- 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
- 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
The tpch
extension implements the data generator and queries for the TPC-H benchmark.
Installing and Loading
The tpch
extension is shipped by default in some DuckDB builds, otherwise it will be transparently autoloaded on first use.
If you would like to install and load it manually, run:
INSTALL tpch;
LOAD tpch;
Usage
Generating Data
To generate data for scale factor 1, use:
CALL dbgen(sf = 1);
Calling dbgen
does not clean up existing TPC-H tables.
To clean up existing tables, use DROP TABLE
before running dbgen
:
DROP TABLE IF EXISTS customer;
DROP TABLE IF EXISTS lineitem;
DROP TABLE IF EXISTS nation;
DROP TABLE IF EXISTS orders;
DROP TABLE IF EXISTS part;
DROP TABLE IF EXISTS partsupp;
DROP TABLE IF EXISTS region;
DROP TABLE IF EXISTS supplier;
Running a Query
To run a query, e.g., query 4, use:
PRAGMA tpch(4);
o_orderpriority | order_count |
---|---|
1-URGENT | 10594 |
2-HIGH | 10476 |
3-MEDIUM | 10410 |
4-NOT SPECIFIED | 10556 |
5-LOW | 10487 |
Listing Queries
To list all 22 queries, run:
FROM tpch_queries();
This function returns a table with columns query_nr
and query
.
Listing Expected Answers
To produced the expected results for all queries on scale factors 0.01, 0.1, and 1, run:
FROM tpch_answers();
This function returns a table with columns query_nr
, scale_factor
, and answer
.
Generating the Schema
It's possible to generate the schema of TPC-H without any data by setting the scale factor to 0:
CALL dbgen(sf = 0);
Data Generator Parameters
The data generator function dbgen
has the following parameters:
Name | Type | Description |
---|---|---|
catalog |
VARCHAR |
Target catalog |
children |
UINTEGER |
Number of partitions |
overwrite |
BOOLEAN |
(Not used) |
sf |
DOUBLE |
Scale factor |
step |
UINTEGER |
Defines the partition to be generated, indexed from 0 to children - 1. Must be defined when the children arguments is defined |
suffix |
VARCHAR |
Append the suffix to table names |
Resource Usage of the Data Generator
Generating TPC-H data sets for large scale factors takes a significant amount of time. Additionally, when the generation is done in a single step, it requires a large amount of memory. The following table gives an estimate on the resources required to produce DuckDB database files containing the generated TPC-H data set using 128 threads.
Scale factor | Database size | Data generation time | Generator's memory usage |
---|---|---|---|
100 | 26 GB | 17 minutes | 71 GB |
300 | 78 GB | 51 minutes | 211 GB |
1000 | 265 GB | 2h 53 minutes | 647 GB |
3000 | 796 GB | 8h 30 minutes | 1799 GB |
The numbers shown above were achieved by running the dbgen
function in a single step, for example:
CALL dbgen(sf = 300);
If you have a limited amount of memory available, you can run the dbgen
function in steps.
For example, you may generate SF300 in 10 steps:
CALL dbgen(sf = 300, children = 10, step = 0);
CALL dbgen(sf = 300, children = 10, step = 1);
...
CALL dbgen(sf = 300, children = 10, step = 9);
Limitation
The tpch(⟨query_id⟩)
function runs a fixed TPC-H query with pre-defined bind parameters (a.k.a. substitution parameters). It is not possible to change the query parameters using the tpch
extension. To run the queries with the parameters prescribed by the TPC-H benchmark, use a TPC-H framework implementation.