Cross-System SQL Translation: Techniques & Applications

• Cross-system SQL translation is the automatic conversion of SQL queries across different dialects, preserving semantics amid syntactic differences.
• It employs methodologies like rule-based parsing, AST rewriting, and LLM-powered techniques to address dialect gaps, schema variations, and domain-specific challenges.
• Industry applications include cloud migration, federated analytics, and ETL pipelines, ensuring optimal performance in diverse database environments.

Cross-system SQL translation refers to the automatic transformation of SQL queries from one computational environment, dialect, or schema to another. This encompasses translating SQL for different database management systems (DBMSs), migrating procedures across SQL dialects during cloud transitions, compiling SQL to non-SQL computation environments (such as spreadsheets), and adapting query logic to new schemas or data domains. Technically, it requires both a deep understanding of syntactic variations across SQL dialects and the ability to preserve query semantics under potentially subtle execution model differences. The field draws from compiler construction, semantic parsing, program synthesis, and neural representation learning, intersecting with practical industrial imperatives such as cloud database migration, federated analytics, and natural language interfaces to databases.

1. Motivations and Scenarios for Cross-System SQL Translation

Cross-system SQL translation arises from the heterogeneity in database infrastructures and user interfaces:

• Dialect Transfer for Cloud Migration: Organizations migrating from on-premise DBMS (e.g., Oracle, IBM DB2) to cloud systems (e.g., AWS Redshift, Google BigQuery) encounter incompatibilities between SQL dialects. Even though core SQL standards (e.g., SQL-92) are widely supported, substantial differences exist in data types, built-in functions, string handling, procedural extensions, and query execution semantics (Zmigrod et al., 13 Mar 2024).
• SQL-to-Spreadsheet Compilation: For users who prefer traditional spreadsheet tools over database systems, translating SQL queries into spreadsheet formulae allows high-level relational manipulation in ubiquitous applications like Excel—without macros or external plugins (Sroka et al., 2013).
• Schema and Domain Adaptation: When transferring queries or NL2SQL examples across databases with different schemas, the translation system must map table/column references, join conditions, and literal values while maintaining semantic intent (Daviran et al., 9 Aug 2025).
• Dialect-bridging in Analytical Tools and Federated Systems: Cross-database analytics, ETL pipelines, and federated query engines require on-the-fly rewriting of queries to match the SQL variants and capabilities of underlying storage engines (Zhou et al., 1 Apr 2025).

In practice, even a small fraction of queries that fail automated translation can present a major engineering bottleneck, especially for large industrial systems spanning hundreds of thousands of lines of SQL (Zmigrod et al., 13 Mar 2024).

2. Methodologies for SQL Translation Across Systems

Cross-system SQL translation leverages a wide array of methodologies:

a) Rule-Based and Parser-Driven Techniques

Traditional approaches use formal grammars and hand-crafted rule sets to perform token-level substitutions, AST rewriting, and transformation of query fragments. For example, the AWS Schema Conversion Tool and Azure Database Schema Conversion Toolkit apply dialect-specific rewrite rules to transform data types and function signatures (Zmigrod et al., 13 Mar 2024). Rule-based systems like SQLGlot or components in CrackSQL encapsulate mappings for common dialect pairs, e.g., converting proprietary concatenation syntax to target-conformant expressions.

b) Tree and Template-Guided Translation

Hybrid frameworks parse queries into abstract syntax trees (AST) or intermediate relational algebra, then apply transformation rules or template-driven logic to adapt operators, joins, and nested structures (Sroka et al., 2013, Daviran et al., 9 Aug 2025). The template skeleton approach abstracts source queries by replacing all schema tokens and constants, then grounds them into the target schema via structured prompting and value substitution—maintaining structural alignment while adapting to domain specifics (Daviran et al., 9 Aug 2025).

c) LLM-Powered Translation

Recent approaches harness the zero/few-shot capabilities of LLMs to directly generate or assist in the translation of complex SQL segments. LLMs can be prompted with dialect-specific tutorials or function documentation to learn target-specific syntax (Pourreza et al., 22 Aug 2024, Zhou et al., 1 Apr 2025). However, direct LLM-based translation risks hallucination—generation of non-existent constructs or drift from the intended logic—which hybrid systems mitigate by segmenting queries, augmenting context with syntax embeddings, and deploying local-to-global adaptive strategies (Zhou et al., 1 Apr 2025).

d) Neural Syntax Embedding and Cross-Dialect Alignment

Advanced systems use embedding models to capture syntax alignment across dialects. CrackSQL's cross-dialect embedding model encodes AST structure, syntax specifications (via Mixture-of-Experts), and textual descriptions, training on both positive (true equivalence) and hard negative (confusable but distinct) pairs (Zhou et al., 1 Apr 2025). This enables robust retrieval and substitution of equivalent functions/operators during translation.

e) Data-Driven and Imitation Learning Approaches

Imitation learning leverages a small set of example corrections provided by experts for error-prone fragments. The translation system generalizes these corrections as tree-to-tree transforms, achieving substantial coverage (successful conversion for over 80% of unique error types with very few examples) (Zmigrod et al., 13 Mar 2024).

f) Intermediate Representation Normalization

Frameworks like Natural SQL (NatSQL) decompose SQL into normalized, simplified forms (e.g., folding set operators into WHERE clauses and eliminating nested subqueries), thereby reducing translation complexity and improving generator reliability across systems (Gan et al., 2021).

3. System Architectures and Practical Implementations

Several system-level implementations exemplify combinations of the above methodologies:

System	Core Principle	Notable Techniques
CrackSQL (Zhou et al., 1 Apr 2025)	Hybrid Rule-LLM Translation	AST segmentation, embedding alignment
OpenIVM (Battiston et al., 25 Apr 2024)	SQL-to-SQL Incremental Compilation	Delta tables, relational operator diff
SQL-GEN (Pourreza et al., 22 Aug 2024)	Synthetic Data for Dialect Diversity	Dialect tutorial expansion, MoE merge
PARROT (Zhou et al., 27 Sep 2025)	Benchmarking LLM SQL Translation	System-specific test pairs, Acc_EX/RES
Spreadsheet Compiler (Sroka et al., 2013)	SQL-to-Formula Compilation	R1C1 formula mapping, BFS/DFS formulas

CrackSQL exemplifies a multi-module approach, segmenting queries via AST, translating segments using rules and LLMs, and utilizing cross-dialect embeddings for syntax retrieval. OpenIVM compiles view definitions into incremental SQL maintenance scripts, enabling propagation of delta updates between heterogeneous DBMS in Hybrid Transactional/Analytical Processing (HTAP) systems (Battiston et al., 25 Apr 2024). SQL-GEN automates synthetic data generation for multiple dialects and unifies models in a mixture-of-experts architecture, merging self-attention layers via spherical linear interpolation (SLERP) and initializing expert routers with keyword-derived embeddings (Pourreza et al., 22 Aug 2024).

Systems like PARROT provide diverse benchmarks and executable correctness metrics to drive comparative evaluation of translation quality across more than twenty production-grade systems (Zhou et al., 27 Sep 2025).

4. Evaluating Correctness, Robustness, and Limitations

Evaluation of cross-system SQL translation must address both syntactic and semantic fidelity. Metrics used include:

• Dialect Compatibility (Acc_EX): Percentage of translations that are syntactically executable on the target system (Zhou et al., 27 Sep 2025).
• Result Consistency (Acc_RES): Percentage yielding identical results when executed on source and target systems.

PARROT demonstrates that current LLM-based translators achieve Acc_EX and Acc_RES ranging approximately from 17% to 60%, indicating considerable headroom for improvement, especially for dialects with large syntactic divergences or less documentation.

Failures arise from:

• Function/operator misalignments (e.g., string concatenation or division behaviors differing across MySQL, Oracle, PostgreSQL).
• Inconsistent handling of schema-dependent constants, e.g., translation of error-prone constructs like division by zero using dialect-neutral wrappers like “1 / NULLIF(col, 0)” (Zhou et al., 27 Sep 2025).
• LLM hallucinations or under-specified context.

Hybrid and embedding-enhanced approaches, as in CrackSQL, substantially reduce errors such as syntax-rule violations and missing functions. However, even with manual rule sets and template abstraction, not all queries are mappable: source queries may express logic absent or undeclared in the target schema, or the LLM may induce unwanted schema leakage.

5. Applications, Impact, and Benchmarking

Applications are broad and span the following domains:

• Cloud Database Migration: Automated, reliable translation of stored procedures and business logic minimizes manual intervention and dual-hosting costs, which is crucial when even a small untranslated fraction incurs unsustainable workload for large organizations (Zmigrod et al., 13 Mar 2024, Zhou et al., 1 Apr 2025).
• Federated Analytics and ETL: Enables analytic platforms to interoperate with multiple backend DBMS, adapting to system-specific constraints.
• Materialized View Maintenance in HTAP: Systems like OpenIVM provide SQL-to-SQL compilation of incremental view logic, orchestrating delta propagation between OLTP (e.g., PostgreSQL) and OLAP (e.g., DuckDB) resources (Battiston et al., 25 Apr 2024).
• Benchmarks for Research and Leaderboards: Datasets like PARROT, with complex translation pairs and publicly available leaderboards, drive quantitative progress and comparative assessment (Zhou et al., 27 Sep 2025).

6. Emerging Directions and Open Challenges

Future research is exploring several promising avenues:

• Segment-Based and Adaptive Translation: Decomposition of complex queries and iterative local-to-global correction improves robustness as queries increase in size and complexity (Zhou et al., 1 Apr 2025).
• MoE-Driven Multi-Dialect Model Unification: Mixture-of-experts models initialized and merged across dialect-specific experts (e.g., using SLERP and dialect-keyword embedding) balance shared syntax learning with dialect specialism and improve scalability (Pourreza et al., 22 Aug 2024).
• Benchmark Construction and Reproducibility: Initiatives like PARROT and SQL-Exchange encourage fine-grained evaluation of both structure preservation and semantic equivalence, offering expanded coverage of dialects and precise diagnostic metrics (Daviran et al., 9 Aug 2025, Zhou et al., 27 Sep 2025).
• Limitations Related to Schema and Domain Drift: Mappings falter if sufficient structural overlap is lacking or semantic keys do not generalize (Daviran et al., 9 Aug 2025). Evaluation benchmarks and methods are being developed to better diagnose these limitations.
• Automation Versus Hallucination Trade-Offs: Research is focused on minimizing LLM hallucinations through hybridization and context enhancement, validating output with rule-based and embedding-based correctness checks.

7. Summary Table: Representative Systems and Features

System	Translation Method	Target Use Case	Notable Features
CrackSQL	Hybrid Rule+LLM+Embed	Multi-dialect SQL	AST segmentation, cross-dialect syntax embeddings
SQL-GEN	Synthetic Data + MoE	Text-to-SQL, Multi-dialect	Tutorial expansion, attention-layer merging
OpenIVM	SQL-to-SQL Compiler	View maintenance (HTAP)	Delta logic, integration with DuckDB and PostgreSQL
Spreadsheet Compiler	Relational Algebra → Formula	SQL-to-Spreadsheet	O(n log² n) sort, BFS/DFS, NULL handling
PARROT	Benchmark & Evaluation	SQL-to-SQL (22 DBMS)	Exe. accuracy, result consistency, public leaderboard

• Translating Relational Queries into Spreadsheets (Sroka et al., 2013)
• Translating between SQL Dialects for Cloud Migration (Zmigrod et al., 13 Mar 2024)
• OpenIVM: a SQL-to-SQL Compiler for Incremental Computations (Battiston et al., 25 Apr 2024)
• SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging (Pourreza et al., 22 Aug 2024)
• CrackSQL: A Hybrid SQL Dialect Translation System Powered by LLMs (Zhou et al., 1 Apr 2025)
• PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation (Zhou et al., 27 Sep 2025)
• SQL-Exchange: Transforming SQL Queries Across Domains (Daviran et al., 9 Aug 2025)
• Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (Gan et al., 2021)

These systems and frameworks collectively define the leading edge of cross-system SQL translation research and deployment, with new benchmarks and hybrid approaches poised to address the persistent challenges of dialect diversity, semantic preservation, and large-scale automation.