Known Issues in Apache Iceberg

Learn about the known issues in Iceberg, the impact or changes to the functionality, and the workaround.

CDPD-75667: Querying an Iceberg table with a TIMESTAMP_LTZ column can result in data loss

When you query an Iceberg table that has a TIMESTAMP_LTZ column, the query could result in data loss.

When creating Iceberg tables from Spark, set the following Spark configuration to avoid creating columns with the TIMESTAMP_LTZ type:

spark.sql.timestampType=TIMESTAMP_NTZ

Apache JIRA: IMPALA-13484

CDPD-75088: Iceberg tables in azure cannot be partitioned by strings ending in '.'

In an Azure environment, you cannot create Iceberg tables from Spark that are partitioned by string columns having a partition value that contains the period (.) character. The query fails with the following error:

24/10/08 18:14:12 WARN  scheduler.TaskSetManager: [task-result-getter-2]: Lost task 0.0 in stage 2.0 (TID 2) (spark-sfvq0t-compute0.spark-r9.l2ov-m7vs.int.cldr.work executor 1): java.lang.IllegalArgumentException: ABFS does not allow files or directories to end with a dot.

None.

CDPD-72942: Unable to read Iceberg table from Hive after writing data through Apache Flink

If you create an Iceberg table with default values using Hive and insert data into the table through Apache Flink, you cannot then read the Iceberg table from Hive using the Beeline client, and the query fails with the following error:

Error while compiling statement: java.io.IOException: java.io.IOException: Cannot create an instance of InputFormat class org.apache.hadoop.mapred.FileInputFormat as specified in mapredWork!

The issue persists even after you use the ALTER TABLE statement to set the engine.hive.enabled table property to "true".

None.

Apache JIRA: HIVE-28525

CDPD-71962: Hive cannot write to a Spark Iceberg table bucketed by date column

If you have used Spark to create an Iceberg table that is bucketed by the "date" column and then try inserting or updating this Iceberg table using Hive, the query fails with the following error:

Error: Error while compiling statement: FAILED: RuntimeException org.apache.hadoop.hive.ql.exec.UDFArgumentException:  ICEBERG_BUCKET() only takes STRING/CHAR/VARCHAR/BINARY/INT/LONG/DECIMAL/FLOAT/DOUBLE types as first argument, got DATE (state=42000,code=40000)

This issue does not occur if the Iceberg table is created through Hive.

None.

CDPD-66305: Do not turn on the optimized Iceberg V2 operator

The optimized Iceberg V2 operator is disabled by default due to a correctness issue. The correct setting for the property that turns off the operator is DISABLE_OPTIMIZED_ICEBERG_V2_READ=true.

Accept the default setting of the V2 operator. Do not change the setting from true to false.

CDPD-64629: Performance degradation of Iceberg tables compared to Hive tables

Cloudera testing of Iceberg and Hive tables using the Hive TPC-DS 1 Tb dataset (Parquet) revealed a slower performance executing a few of the queries in TPCDS. Overall performance of Iceberg executing queries on Hive external tables of Iceberg is faster than Hive.

CDPD-57551: Performance issue can occur on reads after writes of Iceberg tables

Hive might generate too many small files, which causes performance degradation.

Maintain a relatively small number of data files under the iceberg table/partition directory to have efficient reads. To alleviate poor performance caused by too many small files, run the following queries:

TRUNCATE TABLE target;
INSERT OVERWRITE TABLE target select * from target FOR SYSTEM_VERSION AS OF <preTruncateSnapshotId>;