Free DuckDB SQL Formatter & Beautifier Online
Format DuckDB queries and analytical transformations. AST-verified formatting with Monaco Git Diff inspection.
About DuckDB SQL formatting
DuckDB is an embedded analytical database with a compact SQL dialect that is particularly useful for local data work. Queries often read Parquet, CSV, or JSON files directly through table functions, and list and struct values are first-class data. DuckDB also supports friendly aliases, positional references, regular-expression functions, and QUALIFY for filtering the result of a window function. The syntax feels close to PostgreSQL while adding a practical collection of data-frame-style operations.
This DuckDB formatter preserves those analytical idioms while keeping the relational structure clear. read_parquet and similar table functions remain readable as sources, list aggregations and struct field access stay grouped, and QUALIFY is separated from WHERE and HAVING so its post-window evaluation is obvious. Long DATE_TRUNC expressions, window frames, lambda-like list operations, and nested projections are broken at useful boundaries without changing literals or file paths.
The preset query demonstrates Parquet ingestion, nested location fields, list aggregation, a rolling window, and QUALIFY. It is useful for local notebooks, data pipeline prototypes, and reproducible analytics scripts. Paste a DuckDB query into the editor, choose your casing and line-length preferences, format it, and inspect the diff before saving it to a notebook or a transformation project.
That context helps when a local query moves from exploration into a tested transformation.
A stable layout is especially useful in DuckDB because table-function paths and nested values often sit beside ordinary columns in the same SELECT list. The formatted result makes it easier to distinguish file access, list manipulation, and final ranking logic when a local experiment becomes a repeatable data pipeline.
The page also works well for SQL generated from a notebook or a Python data workflow. It gives a compact local query the same review-friendly structure as a warehouse query while retaining DuckDB functions, list syntax, and file-oriented table sources.
What's different about DuckDB formatting? DuckDB formatting treats file-reading table functions, list and struct values, and post-window QUALIFY as readable data stages. Example: SELECT * FROM read_parquet('data/*.parquet') QUALIFY row_number() OVER (ORDER BY ts DESC) = 1;
DuckDB formatting highlights
- DuckDB parquet, JSON, and CSV reader functions
- List and struct transformations
- QUALIFY clauses and regex matching
- Positional reference and columns expression
DuckDB column aliases without AS and advanced struct functions are handled. The editor above is already configured for DuckDB; format the included sample or replace it with a query from your own project.
DuckDB formatter FAQ
Can I format DuckDB parquet reading queries?
Yes, read_parquet, read_csv, and other table functions are formatted cleanly.
Need another engine? Browse the multi-dialect SQL formatter or choose a related dialect below.
Supported SQL Dialects & Engines
Format SQL queries across standard and cloud database dialects using native parser engines and AST-based formatting.
Best-in-class GoogleSQL formatting with native AST engine, pipe syntax support, and zero SQL storage.
Format PostgreSQL queries with support for JSONB, recursive CTEs, and advanced analytical window functions.
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Microsoft ScriptDOM formatting for SQL Server and T-SQL, with versioned grammar, brackets, and CROSS APPLY joins.
High-speed SQL formatting optimized for ClickHouse analytical queries, arrays, and PREWHERE clauses.
Format Snowflake SQL queries and semi-structured expressions.
Format DuckDB queries and analytical transformations.
Format SQLite SQL queries, schemas, and triggers with clean indentation.
Format Oracle SQL queries with support for JSON_TABLE, CONNECT BY hierarchies, and analytical functions.
Format Databricks SQL, Delta Lake time travel (TIMESTAMP AS OF), STRUCT literals, and LATERAL VIEWs.
Format Apache Spark SQL queries, LATERAL VIEWs, struct access, and window transformations.
Format HiveQL queries, LATERAL VIEWs, PARTITION BY, and CLUSTER BY clauses.
Format Amazon Redshift analytical queries, VACUUM, and window functions.
Standard ANSI SQL formatting that works universally across database engines and query editors.