Abstract syntax tree construction

abstract-syntax-tree-construction

Summary

Parse domain-specific query language strings into structured abstract syntax tree (AST) representations that capture the hierarchical syntax and semantics of the query. This skill is essential when implementing a domain-specific language (DSL) interpreter that needs to transform human-readable query expressions into machine-processable intermediate representations for validation and execution.

When to use

Apply this skill when you have a tokenized sequence of domain-specific language tokens and need to construct a hierarchical, unambiguous representation that can be validated against language design principles (expressiveness, precision, scalability, readability) and passed to downstream execution engines. Specifically, use this when building a parser for SQL-inspired query syntax extended with domain-specific assumptions (e.g., mass spectrometry-specific filters like mass-to-charge ratio, retention time, intensity thresholds).

When NOT to use

• Input query is already in a pre-parsed, canonical intermediate format (JSON, protobuf) — proceed directly to validation or execution.
• Query syntax is unambiguous, fully deterministic, and requires no hierarchical decomposition — a simple state machine or regex-based matcher may suffice.
• The domain-specific language has no formal grammar definition or the grammar is under active revision — defer AST construction until the grammar is stabilized.

Inputs

• tokenized query string (sequence of lexical tokens from domain-specific query language)
• formal grammar definition (BNF or EBNF describing syntax and operator precedence)
• mass spectrometry query expressions (MS1MZ, MS2DATA, retention time, intensity filters, tolerance parameters)

Outputs

• abstract syntax tree (AST) or query object representation
• canonical serialization (JSON or Python pickle) of the parse tree
• structured query object ready for execution or analysis

How to apply

Begin by defining a formal grammar capturing the domain-specific language syntax and mass spectrometry-specific operators (e.g., MS1MZ, MS2DATA, tolerance parameters, intensity matching clauses). Construct a tokenizer that lexically analyzes input query strings into recognized tokens (keywords, literals, operators). Build a recursive descent parser or use a parser generator (ANTLR, PLY) that consumes the token sequence and produces a structured AST or query object. At each parse step, validate that clauses respect domain constraints (e.g., retention time and mass-to-charge filters are semantically coherent). Return the AST in a canonical, serializable format (JSON or pickled Python object) suitable for downstream query validation and execution across single spectra or entire data repositories.

Related tools

• ANTLR (Parser generator for constructing recursive descent parsers from formal grammars; generates lexer and parser from DSL grammar specification)
• PLY (Python Lex-Yacc) (Python-based parser generator for tokenization and AST construction from domain-specific query syntax)
• MassQL reference implementation (Reference Python API demonstrating tokenization, parsing, and AST construction for SQL-inspired mass spectrometry queries; includes msql_engine.process_query() for query execution) — https://github.com/mwang87/MassQueryLanguage

Examples

from massql import msql_engine; results_df = msql_engine.process_query('QUERY scaninfo(MS2DATA) WHERE MS1MZ=100', 'input_file.mzML')

Evaluation signals

• AST structure reflects the syntactic hierarchy and operator precedence of the query language; nested clauses are correctly subordinated (e.g., WHERE predicates are children of QUERY nodes).
• All terminals in the parse tree are valid tokens from the domain; no spurious or unrecognized tokens remain.
• Roundtrip property: serializing the AST back to query syntax and re-parsing should produce an isomorphic tree.
• Query validation passes: the AST respects domain constraints (e.g., mass-to-charge filters have valid numeric ranges, tolerance parameters are positive, intensity match clauses are semantically coherent).
• Downstream execution engines (query engines) can traverse the AST without ambiguity to generate results; no missing or malformed subtrees.

Limitations

• Parser construction requires a stable, complete formal grammar; incomplete or ambiguous grammar specifications lead to parse conflicts and incorrect AST structures.
• Performance degrades for deeply nested queries (e.g., complex boolean expressions with many AND/OR clauses) or very long token streams; recursive descent parsers may hit stack limits.
• Error recovery and diagnostic messaging depend on parser implementation; generic parser generators may produce unhelpful error messages for domain experts unfamiliar with parsing concepts.
• Domain-specific assumptions (e.g., mass spectrometry filter semantics) must be encoded in the grammar or as post-parse validation; the parser alone cannot enforce domain correctness without explicit constraints.

Evidence

• [other] Construct a parser (e.g., using recursive descent or parser generator such as ANTLR or PLY) that consumes tokens and produces a structured abstract syntax tree (AST) or query object representation.: "Construct a parser (e.g., using recursive descent or parser generator such as ANTLR or PLY) that consumes tokens and produces a structured abstract syntax tree (AST) or query object representation."
• [other] Validate the parse tree against MassQL design principles: expressiveness (captures complex MS patterns), scalability (handles single spectrum to repository queries), and readability (natural MS semantics).: "Validate the parse tree against MassQL design principles: expressiveness (captures complex MS patterns), scalability (handles single spectrum to repository queries), and readability (natural MS"
• [other] Return the parse tree in a canonical format (e.g., JSON or pickled Python object) for downstream query execution or analysis.: "Return the parse tree in a canonical format (e.g., JSON or pickled Python object) for downstream query execution or analysis."
• [readme] MassQL is inspired by SQL, but it attempts to bake in assumptions of mass spectrometry to make querying much more natural for mass spectrometry users.: "MassQL is inspired by SQL, but it attempts to bake in assumptions of mass spectrometry to make querying much more natural for mass spectrometry users."
• [readme] Language Grammar, Reference Implementation Python API, Command line Utility to execute: "Language Grammar, Reference Implementation Python API, Command line Utility to execute"