Inconsistent behavior of LakeBridge transpiler for similar scripts

[Satyam_Patel]

Hi Everyone,

I am testing the LakeBridge prototype and noticed inconsistent behavior when converting stored procedures.

• For simple scripts, the conversion is correct.

• But for medium/complex scripts, especially those with multiple LEFT JOINs and column extractions, the transpiler sometimes produces unexpected results.

Example behavior I saw:

• The original SQL Server stored procedure was ~80 lines.

• After conversion, the output script became ~1000 lines, mostly filled with repeated SET … = (SELECT …) blocks instead of a single structured query.

• In one case, two scripts with a very similar pattern behaved differently — one converted correctly, while the other expanded into a huge UPDATE/SET style output.

My questions:

• Why does LakeBridge transpiler behave differently for scripts of the same pattern?

• Is there any known limitation or specific rule in LakeBridge that causes such variation?

• Is there a way to standardize/optimize the conversion so it doesn’t blow up into thousands of lines?

Thanks in advance for any guidance!

Note: This is just a similar example to illustrate the issue, not my original script.

[mark_ott]

Why does LakeBridge transpiler behave differently for scripts of the same pattern?

The LakeBridge transpiler in Databricks can behave inconsistently with medium or complex SQL Server stored procedures—especially those with many LEFT JOINs and column derivations—because it relies on heuristic pattern recognition rather than true semantic understanding of SQL logic. This means that seemingly similar patterns may be parsed differently due to nuances in table aliases, subqueries, join complexity, conditional logic, or even minor syntax variations.

The LakeBridge transpiler’s inconsistent behavior is due to its pattern-matching and fallback mechanisms, which can be triggered by seemingly minor differences in script structure or syntax. Careful refactoring or feedback to Databricks can help improve transpiler effectiveness.

[mark_ott]

Is there any known limitation or specific rule in LakeBridge that causes such variation?

Yes, there are specific known limitations and rules in LakeBridge (Databricks’ open-source migration tool) that can cause variation—especially when converting complex SQL code or stored procedures.

Key Limitations and Rules Affecting Variation

• LakeBridge uses a static, rule-based translation method. It relies on predefined pattern-matching configurations to convert SQL and ETL code, which works well for simple, standard patterns. However, for complex scripts—particularly those with nested queries, multiple JOINs, or unusual SQL logic—the output may vary significantly even for similar input scripts.

• The transpiler may expand scripts with multiple LEFT JOINs or complex structures into long sequences of row-by-row assignments (such as repeated SET … = (SELECT …) blocks). This is a fallback to ensure correctness when the original structure is too complicated for rule-based patterning, sometimes causing the converted code to become vastly longer and harder to read.

• Minor differences in source scripts (such as alias usage, join order, or placement of expressions) can trigger different internal representations, which leads the transpiler to handle seemingly similar logic in inconsistent ways.

• LakeBridge currently offers no comprehensive value-level data validation. It reconciles data at the schema, column, and row level, but cannot guarantee perfect 1:1 parity on a field-by-field basis without external tools.

• The translation process is not iterative or AI-driven—once it attempts a conversion, there is no built-in feedback loop to optimize or correct results until full semantic or value-level equivalence is achieved

[mark_ott]

Is there a way to standardize/optimize the conversion so it doesn’t blow up into thousands of lines?

Yes, there are actionable methods to standardize and optimize LakeBridge conversions to prevent code from ballooning into thousands of lines, but these require planning, tool configuration, and post-conversion steps.

Best Practices for Optimization

• Pre-Migration Assessment: Use LakeBridge’s Profiler and Analyzer components to thoroughly assess code complexity before conversion. This surfaces procedures and queries that are likely to "blow up" and flags problematic patterns early, allowing for manual pre-optimization.

• Modularize Legacy Code: Refactoring complex stored procedures and long SQL scripts into smaller, modular units reduces the risk of expansive code generation. Breaking up nested joins and expressions leads to tighter, more manageable output from the transpiler.

• Configure Transpiler Options: LakeBridge is pluggable—investigate switching between BladeBridge and Morpheus, or try modifying transpiler settings for join expansion, mapping, and fallback logic. Fine-tuning converter configuration can help standardize output size and reduce repetition, especially for JOIN/SET explosion issues.

• Manual Standardization: After automated conversion, manually merge repetitive SET or SELECT blocks and optimize procedural constructs. Using Databricks notebooks for visualizing and iterative editing makes this process easier to manage and audit.

• Advanced Tools: Consider using modern, AI-powered migration engines or custom plugins that provide semantic understanding and iterative feedback. While not yet integrated into LakeBridge, some partners offer these as add-ons or separate migration workflows