalized_name', SqlType.varchar(35), NULLABLE),
TableDefinition.Column('normalized_name', SqlType.varchar(35), NULLABLE),
TableDefinition.Column('competitor_flag', SqlType.bool(), NULLABLE),
TableDefinition.Column('year', SqlType.int(), NULLABLE),
TableDefinition.Column('semester_number', SqlType.int(), NULLABLE),
TableDefinition.Column('quarter_number', SqlType.int(), NULLABLE),
TableDefinition.Column('month_number', SqlType.int(), NULLABLE),
TableDefinition.Column('week_number', SqlType.int(), NULLABLE),
TableDefinition.Column('transaction_timestamp', SqlType.date(), NULLABLE),
TableDefinition.Column('ddd_source_units', SqlType.int(), NULLABLE),
TableDefinition.Column('ddd_source_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('ddd_units', SqlType.int(), NULLABLE),
TableDefinition.Column('ddd_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('ddd_dollars', SqlType.numeric(10,4), NULLABLE),
TableDefinition.Column('ddd_dot', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('ddd_mcg', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('ddd_normalized_units', SqlType.int(), NULLABLE),
TableDefinition.Column('ddd_normalized_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('chargeback_source_units', SqlType.int(), NULLABLE),
TableDefinition.Column('chargeback_source_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('chargeback_units', SqlType.int(), NULLABLE),
TableDefinition.Column('chargeback_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('chargeback_dollars', SqlType.numeric(10,4), NULLABLE),
TableDefinition.Column('chargeback_dot', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('chargeback_mcg', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('chargeback_normalized_units', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('chargeback_normalized_units_uom', SqlType.varchar(30), NULLABLE),
TableDefinition.Column('self_reported_source_units', SqlType.numeric(10,5), NULLABLE),
TableDefinition.Column('self_reported_source_units_uom', SqlType.varchar(30), NULLABLE),
def sparkConnect():
# fetching DF from spark filestore
if cf.file_type == 'csv':
df = spark.read.format(cf.file_type) \
.option("inferSchema", cf.infer_schema) \
.option("header", cf.first_row_is_header) \
.option("sep", cf.delimiter) \
.load(cf.input_file_path)
# print('\n', cf.input_file_path, '\n', cf.schema, '\n')
# fetching table from db from databricks
elif cf.file_type == 'jdbc':
df = spark.read.format("jdbc") \
.option("driver", cf.driver) \
.option("url", cf.url) \
.option("dbtable", cf.table) \
.option("user", cf.user) \
.option("password", cf.password) \
.option("inferSchema", cf.infer_schema) \
.option("header", cf.first_row_is_header) \
.load()
df.write.format("csv") \
.option("enoding", cf.charset) \
.option("header", cf.first_row_is_header) \
.option("sep", cf.delimiter) \
.save('/home/hari/HyperConverter/test')
# pdf = df.select('*').toPandas()
# path = '/home/hari/HyperConverter/test.csv'
# pdf.to_csv(path, sep=',', index=False)
path = glob.glob('/home/hari/HyperConverter/test/part*.csv')
cf.input_file_path = path[0]
cf.input_file_path = path
print('\n', cf.input_file_path, '\n')
col = list(df.dtypes)
print(col)
print(len(col))
for i in range(len(col)):
col[i] = list(col[i])
col[i][1] = type_[col[i][1]]
# print('\n', col, '\n')
x = []
for i, j in col:
print(i, j)
if j == 'varchar':
max_length = df.agg({i: "max"}).collect()[0]
#print(max_length)
xyz = max_length["max({})".format(i)]
if xyz != None:
max_length = len(xyz)
if 19 <= max_length <= 40:
max_length = 100
else:
max_length = 30
else:
max_length = 35
print(max_length)
x.append(
TableDefinition.Column(i, SqlType.varchar(max_length),
NULLABLE))
elif j == 'int':
x.append(TableDefinition.Column(i, SqlType.int(), NULLABLE))
elif j == 'date':
x.append(TableDefinition.Column(i, SqlType.date(), NULLABLE))
elif j == 'numeric':
x.append(
TableDefinition.Column(i, SqlType.numeric(10, 4), NULLABLE))
elif j == 'bool':
x.append(TableDefinition.Column(i, SqlType.bool(), NULLABLE))
elif j == 'big_int':
x.append(TableDefinition.Column(i, SqlType.big_int(), NULLABLE))
elif j == 'double':
x.append(TableDefinition.Column(i, SqlType.double(), NULLABLE))
elif j == 'text':
print("this is culprate", i, j)
x.append(TableDefinition.Column(i, SqlType.text(), NULLABLE))
print(x)
print(len(x))
return x