Consistently getting [ErrorCode: INTERNAL_ERROR_QUERY_ENGINE] after adding a new UNION to a view

Hello Ciciliati,

Amazon Athena does have some limitations on the complexity and size of queries that can be executed. While there is no explicit limit on the number of UNIONs you can use in a query, you may be hitting other limits or encountering query optimization issues. The "[ErrorCode: INTERNAL_ERROR_QUERY_ENGINE]" error is a generic error message that indicates an internal issue with Athena's query execution engine.

Here are a few possible reasons for the error you're encountering:

1. Query Complexity: The query complexity and resource requirements increase with the number of UNIONs, especially if the individual SELECT statements are complex. Athena may be struggling to optimize and execute the query due to its complexity.

2. Data Volume: Large data volumes can lead to resource exhaustion, resulting in this error. If your query processes a substantial amount of data, it may require more memory and CPU resources than are available for your query.

3. Query Optimization: Athena's query optimizer may have difficulty optimizing a complex query with multiple UNIONs. Query optimization is a complex process, and sometimes, certain query patterns can lead to suboptimal execution plans.

To resolve this issue, consider the following steps:

1. Optimize Query: Review your query and see if there are ways to simplify it or make it more efficient. Ensure that you have appropriate filters and predicates to reduce the data volume processed by the query.

2. Pagination: If your query processes a large amount of data, consider adding pagination to retrieve smaller chunks of data at a time rather than processing everything in a single query.

3. Data Partitioning: If your data is stored in a format like Parquet or ORC, consider partitioning your data to improve query performance. Partitioning can significantly reduce the amount of data scanned during queries.

4. Resource Allocation: Check the resource allocation for your Athena query. You can adjust the query execution settings to allocate more memory or increase the query timeout if necessary.

5. Query Simplification: If the UNIONs are causing the issue, try to simplify your query by using fewer UNIONs or reorganizing your data to reduce the need for UNION operations.

6. AWS Support: If the issue persists and you've ruled out query complexity and data volume as the primary causes, it may be worth reaching out to AWS Support for further assistance. They can investigate the specific error and provide guidance on how to optimize your query for successful execution.

AWS Athena is designed to handle a wide range of query workloads, but complex queries or large datasets may require additional optimization and resource allocation to ensure smooth execution.