Internal Error with MERGE Command in Spark SQL

[Rafael-Sousa]

I'm trying to perform a MERGE between two tables (customers and customers_update) using Spark SQL, but I’m encountering an internal error during the planning phase. The error message suggests it might be a bug in Spark or one of the plugins in use.
Here’s the SQL code I’m running:

```

MERGE INTO customers AS c USING customers_update AS u ON c.customer_id = u.customer_id WHEN MATCHED AND c.email IS NULL AND u.email IS NOT NULL THEN UPDATE SET email = u.email WHEN NOT MATCHED THEN INSERT (customer_id, email, profile, updated) VALUES (u.customer_id, u.email, u.profile, u.updated);

```

And the error message:

```

[INTERNAL_ERROR] The Spark SQL phase planning failed with an internal error. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace. SQLSTATE: XX000

```

As a workaround, I modified the code to specify the the database and metastore for the table, which resolved the issue:

```

MERGE INTO hive_metastore.default.customers AS c
USING customers_update AS u
ON c.customer_id = u.customer_id
WHEN MATCHED AND c.email IS NULL AND u.email IS NOT NULL THEN
UPDATE SET email = u.email
WHEN NOT MATCHED THEN
INSERT (customer_id, email, profile, updated)
VALUES (u.customer_id, u.email, u.profile, u.updated);

```

I would like to comprehend the necessity of the aforementioned approach in resolving the issue and inquire whether any configuration could avert such errors in the future. Furthermore, is there an established solution for this bug since it remained unresolved following the recommendations from an AI assistant?

[Walter_C · Accepted solution]

The issue you encountered with the MERGE statement in Spark SQL, which was resolved by specifying the database and metastore, is likely related to how Spark handles table references during the planning phase. The internal error you faced suggests a bug in Spark or one of its plugins, which can sometimes be triggered by ambiguous or incomplete table references.

By explicitly specifying the database and metastore (hive_metastore.default.customers), you provided a fully qualified table name, which likely helped Spark's query planner to correctly identify and access the table, thus avoiding the internal error.

To prevent such errors in the future, you can consider the following configurations and best practices:

1. Fully Qualified Table Names: Always use fully qualified table names in your SQL queries to avoid ambiguity and ensure that Spark can correctly resolve the table references.

2. Configuration Settings: Ensure that your Spark configuration is optimized for your environment. For example, setting spark.sql.catalogImplementation to hive if you are using Hive metastore can help Spark to correctly interface with the metastore.

3. Databricks Runtime Updates: Keep your Databricks Runtime up to date. Newer versions often include bug fixes and performance improvements. For instance, the recent updates in Databricks Runtime 15.2 and above have extended merge capabilities and fixed several issues related to SQL operations.

[Rafael-Sousa]

Thank you very much.