Known issues in Iceberg

This topic describes the known issues for Iceberg in Cloudera Data Warehouse on cloud, version 2026.0.21.4-12

Known issues identified in the September 18, 2026 release

There are no new known issues identified in this release.

Known issues identified before the September 18, 2026 release

CDPD-100780: Materialized view rebuild fails when stored using the Iceberg storage format
Materialized view rebuild operations fail when the materialized view is stored using the Iceberg storage format. As a result, subsequent rebuild attempts fail because an existing entry remains in the MATERIALIZATION_REBUILD_LOCKS table, preventing new rebuild operations from proceeding.

This issue occurs due to a defect in Hive during materialized view rebuild, where the rebuild process fails but leaves behind a stale lock entry.

Drop and recreate the materialized view instead of the rebuild operation alter materialized view mat1; using the following command:
drop materialized view mat1;
create materialized view mat1 stored by iceberg stored as orc tblproperties ('format-version'='1') as
select tbl_ice.b, tbl_ice.c from tbl_ice where tbl_ice.c > 52;

Apache Jira: HIVE-29436

CDPD-101214: Semantic error in MERGE statements when using backticks with restricted keywords as column names
Hive fails to run MERGE statements with backticks by using restricted keywords as column names. The query displays a SemanticException during parsing, preventing successful execution of the MERGE operation.
Avoid using restricted keywords as column names, even when enclosed in backticks.
CDPD-101742: Hive Iceberg snapshot expiration fails for ORC LZ4/LZO tables
Hive fails to run queries on Iceberg tables stored in ORC format with LZ4 or LZO compression when LLAP cache is enabled. The failure results in an OWN_TASK_FAILURE status and vertex failures due to an UnsupportedOperationException during ORC decompression. The snapshot expiration functionality itself works as expected and is not impacted.
For Iceberg tables, disable vectorization by setting hive.vectorized.execution.enabled=false.