- Installation
- Documentation
- Getting Started
- Connect
- Data Import
- Overview
- CSV Files
- JSON Files
- 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
- Go
- Java
- Julia
- 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 / 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 / IMPORT DATABASE
- INSERT
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- LOAD / INSTALL
- Query Syntax
- SELECT
- FROM & 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
- JSON
- 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 & 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
- Import
- Schema
- Indexing
- Environment
- 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
- 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
- Overview
- Build Instructions
- Build Configuration
- Building Extensions
- Supported Platforms
- Troubleshooting
- Benchmark Suite
- Internals
- Sitemap
- Why DuckDB
- Media
- FAQ
- Code of Conduct
- Live Demo
The DROP
statement removes a catalog entry added previously with the CREATE
command.
Examples
Delete the table with the name tbl
:
DROP TABLE tbl;
Drop the view with the name v1
; do not throw an error if the view does not exist:
DROP VIEW IF EXISTS v1;
Drop function fn
:
DROP FUNCTION fn;
Drop index idx
:
DROP INDEX idx;
Drop schema sch
:
DROP SCHEMA sch;
Drop sequence seq
:
DROP SEQUENCE seq;
Drop macro mcr
:
DROP MACRO mcr;
Drop macro table mt
:
DROP MACRO TABLE mt;
Drop type typ
:
DROP TYPE typ;
Syntax
Dependencies of Dropped Objects
DuckDB performs limited dependency tracking for some object types.
By default or if the RESTRICT
clause is provided, the entry will not be dropped if there are any other objects that depend on it.
If the CASCADE
clause is provided then all the objects that are dependent on the object will be dropped as well.
CREATE SCHEMA myschema;
CREATE TABLE myschema.t1 (i INTEGER);
DROP SCHEMA myschema;
Dependency Error: Cannot drop entry `myschema` because there are entries that depend on it.
Use DROP...CASCADE to drop all dependents.
The CASCADE
modifier drops both myschema and myschema.t1
:
CREATE SCHEMA myschema;
CREATE TABLE myschema.t1 (i INTEGER);
DROP SCHEMA myschema CASCADE;
The following dependencies are tracked and thus will raise an error if the user tries to drop the depending object without the CASCADE
modifier.
Depending object type | Dependant object type |
---|---|
SCHEMA |
FUNCTION |
SCHEMA |
INDEX |
SCHEMA |
MACRO TABLE |
SCHEMA |
MACRO |
SCHEMA |
SCHEMA |
SCHEMA |
SEQUENCE |
SCHEMA |
TABLE |
SCHEMA |
TYPE |
SCHEMA |
VIEW |
TABLE |
INDEX |
Limitations
Dependencies on Views
Currently, dependencies are not tracked for views. For example, if a view is created that references a table and the table is dropped, then the view will be in an invalid state:
CREATE TABLE tbl (i INTEGER);
CREATE VIEW v AS
SELECT i FROM tbl;
DROP TABLE tbl RESTRICT;
SELECT * FROM v;
Catalog Error: Table with name tbl does not exist!
Limitations on Reclaiming Disk Space
Running DROP TABLE
should free the memory used by the table, but not always disk space.
Even if disk space does not decrease, the free blocks will be marked as free
.
For example, if we have a 2 GB file and we drop a 1 GB table, the file might still be 2 GB, but it should have 1 GB of free blocks in it.
To check this, use the following PRAGMA
and check the number of free_blocks
in the output:
PRAGMA database_size;
For instruction on reclaiming space after dropping a table, refer to the “Reclaiming space” page.