Unsupported features and limitations

Cloudera does not support all features in Apache Iceberg. The list of unsupported features for Cloudera Data Platform (CDP) differs from release to release. Also, Apache Iceberg in CDP has some limitations you need to understand.

Unsupported features

The following features are not supported in this release of CDP:
  • Tagging and branching

    A technical preview is supported from Hive (not Impala or Spark) in Cloudera Data Warehouse Public Cloud.

  • Writing equality deletes
  • Reading files outside the table directory

    An unauthorized party who knows the underlying schema and file location outside the table location can rewrite the manifest files within one table location to point to the data files in another table location to read your data.

  • Buckets defined from Hive do not create like buckets in Iceberg.

    For more information, see "Bucketing workaround" below.

  • Using Iceberg tables as Spark Structured Streaming sources or sinks
  • PyIceberg
  • Migration of Delta Lake tables to Iceberg


The following features have limitations or are not supported in this release:

  • When the underlying table is changed, you need to rebuild the materialized view manually, or use the Hive query scheduling to rebuild the materialized view.
  • From Impala, you can read, but not write, position updates and deletes.
  • Equality updates and deletes are not supported as previously mentioned.
  • An equality delete file in the table is the likely cause of a problem with updates or deletes in the following situations:
    • In Change Data Capture (CDC) applications
    • In upserts from Apache Flink
    • From a third-party engine
  • An Iceberg table that points to another Iceberg table in the HiveCatalog is not supported.
    For example:
    TBLPROPERTIES ('iceberg.table_identifier'='db.tb');
  • See also Iceberg data types limitations and unsupported data types.

Bucketing workaround

A query from Hive to define buckets/folders in Iceberg do not create the same number of buckets/folders as the same query creates in Hive. In Hive bucketing by multiple columns using the following clause creates 64 buckets maximum inside each partition.

| CLUSTERED BY (                                     |
|   id,                                              |
|   partition_id)                                    |

Defining bucketing from Hive on multiple columns of an Iceberg table using this query creates 64*64 buckets/folders; consequently, bucketing by group does not occur as expected. The operation will create many small files at scale, a drag on performance.

Add multiple bucket transforms (partitions) to more than one column in the current version of Iceberg as follows:

bucket(p, col1, col2) =[ bucket(m, col1) , bucket(n, col2) ] where p = m * n