Search Shortcut cmd + k | ctrl + k
Search
cmd+k
ctrl+k
- Installation
- Documentation
- Overview
- 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
- 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
- Configuration
- SQL
- Introduction
- Statements
- Overview
- 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
- DROP
- EXPORT/IMPORT DATABASE
- INSERT
- PIVOT
- Profiling
- SELECT
- SET/RESET
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- Query Syntax
- SELECT
- FROM & JOIN
- WHERE
- GROUP BY
- GROUPING SETS
- HAVING
- ORDER BY
- LIMIT
- 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
- Bitstring Functions
- Blob Functions
- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Interval Functions
- Lambda Functions
- Nested Functions
- Numeric Functions
- Pattern Matching
- Regular Expressions
- Text Functions
- Time Functions
- Timestamp Functions
- Timestamp with Time Zone Functions
- Utility Functions
- Aggregate Functions
- Constraints
- Indexes
- Information Schema
- Metadata Functions
- Keywords and Identifiers
- Samples
- Window Functions
- Extensions
- Overview
- Official Extensions
- Working with Extensions
- Versioning of Extensions
- Arrow
- AutoComplete
- AWS
- Azure
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Iceberg
- ICU
- inet
- jemalloc
- JSON
- MySQL
- PostgreSQL
- Spatial
- SQLite
- Substrait
- TPC-DS
- TPC-H
- 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
- 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
If you find that your workload in DuckDB is slow, we recommend performing the following checks. More detailed instructions are linked for each point.
- Do you have enough memory? DuckDB works best if you have 5-10GB memory per CPU core.
- Are you using a fast disk? Network-attached disks can cause the workload to slow down, especially for larger than memory workloads.
- Are you using indexes or constraints (primary key, unique, etc.)? If possible, try disabling them, which boosts load and update performance.
- Are you using the correct types? For example, use
TIMESTAMPto encode datetime values. - Are you reading from Parquet files? If so, do they have row group sizes between 100k and 1M and file sizes between 100MB to 10GB?
- Does the query plan look right? Study it with
EXPLAIN. - Is the workload running in parallel? Use
htopor the operating system’s task manager to observe this. - Is DuckDB using too many threads? Try limiting the amount of threads.
Are you aware of other common issues? If so, please click the Report content issue link below and describe them along with their workarounds.
About this page
Last modified: 2024-04-25