h1 stringclasses 12
values | h2 stringclasses 93
values | h3 stringlengths 0 64 | h5 stringlengths 0 79 | content stringlengths 24 533k | tokens int64 7 158k | content_embeddings_openai_text_embedding_3_small_512 listlengths 512 512 | content_embeddings_potion_base_8M_256 listlengths 256 256 |
|---|---|---|---|---|---|---|---|
Development | Building | Linux: Building the httpfs Extension Fails | **Problem:**
When building the [`httpfs` extension](#docs:extensions:httpfs:overview) on Linux, the build may fail with the following error.
```console
CMake Error at /usr/share/cmake-3.22/Modules/FindPackageHandleStandardArgs.cmake:230 (message):
Could NOT find OpenSSL, try to set the path to OpenSSL root folder in ... | 154 | [
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Development | Benchmark Suite | DuckDB has an extensive benchmark suite.
When making changes that have potential performance implications, it is important to run these benchmarks to detect potential performance regressions. | 32 | [
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Development | Benchmark Suite | Getting Started | To build the benchmark suite, run the following command in the [DuckDB repository](https://github.com/duckdb/duckdb):
```bash
BUILD_BENCHMARK=1 CORE_EXTENSIONS='tpch' make
``` | 47 | [
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Development | Benchmark Suite | Listing Benchmarks | To list all available benchmarks, run:
```bash
build/release/benchmark/benchmark_runner --list
``` | 23 | [
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Development | Benchmark Suite | Running Benchmarks | #### Running a Single Benchmark {#docs:dev:benchmark::running-a-single-benchmark}
To run a single benchmark, issue the following command:
```bash
build/release/benchmark/benchmark_runner benchmark/micro/nulls/no_nulls_addition.benchmark
```
The output will be printed to `stdout` in CSV format, in the following fo... | 368 | [
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-0.031838521... | [
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Development | Benchmark Suite | Running Benchmarks | Running All Benchmarks | Not specifying any argument will run all benchmarks.
```bash
build/release/benchmark/benchmark_runner
``` | 22 | [
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Development | Benchmark Suite | Running Benchmarks | Other Options | The `--info` flag gives you some other information about the benchmark.
```bash
build/release/benchmark/benchmark_runner benchmark/micro/nulls/no_nulls_addition.benchmark --info
```
```text
display_name:NULL Addition (no nulls)
group:micro
subgroup:nulls
```
The `--query` flag will print the query that is run by ... | 112 | [
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Internals | Overview of DuckDB Internals | On this page is a brief description of the internals of the DuckDB engine. | 16 | [
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Internals | Overview of DuckDB Internals | Parser | The parser converts a query string into the following tokens:
* [`SQLStatement`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/parser/sql_statement.hpp)
* [`QueryNode`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/parser/query_node.hpp)
* [`TableRef`](https://github.com/duckdb/duckdb/... | 496 | [
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Internals | Overview of DuckDB Internals | Binder | The binder converts all nodes into their **bound** equivalents. In the binder phase:
* The tables and columns are resolved using the catalog
* Types are resolved
* Aggregate/window functions are extracted
The following conversions happen:
* SQLStatement → [`BoundStatement`](https://github.com/duckdb/duckdb/blob/m... | 179 | [
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Internals | Overview of DuckDB Internals | Logical Planner | The logical planner creates [`LogicalOperator`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/planner/logical_operator.hpp) nodes from the bound statements. In this phase, the actual logical query tree is created. | 50 | [
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Internals | Overview of DuckDB Internals | Optimizer | After the logical planner has created the logical query tree, the optimizers are run over that query tree to create an optimized query plan. The following query optimizers are run:
* **Expression Rewriter**: Simplifies expressions, performs constant folding
* **Filter Pushdown**: Pushes filters down into the query pl... | 202 | [
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Internals | Overview of DuckDB Internals | Column Binding Resolver | The column binding resolver converts logical [`BoundColumnRefExpresion`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/planner/expression/bound_columnref_expression.hpp) nodes that refer to a column of a specific table into [`BoundReferenceExpression`](https://github.com/duckdb/duckdb/blob/main/src/incl... | 105 | [
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-0.... | |
Internals | Overview of DuckDB Internals | Physical Plan Generator | The physical plan generator converts the resulting logical operator tree into a [`PhysicalOperator`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/execution/physical_operator.hpp) tree. | 42 | [
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Internals | Overview of DuckDB Internals | Execution | In the execution phase, the physical operators are executed to produce the query result.
DuckDB uses a push-based vectorized model, where [`DataChunks`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/common/types/data_chunk.hpp) are pushed through the operator tree.
For more information, see the talk [Pu... | 93 | [
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Internals | Storage Versions and Format | Compatibility | #### Backward Compatibility {#docs:internals:storage::backward-compatibility}
_Backward compatibility_ refers to the ability of a newer DuckDB version to read storage files created by an older DuckDB version. Version 0.10 is the first release of DuckDB that supports backward compatibility in the storage format. DuckD... | 350 | [
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-0.028996339067816734,
-0.... | |
Internals | Storage Versions and Format | How to Move Between Storage Formats | When you update DuckDB and open an old database file, you might encounter an error message about incompatible storage formats, pointing to this page.
To move your database(s) to newer format you only need the older and the newer DuckDB executable.
Open your database file with the older DuckDB and run the SQL statemen... | 298 | [
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-0.031838521... | [
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-0.006689759902656078,
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0.07191573083400726,
-0.028996339067816734,
-0.... | |
Internals | Storage Versions and Format | Storage Header | DuckDB files start with a `uint64_t` which contains a checksum for the main header, followed by four magic bytes (` DUCK`), followed by the storage version number in a `uint64_t`.
```bash
hexdump -n 20 -C mydata.db
```
```text
00000000 01 d0 e2 63 9c 13 39 3e 44 55 43 4b 2b 00 00 00 |...c..9>DUCK+...|
00000010 ... | 197 | [
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0.030868330970406532,
0.07191573083400726,
-0.028996339067816734,
-0.... | |
Internals | Storage Versions and Format | Storage Version Table | For changes in each given release, check out the [change log](https://github.com/duckdb/duckdb/releases) on GitHub.
To see the commits that changed each storage version, see the [commit log](https://github.com/duckdb/duckdb/commits/main/src/storage/storage_info.cpp).
<div class="narrow_table"></div>
| Storage versi... | 357 | [
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-0.02972525544464588,
0.050518978387117386,
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-0.031838521... | [
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Internals | Storage Versions and Format | Compression | DuckDB uses [lightweight compression](https://duckdb.org/2022/10/28/lightweight-compression).
Note that compression is only applied to persistent databases and is **not applied to in-memory instances**.
#### Compression Algorithms {#docs:internals:storage::compression-algorithms}
The compression algorithms supporte... | 435 | [
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Internals | Storage Versions and Format | Disk Usage | The disk usage of DuckDB's format depends on a number of factors, including the data type and the data distribution, the compression methods used, etc.
As a rough approximation, loading 100 GB of uncompressed CSV files into a DuckDB database file will require 25 GB of disk space, while loading 100 GB of Parquet files w... | 77 | [
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Internals | Storage Versions and Format | Row Groups | DuckDB's storage format stores the data in _row groups,_ i.e., horizontal partitions of the data.
This concept is equivalent to [Parquet's row groups](https://parquet.apache.org/docs/concepts/).
Several features in DuckDB, including [parallelism](#docs:guides:performance:how_to_tune_workloads) and [compression](https:/... | 99 | [
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Internals | Storage Versions and Format | Troubleshooting | #### Error Message When Opening an Incompatible Database File {#docs:internals:storage::error-message-when-opening-an-incompatible-database-file}
When opening a database file that has been written by a different DuckDB version from the one you are using, the following error message may occur:
```console
Error: unab... | 197 | [
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-0.... | |
Internals | Execution Format | `Vector` is the container format used to store in-memory data during execution.
`DataChunk` is a collection of Vectors, used for instance to represent a column list in a `PhysicalProjection` operator. | 43 | [
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-0.031838521... | [
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Internals | Execution Format | Data Flow | DuckDB uses a vectorized query execution model.
All operators in DuckDB are optimized to work on Vectors of a fixed size.
This fixed size is commonly referred to in the code as `STANDARD_VECTOR_SIZE`.
The default `STANDARD_VECTOR_SIZE` is 2048 tuples. | 60 | [
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-0.031838521... | [
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-0.... | |
Internals | Execution Format | Vector Format | Vectors logically represent arrays that contain data of a single type. DuckDB supports different *vector formats*, which allow the system to store the same logical data with a different *physical representation*. This allows for a more compressed representation, and potentially allows for compressed execution throughou... | 695 | [
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0.0549049973487854,
0.024422157555818558,
0.00978381559252739,
-0.02972525544464588,
0.050518978387117386,
0.0003144664515275508,
-0.01877017319202423,
-0.031838521... | [
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0.030868330970406532,
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Internals | Execution Format | Complex Types | #### String Vectors {#docs:internals:vector::string-vectors}
To efficiently store strings, we make use of our `string_t` class.
```cpp
struct string_t {
union {
struct {
uint32_t length;
char prefix[4];
char *ptr;
} pointer;
struct {
uint32_t length;
char inlined[12];
} inlined;
} value;
};
```
Short strings (` <... | 535 | [
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Internals | Pivot Internals | `PIVOT` | [Pivoting](#docs:sql:statements:pivot) is implemented as a combination of SQL query re-writing and a dedicated `PhysicalPivot` operator for higher performance.
Each `PIVOT` is implemented as set of aggregations into lists and then the dedicated `PhysicalPivot` operator converts those lists into column names and values.... | 648 | [
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Internals | Pivot Internals | `UNPIVOT` | #### Internals {#docs:internals:pivot::internals}
Unpivoting is implemented entirely as rewrites into SQL queries.
Each `UNPIVOT` is implemented as set of `unnest` functions, operating on a list of the column names and a list of the column values.
If dynamically unpivoting, the `COLUMNS` expression is evaluated first... | 533 | [
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Acknowledgments | This document is built with [Pandoc](https://pandoc.org/) using the [Eisvogel template](https://github.com/Wandmalfarbe/pandoc-latex-template). The scripts to build the document are available in the [DuckDB-Web repository](https://github.com/duckdb/duckdb-web/tree/main/single-file-document).
The emojis used in this d... | 160 | [
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