SQL Compatibility

Data Storage and Sources
Updated on:
April 16, 2024

What is SQL compatibility?

SQL compatibility measures how well a database or analytics engine conforms to SQL standards in areas like syntax, datatypes, functions, operators, and handling of nulls and three-valued logic. Higher compatibility allows easier migration from other SQL environments.

Core SQL elements like SELECT, JOIN, GROUP BY, aggregates should function similarly. Vendor specific SQL extensions may provide additional functionality if needed. Adhering closely to standard SQL helps limit lock-in.

For analytics engines like Apache Arrow DataFusion, SQL compatibility enables users to leverage existing SQL skills. It also simplifies migrating analytics workflows from traditional relational databases.

The query execution engine needs to handle standard SQL correctly. SQL with minimal proprietary extensions makes it easier to switch between analytical tools if needed. Standard SQL skills apply across compatible platforms.

What does it do/how does it work?

High SQL compatibility lets developers reuse SQL skills and code while limiting proprietary extensions. Standards compliance also aids interoperability between different systems.

ANSI SQL standard features are implemented in a compatible way. Data types, operators, functions behave predictably. Code portability is enhanced through consistent handling of nulls, dates, strings, identifiers, etc. Extensions build on top of standards.

Why is it important? Where is it used?

SQL skills are widely useful across many databases and systems. SQL compatibility allows easier migration between platforms, integration across systems, and code reusability.

High SQL compatibility is important for cloud analytics services, data lake environments, and data warehouses to attract users. It lowers switching costs compared to proprietary SQL dialects.

FAQ

What are some key areas of SQL compatibility?

  • Syntax - SELECT, JOIN, filtering, aggregation
  • Functions - String, date, math, conditional, etc.
  • Data types - Numeric, text, dates, arrays
  • Null and three-valued logic
  • Identifier case sensitivity
  • Comments and statement delimiters

What are some challenges with compatibility?

  • Gaps between standard and implementation
  • Varying support for newer standards
  • Tuning for performance over standards compliance
  • Pressure to add proprietary extensions

How can compatibility be measured?

  • SQL compatibility test suites
  • Porting representative queries/applications
  • Testing edge cases like nulls, datatypes
  • Evaluating use of proprietary features

How is compatibility evolving?

  • Expanded standards for JSON, relations, analytics
  • Added datatypes like spatial, XML, JSON
  • Window functions, CTEs, recursive queries
  • Alternative query languages gaining adoption

References:

Related Entries

Apache Arrow DataFusion

Apache DataFusion is an extensible, high-performance data processing framework in Rust, designed to efficiently execute analytical queries on large datasets. It utilizes the Apache Arrow in-memory data format.

Read more ->
Relational Database

A relational database is a type of database that stores and provides access to data according to relations between defined entities organized in tables.

Read more ->
Query Execution

Query execution is the process of carrying out the actual steps to retrieve results for a database query as per the generated execution plan.

Read more ->

Get early access to AI-native data infrastructure