Compilers translate high-level programming languages into machine code, allowing computers to execute written programs. They perform this translation through several stages, including lexical analysis, syntax analysis, semantic analysis, optimization, and code generation.

The PartiQL query language [2] compiler transform high-level query statements into a logical plan ready for evaluation, enabling the processing of queries across various data sources without the need for direct execution on a specific database system. This transformation process unfolds through several stages, including lexical analysis, syntax analysis, semantic analysis, query optimization, and logical plan generation.

In PartiQL’s context, the compiler aims to bridge the gap between human-readable queries and logical plan, optimizing the query for logical efficiency and effectiveness. For instance, a PartiQL compiler takes a query that selects records based on specific criteria from a dataset and converts it into an optimized logical plan. This plan is then ready for evaluation across multiple data formats and storage systems, ensuring engine-independent querying without direct reliance on the underlying physical storage mechanisms.

The compilation and evaluation steps for queries in languages like PartiQL are crucial for processing and interpreting user requests across various data environments. These steps transform user queries from a human-readable format into a logical plan that systems can evaluate to produce results. The necessity for these steps arises from several fundamental requirements:

  • Correctness: Ensuring queries are syntactically and semantically correct. This correctness guarantees an accurate representation of the user’s intent and executable queries within the system’s framework. For example, semantic analysis checks if SELECT age FROM employees references a valid age column in employees.
  • Efficiency: Optimizing queries through transformation and logical planning allows systems to process requests faster and more resource-efficiently. For instance, query optimization might reorder a query SELECT name FROM employees WHERE department = 'HR' AND age > 30 to first filter on the more selective condition, such as age > 30 if it is determined to reduce the search space more significantly.
  • Flexibility: These steps enable query languages to abstract from physical storage details, allowing the same query to run across different storage systems without user modification. An example is transforming SELECT * FROM logs WHERE date > '2022-01-01' into a logical plan that can be applied whether logs are stored in a SQL database, a NoSQL document store, or a distributed file system.
  • Interoperability: A standardized process ensures that queries can be executed in diverse environments, enhancing the portability of applications. For example, a logical plan for SELECT id, COUNT(*) FROM transactions GROUP BY id can be evaluated in a traditional database or a stream-processing system, allowing the same analytics logic in batch or real-time contexts.

These methodologies are rooted in computer science, specifically in compilers, databases, and algorithms, leveraging unique data structures and algorithms:

  • Lexical Analysis uses finite automata to tokenize input text, breaking down SELECT * FROM table WHERE condition into tokens like SELECT, *, FROM, table, WHERE, and condition.
  • Syntax Analysis employs parsing algorithms to construct a parse tree, ensuring the query INSERT INTO employees (name, age) VALUES ('John', 30) adheres to the correct format of an INSERT statement.
  • Semantic Analysis relies on symbol tables to validate the semantic correctness of UPDATE employees SET age = age + 1 WHERE id = 5, ensuring age and id are valid columns.
  • Query Transformation and Optimization utilize algorithms to rewrite SELECT name FROM employees WHERE department = 'HR' OR age < 25 for efficiency, perhaps by applying an index on department or age if available.
  • Logical Plan Generation and Evaluation involve creating data flow plans for SELECT COUNT(*) FROM orders GROUP BY customerId, using data structures like hash tables for aggregation.

Inputs, outputs, and dependencies

Compiler steps

For PartiQL, a query language that abstracts over the specifics of physical data storage and execution layers, the compilation process focuses on transforming a high-level query into a form that’s ready for evaluation, rather than execution on a specific database system. The steps involved in compiling a PartiQL query, with their inputs, outputs, and dependencies as shown in the above diagram are:

  1. Lexical Analysis
    • Inputs: Query text.
    • Outputs: Tokens (such as keywords, identifiers, literals).
    • Dependencies: Lexical rules specific to PartiQL.
  2. Syntax Analysis (Parsing)
    • Inputs: Tokens.
    • Outputs: Parse tree or Abstract Syntax Tree (AST).
    • Dependencies: Grammar [1] rules defined by PartiQL.
  3. Semantic Analysis
    • Inputs: Parse tree or AST.
    • Outputs: Annotated AST, where annotations include type information and scope bindings.
    • Dependencies:
      • Schema information (if applicable): Validates table names, column names, and functions against a logical schema.
      • Type system: Ensures operations are semantically correct, given the types of operands.
  4. Query Transformation/Optimization
    • Inputs: Annotated AST.
    • Outputs: Optimized logical plan.
    • Dependencies:
      • Optimization rules: Applied to transform the query into a more efficient form without a specific focus on physical execution strategies.
      • Logical data model: Considerations of how data is logically organized in PartiQL’s abstract environment.
  5. Logical Plan Generation
    • Inputs: Optimized logical plan.
    • Outputs: Evaluation-ready logical plan.
    • Dependencies:
      • Data access patterns: General patterns for how data can be retrieved or manipulated logically.
      • Environment constraints: Any limitations or features of the evaluation environment that might influence plan formulation.
  6. Evaluation
    • Inputs: Evaluation-ready logical plan.
    • Outputs: Query results.
    • Dependencies:
      • Evaluation engine: The component that interprets the logical plan and computes results based on the logical data model.
      • Logical data sources: Abstract representations of data sources that PartiQL queries can operate over.

Given PartiQL’s design to operate independently of a physical storage or execution layer, its compilation process emphasizes transforming queries into logical plans optimized for evaluation. This approach allows PartiQL to serve as a bridge for querying data across different formats and storage systems with a unified syntax, ensuring flexibility and broad applicability in data querying and manipulation.

Summary

The PartiQL compiler transforms human-readable queries into optimized, evaluation-ready logical plans. This process, encompassing lexical analysis, syntax analysis, semantic analysis, query optimization, and logical plan generation, ensures efficient and effective query evaluation across diverse data sources. Unlike traditional compilers that target machine code generation for direct execution on hardware, the PartiQL compiler focuses on abstracting queries from the specifics of physical data storage.

Through this sophisticated compilation process, PartiQL empowers users to interact with data flexibly and efficiently, bridging the gap between complex data structures and actionable insights without being tethered to a single storage model or execution environment.

In the following posts, we will unravel each topic in sufficient detail.

References

  1. The Role of Grammar in PartiQL
  2. What is PartiQL?