Apache Spark SQL SQL Minifier
Free online Apache Spark SQL minifier. Compress Spark SQL queries into a single line while preserving LATERAL VIEW, EXPLODE, MERGE INTO, and Delta Lake DDL syntax.
What is Apache Spark SQL?
Apache Spark SQL is the SQL interface for Apache Spark, enabling analysts and engineers to query structured and semi-structured data using familiar SQL syntax at scale. It can run on data stored in HDFS, S3, Delta Lake, Apache Hive, and many other data sources.
Spark SQL supports ANSI SQL plus Spark-specific extensions including LATERAL VIEW with table-generating functions (EXPLODE, POSEXPLODE), MERGE INTO for Delta Lake upserts, and Hive-compatible CREATE TABLE syntax with USING, PARTITIONED BY, and LOCATION options.
The Spark SQL formatter handles Spark's unique syntax — including LATERAL VIEW, EXPLODE(), Delta Lake MERGE INTO, and external table DDL — formatting complex data engineering queries into clean, maintainable SQL.
Frequently asked questions
Will Spark SQL LATERAL VIEW expressions be preserved?
Yes. LATERAL VIEW with EXPLODE(), POSEXPLODE(), and other table-generating functions are minified correctly — LATERAL VIEW syntax is preserved inline with surrounding whitespace removed.
Why minify Spark SQL?
Spark SQL is often minified when embedding queries in PySpark spark.sql() calls, Airflow SparkSubmitOperator SQL strings, or dbt Spark adapter models where compact single-line SQL is preferred.
Will MERGE INTO statements be safely minified?
Yes. Spark SQL MERGE INTO (used with Delta Lake) statements are minified correctly, preserving all WHEN clauses and their corresponding actions on a single line.
Does minification affect Spark SQL execution plans?
No. Spark's SQL parser is whitespace-agnostic. The resulting Logical Plan and Physical Plan are identical regardless of whether the SQL is formatted or minified.
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