Error: `table_type` missing from table parameters when loading table from Hive metastore

[edgarrmondragon]

Apache Iceberg version

main (development)

Please describe the bug 🐞

I'm (a user of tap-iceberg is) running into the following error when trying to load a Hive table using pyiceberg.

pyiceberg.exceptions.NoSuchPropertyException: Property table_type missing, could not determine type: bronze.my_iceberg_table

The call in question is https://github.com/shaped-ai/tap-iceberg/blob/38064b3aaca5394ba1482970e790d3e2f6020946/tap_iceberg/tap.py#L94.

It seems the loaded table is missing the table type parameter in

iceberg-python/pyiceberg/catalog/hive.py

Lines 329 to 331 in d587e67

	def _get_hive_table(self, open_client: Client, database_name: str, table_name: str) -> HiveTable:
	try:
	return open_client.get_table(dbname=database_name, tbl_name=table_name)

?

Thanks in advance if this turns out to be user error 😃

[kevinjqliu]

In load_table, there's a 2 step process. First it fetches from HMS using get_table, then it converts the hive table into iceberg (_convert_hive_into_iceberg).

iceberg-python/pyiceberg/catalog/hive.py

Lines 524 to 527 in d587e67

	with self._client as open_client:
	hive_table = self._get_hive_table(open_client, database_name, table_name)
	return self._convert_hive_into_iceberg(hive_table)

The error here is the 2nd step. It is expected that the hive table has a property "table_type" and maps to the string "iceberg".

iceberg-python/pyiceberg/catalog/hive.py

Lines 274 to 285 in d587e67

	def _convert_hive_into_iceberg(self, table: HiveTable) -> Table:
	properties: Dict[str, str] = table.parameters
	if TABLE_TYPE not in properties:
	raise NoSuchPropertyException(
	f"Property table_type missing, could not determine type: {table.dbName}.{table.tableName}"
	)
	table_type = properties[TABLE_TYPE]
	if table_type.lower() != ICEBERG:
	raise NoSuchIcebergTableError(
	f"Property table_type is {table_type}, expected {ICEBERG}: {table.dbName}.{table.tableName}"
	)

Who created the table in this case? When PyIceberg creates the table, it injects the table_type property

iceberg-python/pyiceberg/catalog/hive.py

Line 373 in d587e67

tbl = self._convert_iceberg_into_hive(staged_table)

[edgarrmondragon]

Who created the table in this case? When PyIceberg creates the table, it injects the table_type property

I suppose it was created by a third-party and not by HiveCatalog.create_table. Are only tables created by pyiceberg supported here?

[kevinjqliu]

Are only tables created by pyiceberg supported here?

Anyone can create an iceberg table using HMS, which can be read by PyIceberg. In HMS, the assumption is that iceberg tables have a specific property set so that engines can distinguish between hive and iceberg tables.

In this case, the table was created as a "hive table" and not an "iceberg table".

[edgarrmondragon]

Anyone can create an iceberg table using HMS, which can be read by PyIceberg. In HMS, the assumption is that iceberg tables have a specific property set so that engines can distinguish between hive and iceberg tables.

In this case, the table was created as a "hive table" and not an "iceberg table".

@kevinjqliu thanks for the info 🙏. Just two more questions:

1. is there a way to set this property manually?
2. would doing that break something?

[kevinjqliu]

is there a way to set this property manually?

You can use an engine (like Spark/Trino) to interact with the Hive table to add the extra table parameter. Alternatively, a hacky way is to use hive client in pyiceberg. Like so

iceberg-python/pyiceberg/catalog/hive.py

Lines 570 to 574 in d587e67

	with self._client as open_client:
	tbl = open_client.get_table(dbname=from_database_name, tbl_name=from_table_name)
	tbl.dbName = to_database_name
	tbl.tableName = to_table_name
	open_client.alter_table(dbname=from_database_name, tbl_name=from_table_name, new_tbl=tbl)

it should work, but definitely test it out first

would doing that break something?

Nope, adding that specific parameter to HMS is how the iceberg table is defined.
You can see example in the core iceberg library and in an engine like Trino