Search Shortcut cmd + k | ctrl + k

- Installation
- Documentation

- Getting Started
- Connect

- Overview
- Concurrency

- Data Import

- Overview
- Data Sources
- CSV Files

- Overview
- Auto Detection
- Reading Faulty CSV Files
- Tips

- 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

- Overview
- Combining Schemas

- Parquet Files

- Overview
- Metadata
- Encryption
- Tips

- Partitioning

- Hive Partitioning
- Partitioned Writes

- 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

- Overview
- Arguments
- Dot Commands
- Output Formats
- Editing
- Autocomplete
- Syntax Highlighting

- Dart
- Go
- Java
- Julia
- Node.js (Neo)

- Overview

- Node.js

- Overview
- API Reference

- 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

- Overview
- Instantiation
- Data Ingestion
- Query
- Extensions

- ADBC
- ODBC

- Overview
- Linux Setup
- Windows Setup
- macOS Setup
- Configuration

- 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
- LOAD / INSTALL
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- 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 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

- Information Schema
- Metadata Functions

- DuckDB's SQL Dialect

- Overview
- Indexing
- Friendly SQL
- Keywords and Identifiers
- Order Preservation
- PostgreSQL Compatibility

- Samples

- Configuration

- Overview
- Pragmas
- Secrets Manager

- 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)

- Overview
- HTTP(S) Support
- Hugging Face
- S3 API Support
- Legacy Authentication Scheme for S3 API

- Iceberg
- ICU
- inet
- jemalloc
- MySQL
- PostgreSQL
- Spatial

- Overview
- Function Reference
- R-Tree Indexes
- GDAL Integration

- SQLite
- Substrait
- TPC-DS
- TPC-H
- VSS

- Guides

- Overview
- Data Viewers

- Tableau
- CLI Charting with YouPlot

- Database Integration

- Overview
- MySQL Import
- PostgreSQL Import
- SQLite Import

- 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

- ODBC Guide

- 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

- DBeaver SQL IDE

- SQL Features

- AsOf Join
- Full-Text Search
- query and query_table Functions

- Snippets

- Create Synthetic Data
- Sharing Macros

- Glossary of Terms
- Browse Offline

- Operations Manual

- Overview
- Limits
- Non-Deterministic Behavior
- Embedding DuckDB
- DuckDB's Footprint

- Files Created by DuckDB
- Gitignore for DuckDB
- Reclaiming Space

- Securing DuckDB

- Overview
- Securing Extensions

- Development

- DuckDB Repositories
- Testing

- Overview
- sqllogictest Introduction
- Writing Tests
- Debugging
- Result Verification
- Persistent Testing
- Loops
- Multiple Connections
- Catch

- Profiling
- Release Calendar
- Building

- Overview
- Build Instructions
- Build Configuration
- Building Extensions
- Platforms
- Troubleshooting

- Benchmark Suite

- Internals

- Overview
- Storage Versions and Format
- Execution Format
- Pivot

- Sitemap
- Why DuckDB
- Media
- FAQ
- Code of Conduct
- Live Demo

Collations

Collations provide rules for how text should be sorted or compared in the execution engine. Collations are useful for localization, as the rules for how text should be ordered are different for different languages or for different countries. These orderings are often incompatible with one another. For example, in English the letter `y` comes between `x` and `z`. However, in Lithuanian the letter `y` comes between the `i` and `j`. For that reason, different collations are supported. The user must choose which collation they want to use when performing sorting and comparison operations.

By default, the `BINARY` collation is used. That means that strings are ordered and compared based only on their binary contents. This makes sense for standard ASCII characters (i.e., the letters A-Z and numbers 0-9), but generally does not make much sense for special unicode characters. It is, however, by far the fastest method of performing ordering and comparisons. Hence it is recommended to stick with the `BINARY` collation unless required otherwise.

> Warning Collation support in DuckDB has some known limitations and has several planned improvements.

## Using Collations

In the stand-alone installation of DuckDB three collations are included: `NOCASE`, `NOACCENT` and `NFC`. The `NOCASE` collation compares characters as equal regardless of their casing. The `NOACCENT` collation compares characters as equal regardless of their accents. The `NFC` collation performs NFC-normalized comparisons, see Unicode normalization for more information.

    SELECT 'hello' = 'hElLO';

    false

    SELECT 'hello' COLLATE NOCASE = 'hElLO';

    true

    SELECT 'hello' = 'hëllo';

    false

    SELECT 'hello' COLLATE NOACCENT = 'hëllo';

    true

Collations can be combined by chaining them using the dot operator. Note, however, that not all collations can be combined together. In general, the `NOCASE` collation can be combined with any other collator, but most other collations cannot be combined.

    SELECT 'hello' COLLATE NOCASE = 'hElLÖ';

    false

    SELECT 'hello' COLLATE NOACCENT = 'hElLÖ';

    false

    SELECT 'hello' COLLATE NOCASE.NOACCENT = 'hElLÖ';

    true

## Default Collations

The collations we have seen so far have all been specified per expression. It is also possible to specify a default collator, either on the global database level or on a base table column. The `PRAGMA` `default_collation` can be used to specify the global default collator. This is the collator that will be used if no other one is specified.

    SET default_collation = NOCASE;
    SELECT 'hello' = 'HeLlo';

    true

Collations can also be specified per-column when creating a table. When that column is then used in a comparison, the per-column collation is used to perform that comparison.

    CREATE TABLE names (name VARCHAR COLLATE NOACCENT);
    INSERT INTO names VALUES ('hännes');

    SELECT name
    FROM names
    WHERE name = 'hannes';

    hännes

Be careful here, however, as different collations cannot be combined. This can be problematic when you want to compare columns that have a different collation specified.

    SELECT name
    FROM names
    WHERE name = 'hannes' COLLATE NOCASE;

    ERROR: Cannot combine types with different collation!

    CREATE TABLE other_names (name VARCHAR COLLATE NOCASE);
    INSERT INTO other_names VALUES ('HÄNNES');

    SELECT names.name AS name, other_names.name AS other_name
    FROM names, other_names
    WHERE names.name = other_names.name;

    ERROR: Cannot combine types with different collation!

We need to manually overwrite the collation:

    SELECT names.name AS name, other_names.name AS other_name
    FROM names, other_names
    WHERE names.name COLLATE NOACCENT.NOCASE = other_names.name COLLATE NOACCENT.NOCASE;

name | other_name
--- | ---
hännes | HÄNNES

## ICU Collations

The collations we have seen so far are not region-dependent, and do not follow any specific regional rules. If you wish to follow the rules of a specific region or language, you will need to use one of the ICU collations. For that, you need to load the ICU extension.

If you are using the C++ API, you may find the extension in the `extension/icu` folder of the DuckDB project. Using the C++ API, the extension can be loaded as follows:

    DuckDB db;
    db.LoadExtension<ICUExtension>();

Loading this extension will add a number of language and region specific collations to your database. These can be queried using `PRAGMA collations` command, or by querying the `pragma_collations` function.

    PRAGMA collations;
    SELECT * FROM pragma_collations();

    [af, am, ar, as, az, be, bg, bn, bo, bs, bs, ca, ceb, chr, cs, cy, da, de, de_AT, dsb, dz, ee, el, en, en_US, en_US, eo, es, et, fa, fa_AF, fi, fil, fo, fr, fr_CA, ga, gl, gu, ha, haw, he, he_IL, hi, hr, hsb, hu, hy, id, id_ID, ig, is, it, ja, ka, kk, kl, km, kn, ko, kok, ku, ky, lb, lkt, ln, lo, lt, lv, mk, ml, mn, mr, ms, mt, my, nb, nb_NO, ne, nl, nn, om, or, pa, pa, pa_IN, pl, ps, pt, ro, ru, se, si, sk, sl, smn, sq, sr, sr, sr_BA, sr_ME, sr_RS, sr, sr_BA, sr_RS, sv, sw, ta, te, th, tk, to, tr, ug, uk, ur, uz, vi, wae, wo, xh, yi, yo, zh, zh, zh_CN, zh_SG, zh, zh_HK, zh_MO, zh_TW, zu]

These collations can then be used as the other collations would be used before. They can also be combined with the `NOCASE` collation. For example, to use the German collation rules you could use the following code snippet:

    CREATE TABLE strings (s VARCHAR COLLATE DE);
    INSERT INTO strings VALUES ('Gabel'), ('Göbel'), ('Goethe'), ('Goldmann'), ('Göthe'), ('Götz');
    SELECT * FROM strings ORDER BY s;

    "Gabel", "Göbel", "Goethe", "Goldmann", "Göthe", "Götz"

##### About this page

- Report content issue
- Edit this page on GitHub