Teradata Limitations | QueryGrid - Known Limitations (Teradata-to-TargetConnector) - Teradata QueryGrid

• mapreduce.map.speculative=false
• mapreduce.reduce.speculative=false
• hive.mapred.reduce.tasks.speculative.execution=false
• tez.am.speculation.enabled=false

• If the Hive table statistics are inaccurate (this is uncommon), enabling this property can result in a performance overhead on the insert query.
• If there are concurrent inserts on the Hive table, an inaccurate number of rows may be displayed, resulting in an approximate result rather than a precise number.

After data has been exported and committed to a remote system, any subsequent errors or aborts on the local system do not roll back the remote request.

• Spark 2.1 and later: When using the Spark initiator, if the schema of a target table changes after a non-native table representing that target table has been created, that non-native table must be recreated in order to reflect the schema change.
• Spark 2.2 and later: When importing data for the DATE type using the Spark target connector or exporting data of the DATE type using the Spark initiator, the data value from Spark can be incorrect.
• Spark 2.2 and later: Spark does not support Char/Varchar; therefore, when using the Spark target connector to insert data from QueryGrid and the target table contains char/varchar columns, the data from QueryGrid may be incorrect. To avoid possible incorrect data, use String instead of Char/Varchar.
• If Spark is upgraded or the location of standard Spark JARs are changed, a tdqg-node restart is required.

For example, when writing to BigQuery there is a maximum row size of 10MB and a maximum of 100 concurrent threads when not using a multi-region.

See the BigQuery Quotas and Limits documentation.

select col1 from project_name.dataset_name.table_name@fs_name

Use the following workaround instead:

select * from foreign table (select col1 from project_name.dataset_name.table_name)@fs_name ft;