Implementing Data Engineering Integration with Google Dataproc

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Troubleshooting Mapping Failures on the Dataproc Cluster

This section provides information on troubleshooting common error messages and limitations that you might encounter when you run mappings on a Dataproc cluster.

When you run mappings on a Dataproc cluster to read or write data to SerDe-backed Hive tables, the mapping fails with the java.lang.ClassNotFoundException: org.apache.hive.hcatalog.data.JsonSerDe error.: Add the following property in the
hive-site.xml
on all the nodes of the Dataproc cluster and restart the Hive server:
<property> <name>hive.aux.jars.path</name> <value>file:///usr/lib/hive/lib/hive-hcatalog-core-<version>.jar</value> </property>

When you run mappings on a non-VPN Dataproc cluster, the mappings fail.: Configure the following properties in the
hdfs-site.xml
on all the nodes of the Dataproc cluster:
<property> <name>dfs.namenode.rpc-bind-host</name> <value>0.0.0.0</value> </property> <property> <name>dfs.namenode.servicerpc-bind-host</name> <value>0.0.0.0</value> </property> <property> <name>dfs.namenode.http-bind-host</name> <value>0.0.0.0</value> </property> <property> <name>dfs.namenode.https-bind-host</name> <value>0.0.0.0</value> </property> <property> <name>dfs.client.use.datanode.hostname</name> <value>true</value> </property>
When I run a mapping with a Hive source or a Hive target on a different cluster, the Data Integration Service fails to push the mapping to Hadoop with the following error: Failed to execute query [exec0_query_6] with error code [10], error message [FAILED: Error in semantic analysis: Line 1:181 Table not found customer_eur], and SQL state [42000]].: When you run a mapping in a Hadoop environment, the Hive connection selected for the Hive source or Hive target, and the mapping must be on the same Hive metastore.
When I run a mapping with SQL overrides concurrently, the mapping hangs.: There are not enough available resources because the cluster is being shared across different engines.; Use different YARN scheduler queues for the Blaze and Spark engines to allow HiveServer2 to run SQL overrides through these engines.
When I configure a mapping to create a partitioned Hive table, the mapping fails with the error "Need to specify partition columns because the destination table is partitioned.": This issue happens because of internal Informatica requirements for a query that is designed to create a Hive partitioned table. For details and a workaround, see Knowledge Base article 516266.
Time stamp data that is precise to the nanosecond is truncated when a mapping runs on the Spark engine: Spark stores time stamp data to a precision of 1 microsecond (1us) and does not support nanosecond precision. When a mapping that runs on the Spark engine reads datetime data that has nanosecond precision, the data is truncated to the microsecond. For example,
2015-01-02 00:00:00.000456789
is truncated to
2015-01-02 00:00:00.000456
.; The Blaze engine supports nanosecond precision.

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Implementing Data Engineering Integration with Google Dataproc

Implementing Data Engineering Integration with Google Dataproc

Troubleshooting Mapping Failures on the Dataproc Cluster

Troubleshooting Mapping Failures on the Dataproc Cluster