- Installation
- Documentation
- Getting Started
- Connect
- Data Import
- Overview
- Data Sources
- CSV Files
- JSON Files
- Overview
- Creating JSON
- Loading JSON
- Writing JSON
- JSON Type
- JSON Functions
- Format Settings
- Installing and Loading
- SQL to / from JSON
- Caveats
- Multiple Files
- Parquet Files
- Partitioning
- Appender
- INSERT Statements
- Client APIs
- Overview
- C
- Overview
- Startup
- Configuration
- Query
- Data Chunks
- Vectors
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Dart
- Go
- Java
- Julia
- Node.js (Neo)
- Node.js
- Python
- Overview
- Data Ingestion
- Conversion between DuckDB and Python
- DB API
- Relational API
- Function API
- Types API
- Expression API
- Spark API
- API Reference
- Known Python Issues
- R
- Rust
- Swift
- Wasm
- ADBC
- ODBC
- SQL
- Introduction
- Statements
- Overview
- ANALYZE
- ALTER TABLE
- ALTER VIEW
- ATTACH and DETACH
- CALL
- CHECKPOINT
- COMMENT ON
- COPY
- CREATE INDEX
- CREATE MACRO
- CREATE SCHEMA
- CREATE SECRET
- CREATE SEQUENCE
- CREATE TABLE
- CREATE VIEW
- CREATE TYPE
- DELETE
- DESCRIBE
- DROP
- EXPORT and IMPORT DATABASE
- INSERT
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- LOAD / INSTALL
- Query Syntax
- SELECT
- FROM and JOIN
- WHERE
- GROUP BY
- GROUPING SETS
- HAVING
- ORDER BY
- LIMIT and OFFSET
- SAMPLE
- Unnesting
- WITH
- WINDOW
- QUALIFY
- VALUES
- FILTER
- Set Operations
- Prepared Statements
- Data Types
- Overview
- Array
- Bitstring
- Blob
- Boolean
- Date
- Enum
- Interval
- List
- Literal Types
- Map
- NULL Values
- Numeric
- Struct
- Text
- Time
- Timestamp
- Time Zones
- Union
- Typecasting
- Expressions
- Overview
- CASE Statement
- Casting
- Collations
- Comparisons
- IN Operator
- Logical Operators
- Star Expression
- Subqueries
- Functions
- Overview
- Aggregate Functions
- Array Functions
- Bitstring Functions
- Blob Functions
- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Interval Functions
- Lambda Functions
- List Functions
- Map Functions
- Nested Functions
- Numeric Functions
- Pattern Matching
- Regular Expressions
- Struct Functions
- Text Functions
- Time Functions
- Timestamp Functions
- Timestamp with Time Zone Functions
- Union Functions
- Utility Functions
- Window Functions
- Constraints
- Indexes
- Meta Queries
- DuckDB's SQL Dialect
- Samples
- Configuration
- Extensions
- Overview
- Core Extensions
- Community Extensions
- Working with Extensions
- Versioning of Extensions
- Arrow
- AutoComplete
- AWS
- Azure
- Delta
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Iceberg
- ICU
- inet
- jemalloc
- MySQL
- PostgreSQL
- Spatial
- SQLite
- Substrait
- TPC-DS
- TPC-H
- VSS
- Guides
- Overview
- Data Viewers
- Database Integration
- File Formats
- Overview
- CSV Import
- CSV Export
- Directly Reading Files
- Excel Import
- Excel Export
- JSON Import
- JSON Export
- Parquet Import
- Parquet Export
- Querying Parquet Files
- Network and Cloud Storage
- Overview
- HTTP Parquet Import
- S3 Parquet Import
- S3 Parquet Export
- S3 Iceberg Import
- S3 Express One
- GCS Import
- Cloudflare R2 Import
- DuckDB over HTTPS / S3
- Meta Queries
- Describe Table
- EXPLAIN: Inspect Query Plans
- EXPLAIN ANALYZE: Profile Queries
- List Tables
- Summarize
- DuckDB Environment
- ODBC
- Performance
- Overview
- Environment
- Import
- Schema
- Indexing
- Join Operations
- File Formats
- How to Tune Workloads
- My Workload Is Slow
- Benchmarks
- Python
- Installation
- Executing SQL
- Jupyter Notebooks
- SQL on Pandas
- Import from Pandas
- Export to Pandas
- Import from Numpy
- Export to Numpy
- SQL on Arrow
- Import from Arrow
- Export to Arrow
- Relational API on Pandas
- Multiple Python Threads
- Integration with Ibis
- Integration with Polars
- Using fsspec Filesystems
- SQL Editors
- SQL Features
- Snippets
- Glossary of Terms
- Browse Offline
- Operations Manual
- Overview
- Limits
- Non-Deterministic Behavior
- Embedding DuckDB
- DuckDB's Footprint
- Securing DuckDB
- Development
- DuckDB Repositories
- Testing
- Overview
- sqllogictest Introduction
- Writing Tests
- Debugging
- Result Verification
- Persistent Testing
- Loops
- Multiple Connections
- Catch
- Profiling
- Release Calendar
- Building
- Benchmark Suite
- Internals
- Sitemap
- Why DuckDB
- Media
- FAQ
- Code of Conduct
- Live Demo
Examples
Unnest a list, generating 3 rows (1, 2, 3):
SELECT unnest([1, 2, 3]);
Unnesting a struct, generating two columns (a, b):
SELECT unnest({'a': 42, 'b': 84});
Recursive unnest of a list of structs:
SELECT unnest([{'a': 42, 'b': 84}, {'a': 100, 'b': NULL}], recursive := true);
Limit depth of recursive unnest using max_depth
:
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 2);
The unnest
special function is used to unnest lists or structs by one level. The function can be used as a regular scalar function, but only in the SELECT
clause. Invoking unnest
with the recursive
parameter will unnest lists and structs of multiple levels. The depth of unnesting can be limited using the max_depth
parameter (which assumes recursive
unnesting by default).
Unnesting Lists
Unnest a list, generating 3 rows (1, 2, 3):
SELECT unnest([1, 2, 3]);
Unnest a scalar list, generating 3 rows ((1, 10), (2, 11), (3, NULL)):
SELECT unnest([1, 2, 3]), unnest([10, 11]);
Unnest a scalar list, generating 3 rows ((1, 10), (2, 10), (3, 10)):
SELECT unnest([1, 2, 3]), 10;
Unnest a list column generated from a subquery:
SELECT unnest(l) + 10 FROM (VALUES ([1, 2, 3]), ([4, 5])) tbl(l);
Empty result:
SELECT unnest([]);
Empty result:
SELECT unnest(NULL);
Using unnest
on a list will emit one tuple per entry in the list. When unnest
is combined with regular scalar expressions, those expressions are repeated for every entry in the list. When multiple lists are unnested in the same SELECT
clause, the lists are unnested side-by-side. If one list is longer than the other, the shorter list will be padded with NULL
values.
An empty list and a NULL
list will both unnest to zero elements.
Unnesting Structs
Unnesting a struct, generating two columns (a, b):
SELECT unnest({'a': 42, 'b': 84});
Unnesting a struct, generating two columns (a, b):
SELECT unnest({'a': 42, 'b': {'x': 84}});
unnest
on a struct will emit one column per entry in the struct.
Recursive Unnest
Unnesting a list of lists recursively, generating 5 rows (1, 2, 3, 4, 5):
SELECT unnest([[1, 2, 3], [4, 5]], recursive := true);
Unnesting a list of structs recursively, generating two rows of two columns (a, b):
SELECT unnest([{'a': 42, 'b': 84}, {'a': 100, 'b': NULL}], recursive := true);
Unnesting a struct, generating two columns (a, b):
SELECT unnest({'a': [1, 2, 3], 'b': 88}, recursive := true);
Calling unnest
with the recursive
setting will fully unnest lists, followed by fully unnesting structs. This can be useful to fully flatten columns that contain lists within lists, or lists of structs. Note that lists within structs are not unnested.
Setting the Maximum Depth of Unnesting
The max_depth
parameter allows limiting the maximum depth of recursive unnesting (which is assumed by default and does not have to be specified separately).
For example, unnestig to max_depth
of 2 yields the following:
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 2) AS x;
x |
---|
[1, 2] |
[3, 4] |
[5, 6] |
[7, 8, 9] |
[] |
[10, 11] |
Meanwhile, unnesting to max_depth
of 3 results in:
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 3) AS x;
x |
---|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
Keeping Track of List Entry Positions
To keep track of each entry's position within the original list, unnest
may be combined with generate_subscripts
:
SELECT unnest(l) AS x, generate_subscripts(l, 1) AS index
FROM (VALUES ([1, 2, 3]), ([4, 5])) tbl(l);
x | index |
---|---|
1 | 1 |
2 | 2 |
3 | 3 |
4 | 1 |
5 | 2 |