Friendly SQL

Creating tables and inserting data:
- CREATE OR REPLACE TABLE: this clause allows avoiding DROP TABLE IF EXISTS statements in scripts.
- CREATE TABLE ... AS SELECT (CTAS): this clause allows creating a new table from the output of a table without manually defining a schema.
- INSERT INTO ... BY NAME: this variant of the INSERT statement allows using column names instead of positions.
Describing tables and computing statistics:
- DESCRIBE: this clause provides a succinct summary of the schema of a table or query.
- SUMMARIZE: this clause returns summary statistics for a table or query.
Making SQL clauses more compact:
- FROM-first syntax with an optional SELECT clause: DuckDB allows queries in the form of FROM tbl which selects all columns (performing a SELECT * statement).
- GROUP BY ALL: this clause allows omitting the group-by columns by inferring them from the list of attributes in the SELECT clause.
- ORDER BY ALL: this clause allows ordering on all columns (e.g., to ensure deterministic results).
- SELECT * EXCLUDE: the EXCLUDE option allows excluding specific columns from the * expression.
- SELECT * REPLACE: the REPLACE option allows replacing specific columns with different expressions in a * expression.
- UNION BY NAME: this clause performing the UNION operation along the names of columns (instead of relying on positions).
Transforming tables:
- PIVOT to turn long tables to wide tables.
- UNPIVOT to turn wide tables to long tables.
Defining SQL-level variables:
- SET VARIABLE
- RESET VARIABLE

Query Features

Column aliases in WHERE, GROUP BY, and HAVING
COLUMNS() expression can be used to execute the same expression on multiple columns:
Reusable column aliases, e.g.: SELECT i + 1 AS j, j + 2 AS k FROM range(0, 3) t(i)
Advanced aggregation features for analytical (OLAP) queries:
- FILTER clause
- GROUPING SETS, GROUP BY CUBE, GROUP BY ROLLUP clauses
count() shorthand for count(*)

Literals and Identifiers

Data Types

Data Import

Auto-detecting the headers and schema of CSV files
Directly querying CSV files and Parquet files
Loading from files using the syntax FROM 'my.csv', FROM 'my.csv.gz', FROM 'my.parquet', etc.
Filename expansion (globbing), e.g.: FROM 'my-data/part-*.parquet'

Functions and Expressions

Dot operator for function chaining: SELECT ('hello').upper()
String formatters: the format() function with the fmt syntax and the printf() function
List comprehensions
List slicing
String slicing
STRUCT.* notation
Simple LIST and STRUCT creation

Join Types

Trailing Commas

DuckDB allows trailing commas, both when listing entities (e.g., column and table names) and when constructing LIST items. For example, the following query works:

SELECT
    42 AS x,
    ['a', 'b', 'c',] AS y,
    'hello world' AS z,
;

"Top-N in Group" Queries

Computing the "top-N rows in a group" ordered by some criteria is a common task in SQL that unfortunately often requires a complex query involving window functions and/or subqueries.

To aid in this, DuckDB provides the aggregate functions max(arg, n), min(arg, n), arg_max(arg, val, n), arg_min(arg, val, n), max_by(arg, val, n) and min_by(arg, val, n) to efficiently return the "top" n rows in a group based on a specific column in either ascending or descending order.

For example, let's use the following table:

SELECT * FROM t1;

┌─────────┬───────┐
│   grp   │  val  │
│ varchar │ int32 │
├─────────┼───────┤
│ a       │     2 │
│ a       │     1 │
│ b       │     5 │
│ b       │     4 │
│ a       │     3 │
│ b       │     6 │
└─────────┴───────┘

We want to get a list of the top-3 val values in each group grp. The conventional way to do this is to use a window function in a subquery:

SELECT array_agg(rs.val), rs.grp 
FROM (
    SELECT val, grp, row_number() OVER (PARTITION BY grp ORDER BY val DESC) as rid 
    FROM t1 ORDER BY val DESC
) AS rs 
WHERE rid < 4 
GROUP BY rs.grp;

┌───────────────────┬─────────┐
│ array_agg(rs.val) │   grp   │
│      int32[]      │ varchar │
├───────────────────┼─────────┤
│ [3, 2, 1]         │ a       │
│ [6, 5, 4]         │ b       │
└───────────────────┴─────────┘

But in DuckDB, we can do this much more concisely (and efficiently!):

SELECT max(val, 3) FROM t1 GROUP BY grp;

┌─────────────┐
│ max(val, 3) │
│   int32[]   │
├─────────────┤
│ [3, 2, 1]   │
│ [6, 5, 4]   │
└─────────────┘

“Friendlier SQL with DuckDB” blog post
“Even Friendlier SQL with DuckDB” blog post
“SQL Gymnastics: Bending SQL into Flexible New Shapes” blog post

Clauses