- Installation
- Documentation
- Getting Started
- Connect
- Data Import and Export
- 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
- Lakehouse Formats
- Client APIs
- Overview
- Tertiary Clients
- ADBC
- C
- Overview
- Startup
- Configuration
- Query
- Data Chunks
- Vectors
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Overview
- Arguments
- Dot Commands
- Output Formats
- Editing
- Safe Mode
- Autocomplete
- Syntax Highlighting
- Known Issues
- Dart
- Go
- Java (JDBC)
- Julia
- Node.js (Deprecated)
- Node.js (Neo)
- ODBC
- PHP
- 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
- SQL
- Introduction
- Statements
- Overview
- ANALYZE
- ALTER DATABASE
- 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
- LOAD / INSTALL
- MERGE INTO
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- SHOW and SHOW DATABASES
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- 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 Expression
- Casting
- Collations
- Comparisons
- IN Operator
- Logical Operators
- Star Expression
- Subqueries
- TRY
- 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
- Overview
- Indexing
- Friendly SQL
- Keywords and Identifiers
- Order Preservation
- PostgreSQL Compatibility
- SQL Quirks
- Samples
- Configuration
- Extensions
- Overview
- Installing Extensions
- Advanced Installation Methods
- Distributing Extensions
- Versioning of Extensions
- Troubleshooting of Extensions
- Core Extensions
- Overview
- AutoComplete
- Avro
- AWS
- Azure
- Delta
- DuckLake
- Encodings
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Iceberg
- Overview
- Iceberg REST Catalogs
- Amazon S3 Tables
- Amazon SageMaker Lakehouse (AWS Glue)
- Troubleshooting
- ICU
- inet
- jemalloc
- MySQL
- PostgreSQL
- Spatial
- SQLite
- TPC-DS
- TPC-H
- UI
- 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
- File Access with the file: Protocol
- 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
- Fastly Object Storage Import
- 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
- Working with Huge Databases
- Python
- Installation
- Executing SQL
- Jupyter Notebooks
- marimo 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
- Creating Synthetic Data
- Dutch Railway Datasets
- Sharing Macros
- Analyzing a Git Repository
- Importing Duckbox Tables
- Copying an In-Memory Database to a File
- Troubleshooting
- Glossary of Terms
- Browsing Offline
- Operations Manual
- Overview
- DuckDB's Footprint
- Installing DuckDB
- Logging
- Securing DuckDB
- Non-Deterministic Behavior
- Limits
- DuckDB Docker Container
- Development
- DuckDB Repositories
- Profiling
- Building DuckDB
- Overview
- Build Configuration
- Building Extensions
- Android
- Linux
- macOS
- Raspberry Pi
- Windows
- Python
- R
- Troubleshooting
- Unofficial and Unsupported Platforms
- Benchmark Suite
- Testing
- Internals
- Sitemap
- Live Demo
The CASE expression performs a switch based on a condition. The basic form is identical to the ternary condition used in many programming languages (CASE WHEN cond THEN a ELSE b END is equivalent to cond ? a : b). With a single condition this can be expressed with IF(cond, a, b).
CREATE OR REPLACE TABLE integers AS SELECT unnest([1, 2, 3]) AS i;
SELECT i, CASE WHEN i > 2 THEN 1 ELSE 0 END AS test
FROM integers;
| i | test |
|---|---|
| 1 | 0 |
| 2 | 0 |
| 3 | 1 |
This is equivalent to:
SELECT i, IF(i > 2, 1, 0) AS test
FROM integers;
The WHEN cond THEN expr part of the CASE expression can be chained, whenever any of the conditions returns true for a single tuple, the corresponding expression is evaluated and returned.
CREATE OR REPLACE TABLE integers AS SELECT unnest([1, 2, 3]) AS i;
SELECT i, CASE WHEN i = 1 THEN 10 WHEN i = 2 THEN 20 ELSE 0 END AS test
FROM integers;
| i | test |
|---|---|
| 1 | 10 |
| 2 | 20 |
| 3 | 0 |
The ELSE clause of the CASE expression is optional. If no ELSE clause is provided and none of the conditions match, the CASE expression will return NULL.
CREATE OR REPLACE TABLE integers AS SELECT unnest([1, 2, 3]) AS i;
SELECT i, CASE WHEN i = 1 THEN 10 END AS test
FROM integers;
| i | test |
|---|---|
| 1 | 10 |
| 2 | NULL |
| 3 | NULL |
It is also possible to provide an individual expression after the CASE but before the WHEN. When this is done, the CASE expression is effectively transformed into a switch statement.
CREATE OR REPLACE TABLE integers AS SELECT unnest([1, 2, 3]) AS i;
SELECT i, CASE i WHEN 1 THEN 10 WHEN 2 THEN 20 WHEN 3 THEN 30 END AS test
FROM integers;
| i | test |
|---|---|
| 1 | 10 |
| 2 | 20 |
| 3 | 30 |
This is equivalent to:
SELECT i, CASE WHEN i = 1 THEN 10 WHEN i = 2 THEN 20 WHEN i = 3 THEN 30 END AS test
FROM integers;