Can’t SELECT anything in a AWS Glue Data Catalog view due to invalid view text:

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Summary

I encountered a critical failure when attempting to query an AWS Glue Data Catalog view from an EMR cluster using Spark SQL. The view, created by a Glue job as a protected multi-dialect view with SECURITY DEFINER, generated an AnalysisException with the error code [INVALID_VIEW_TEXT]. The error explicitly stated that Spark could not display the view due to the view text being invalid or potentially tampered with. Despite confirming the view definition in the Glue Data Catalog appeared syntactically correct, the Spark metastore client failed to resolve or translate the view definition, causing all read operations to fail. The issue persisted across multiple EMR versions and Spark configurations, indicating a fundamental incompatibility between the Glue-specific view attributes and the standard Spark SQL analyzer.

Root Cause

The root cause was identified as a type mismatch between AWS Glue’s proprietary PROTECTED MULTI DIALECT VIEW syntax and Apache Spark’s standard CREATE VIEW parser. While the Glue Data Catalog stores the view definition verbatim, Spark’s CatalogPlugin layer expects view definitions to be compatible with standard Hive or Spark SQL semantics.

Specific technical factors included:

  • Unsupported View Properties: The PROTECTED and MULTI DIALECT keywords are proprietary extensions added by Glue that are not recognized by the open-source Spark SQL parser. When Spark attempts to parse the view text retrieved from the Glue catalog, it hits a syntax error during the CreateViewCommand execution phase.
  • Security Definer Context: The SECURITY DEFINER clause, while valid in Glue, introduced a parsing ambiguity in older Spark versions (specifically those bundled with EMR 6.x prior to a certain patch level) that expected SECURITY to be followed by specific tokens not present in the Glue output.
  • FQDN Resolution: The view identifier spark_catalog.risk_models_output.vw_behavior_special_limit_score indicated the catalog being used was spark_catalog (the default Spark in-memory catalog), not the actual Glue Data Catalog. This caused a mismatch where the metadata fetch used the Glue Catalog, but the view parsing logic defaulted to Spark’s internal analyzer which lacked the Glue-specific dialect definitions.

Why This Happens in Real Systems

This issue arises frequently in hybrid environments where managed cloud services (Glue) outpace the version compatibility of compute engines (EMR/Spark).

Key contributing factors in real-world systems include:

  • Proprietary Metadata Extensions: Cloud providers often add metadata attributes to standard DDL to enable features like column-level lineage, fine-grained access control, or cross-dialect compatibility. These extensions are invisible to standard SQL parsers until the underlying engine is updated to support them.
  • Catalog Abstraction Leaks: Spark’s pluggable catalog architecture allows integration with external metastores (like Glue), but the translation layer between the external metastore’s data format and Spark’s internal Table definition is often buggy or incomplete. When the translation fails, the fallback behavior is often a generic AnalysisException.
  • Asynchronous Version Drift: A Glue job running on a recent runtime version may create views using syntax that an EMR cluster—pinned to a specific stable release for production compliance—cannot yet parse. This creates a silent failure mode where the resource exists but is unusable by the compute engine.

Real-World Impact

The impact of this error is severe because it breaks the fundamental contract of a Data Catalog: retrievability.

  • Blocked Data Access: Data consumers (Data Scientists, Analysts) are completely blocked from reading the dataset, halting downstream workflows and reporting.
  • Pipeline Failures: ETL jobs that depend on this view will fail silently or with cryptic errors, causing data freshness delays.
  • Operational Overhead: Engineers must spend time diagnosing a failure that appears to be a syntax error (suggesting user error) but is actually an infrastructure compatibility issue. This erodes trust in the platform stability.
  • Security Misalignment: If the view was created to enforce row-level security via the PROTECTED keyword, the inability to query it effectively bypasses those security controls by preventing access entirely, which may not be the desired security posture.

Example or Code

To demonstrate the fix, we first reproduce the view creation with the problematic syntax, then show the corrected version compatible with standard Spark SQL running on EMR.

-- PROBLEMATIC: Glue-specific syntax that causes Spark to fail on EMR
-- This syntax is supported by Glue but not parsed by standard Spark SQL
CREATE OR REPLACE PROTECTED MULTI DIALECT VIEW 
risk_models_output.vw_behavior_special_limit_score 
SECURITY DEFINER AS 
SELECT 
    customer_id, 
    behavior_score, 
    limit_amount 
FROM risk_models_output.raw_behavior_data;

-- FIXED: Standard Spark SQL compatible syntax
-- Removing proprietary keywords allows Spark to parse and execute the view
CREATE OR REPLACE VIEW 
risk_models_output.vw_behavior_special_limit_score AS 
SELECT 
    customer_id, 
    behavior_score, 
    limit_amount 
FROM risk_models_output.raw_behavior_data;

How Senior Engineers Fix It

Senior engineers approach this by validating the compatibility layer between the metastore and the compute engine, rather than assuming the view definition is correct simply because it is stored in the catalog.

  1. Verification of Catalog Identity: Ensure the Spark session is explicitly configured to use the Glue Data Catalog, not the default in-memory catalog. This is done by setting spark.sql.catalogImplementation to hive and providing the correct hive.metastore.uris (which points to the Glue metastore).
  2. Normalization of DDL: The most robust fix is to recreate the view using standard SQL syntax. If the proprietary features (like PROTECTED) are strictly required for Glue-specific governance, they must be stripped for the Spark definition and enforced via IAM policies or Lake Formation permissions instead.
  3. Version Compatibility Check: Check the EMR release label against the Glue runtime version used to create the view. If a mismatch exists, upgrade the EMR cluster or downgrade the Glue job runtime to align versions.
  4. Bypassing the Parser: As a temporary workaround, senior engineers might inject the view definition directly into the Spark Catalog using Scala code, bypassing the SQL parser, though this is risky and non-persistent.

Why Juniors Miss It

Junior engineers often struggle with this error because the symptoms mimic standard syntax errors, and the root cause lies in deep infrastructure abstractions.

  • Focus on Syntax, Not Semantics: Juniors see INVALID_VIEW_TEXT and immediately assume they made a typo in the SELECT statement. They scrutinize the query logic rather than the CREATE VIEW keywords preceding it.
  • Lack of Catalog Awareness: They often assume the “Glue Data Catalog” is just a database and overlook the translation layer required for Spark to interact with it. They may not know how to check if Spark is actually using the Glue metastore or the default Hive metastore.
  • Trust in Console: Viewing the definition in the AWS Console shows a perfectly formatted SQL query. It is difficult to conceptualize that the storage format (Glue) differs from the execution format (Spark), leading them to blame the EMR cluster configuration or permissions rather than the DDL itself.
  • Incomplete Error Interpretation: The error mentions “unauthorized modification” or “tampering,” leading juniors to check CloudTrail logs or IAM roles extensively, wasting time on what is essentially a parsing incompatibility.