SQL Parser Selection and Implementation: ANTLR vs Apache Calcite for Big Data Applications

The article discusses the motivation for implementing SQL parsers in big data systems to lower the barrier for users unfamiliar with specialized APIs.

It explains that traditional SQL queries rely on relational databases, but massive data requires big data components like Hive, Spark, Flink, Kafka, HBase, some of which lack native SQL support.

By introducing a SQL parser, a single interface can adapt to various backend components, simplifying development and maintenance.

The core components of a SQL parser are lexical analysis, syntax analysis, and semantic analysis, illustrated with examples such as SELECT name FROM tab; and SELECT name FROM tab WHERE id=1001; .

The article then compares two popular implementations: ANTLR, which requires defining grammar files and generating code, and Apache Calcite, which reuses existing parsers (JavaCC) and provides modular query optimization and execution.

An example ANTLR grammar snippet is shown: ID : [a-zA-Z]+ ; INT : [0-9]+ ; and a simple listener for extracting table names.

A Calcite example demonstrates querying JSON datasets with a few lines of code using JSqlUtils.

The comparison concludes that Calcite offers lower learning curve and higher flexibility, making it preferable for production big data SQL layers.

Finally, the article outlines typical scenarios where a SQL parser is beneficial: providing customizable SQL for relational databases, offering JDBC/ODBC interfaces, aiding non‑programmer data analysts, and enabling SQL on big data components that lack it.