def task_with_log_datasets():
log_dataset_op(
"location://path/to/value.csv",
"read", # Check passing str values too
success=False,
with_schema=False,
)
def task_with_log_datasets():
log_dataset_op(
"location://path/to/value.csv",
DbndDatasetOperationType.read,
row_count=987,
column_count=4,
)
def flush_operations(self, connection: PostgresConnectionWrapper):
if connection in self.connections:
for op in self.connections.get_operations(connection):
if self.conf.with_schema:
op.extract_schema(connection)
if self.conf.with_stats:
op.extract_stats(connection)
if self.conf.with_preview:
op.extract_preview(connection)
log_dataset_op(
op_path=op.render_connection_path(connection),
op_type=op.op_type,
success=op.success,
data=op,
error=op.error,
with_preview=self.conf.with_preview,
send_metrics=self.conf.send_metrics,
with_schema=self.conf.with_schema,
with_partition=self.conf.with_partition,
with_stats=self.conf.with_stats,
with_histograms=self.conf.with_histograms,
)
# we clean all the batch of operations we reported so we don't report twice
self.connections.clear_operations(connection)
def report_operations(self, connection: SnowflakeConnection,
operations: List[SqlOperation]):
if connection.is_closed():
# already closed, cannot proceed (and probably already tracked)
return
# update the tables names
operations = [op.evolve_table_name(connection) for op in operations]
# looks for tables schemas
tables = chain.from_iterable(op.tables for op in operations
if not op.is_file)
tables_schemas: Dict[str, DTypes] = {}
for table in tables:
table_schema = get_snowflake_table_schema(connection, table)
if table_schema:
tables_schemas[table] = table_schema
operations: List[SqlOperation] = [
op.evolve_schema(tables_schemas) for op in operations
]
for op in operations:
log_dataset_op(
op_path=render_connection_path(connection, op, "snowflake"),
op_type=op.op_type,
success=op.success,
data=op,
with_schema=True,
send_metrics=True,
)
def task_with_log_datasets():
log_dataset_op(
"/path/to/value.csv",
DbndDatasetOperationType.read,
data=pandas_data_frame,
with_preview=True,
with_schema=True,
)
def get_data(source: str, days: int):
today = pd.Timestamp.now().today()
start_date = today - pd.Timedelta(days=days)
name = (
f"test_data_{today.strftime('%Y-%m-%d')}_{start_date.strftime('%Y-%m-%d')}.csv"
)
file_path = os.path.join(source, name)
records_amounts = int((days * 24 * 10) * uniform(0.5, 1.5))
df = pd.DataFrame(np.random.randint(0, 100, size=(records_amounts, 4)),
columns=list("ABCD"))
df["dates"] = [random_date(start_date, today) for _ in range(len(df))]
log_dataset_op(
op_path=file_path,
op_type=DbndDatasetOperationType.read,
data=df,
with_schema=True,
with_preview=True,
with_histograms=True,
)
return df
def task_with_log_datasets():
log_dataset_op(
"location://path/to/value.csv",
DbndDatasetOperationType.read,
with_schema=False,
)
def task_with_log_datasets():
a_target = target("/path/to/value.csv")
log_dataset_op(a_target,
DbndDatasetOperationType.read,
with_schema=False)
