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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 the
system variable OPENSSL_ROOT_DIR (missing: OPENSSL_CRYPTO_LIBRARY
OPENSSL_INCLUDE_DIR)
```
**Solution:**
Install the `libssl-dev` library.
```bash
sudo apt-get install -y libssl-dev
```
Then, build with:
```bash
GEN=ninja CORE_EXTENSIONS="httpfs" make
``` | 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 format:
```text
nameruntiming
benchmark/micro/nulls/no_nulls_addition.benchmark10.121234
benchmark/micro/nulls/no_nulls_addition.benchmark20.121702
benchmark/micro/nulls/no_nulls_addition.benchmark30.122948
benchmark/micro/nulls/no_nulls_addition.benchmark40.122534
benchmark/micro/nulls/no_nulls_addition.benchmark50.124102
```
You can also specify an output file using the `--out` flag. This will write only the timings (delimited by newlines) to that file.
```bash
build/release/benchmark/benchmark_runner benchmark/micro/nulls/no_nulls_addition.benchmark --out=timings.out
```
The output will contain the following:
```text
0.182472
0.185027
0.184163
0.185281
0.182948
```
#### Running Multiple Benchmark Using a Regular Expression {#docs:dev:benchmark::running-multiple-benchmark-using-a-regular-expression}
You can also use a regular expression to specify which benchmarks to run.
Be careful of shell expansion of certain regex characters (e.g., `*` will likely be expanded by your shell, hence this requires proper quoting or escaping).
```bash
build/release/benchmark/benchmark_runner "benchmark/micro/nulls/.*"
``` | 368 | [
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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 the benchmark.
```sql
SELECT min(i + 1) FROM integers;
```
The `--profile` flag will output a query tree. | 112 | [
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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/blob/main/src/include/duckdb/parser/tableref.hpp)
* [`ParsedExpression`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/parser/parsed_expression.hpp)
The parser is not aware of the catalog or any other aspect of the database. It will not throw errors if tables do not exist, and will not resolve **any** types of columns yet. It only transforms a query string into a set of tokens as specified.
#### ParsedExpression {#docs:internals:overview::parsedexpression}
The ParsedExpression represents an expression within a SQL statement. This can be e.g., a reference to a column, an addition operator or a constant value. The type of the ParsedExpression indicates what it represents, e.g., a comparison is represented as a [`ComparisonExpression`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/parser/expression/comparison_expression.hpp).
ParsedExpressions do **not** have types, except for nodes with explicit types such as `CAST` statements. The types for expressions are resolved in the Binder, not in the Parser.
#### TableRef {#docs:internals:overview::tableref}
The TableRef represents any table source. This can be a reference to a base table, but it can also be a join, a table-producing function or a subquery.
#### QueryNode {#docs:internals:overview::querynode}
The QueryNode represents either (1) a `SELECT` statement, or (2) a set operation (i.e. `UNION`, `INTERSECT` or `DIFFERENCE`).
#### SQL Statement {#docs:internals:overview::sql-statement}
The SQLStatement represents a complete SQL statement. The type of the SQL Statement represents what kind of statement it is (e.g., `StatementType::SELECT` represents a `SELECT` statement). A single SQL string can be transformed into multiple SQL statements in case the original query string contains multiple queries. | 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/main/src/include/duckdb/planner/bound_statement.hpp)
* QueryNode → [`BoundQueryNode`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/planner/bound_query_node.hpp)
* TableRef → [`BoundTableRef`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/planner/bound_tableref.hpp)
* ParsedExpression → [`Expression`](https://github.com/duckdb/duckdb/blob/main/src/include/duckdb/planner/expression.hpp) | 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 plan and duplicates filters over equivalency sets. Also prunes subtrees that are guaranteed to be empty (because of filters that statically evaluate to false).
* **Join Order Optimizer**: Reorders joins using dynamic programming. Specifically, the `DPccp` algorithm from the paper [Dynamic Programming Strikes Back](https://15721.courses.cs.cmu.edu/spring2017/papers/14-optimizer1/p539-moerkotte.pdf) is used.
* **Common Sub Expressions**: Extracts common subexpressions from projection and filter nodes to prevent unnecessary duplicate execution.
* **In Clause Rewriter**: Rewrites large static IN clauses to a MARK join or INNER join. | 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/include/duckdb/planner/expression/bound_reference_expression.hpp) nodes that refer to a specific index into the DataChunks that are passed around in the execution engine. | 105 | [
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|
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 [Push-Based Execution in DuckDB](https://www.youtube.com/watch?v=1kDrPgRUuEI). | 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. DuckDB v0.10 can read and operate on files created by the previous DuckDB version – DuckDB v0.9.
For future DuckDB versions, our goal is to ensure that any DuckDB version released **after** can read files created by previous versions, starting from this release. We want to ensure that the file format is fully backward compatible. This allows you to keep data stored in DuckDB files around and guarantees that you will be able to read the files without having to worry about which version the file was written with or having to convert files between versions.
#### Forward Compatibility {#docs:internals:storage::forward-compatibility}
_Forward compatibility_ refers to the ability of an older DuckDB version to read storage files produced by a newer DuckDB version. DuckDB v0.9 is [**partially** forward compatible with DuckDB v0.10](https://duckdb.org/2024/02/13/announcing-duckdb-0100#forward-compatibility). Certain files created by DuckDB v0.10 can be read by DuckDB v0.9.
Forward compatibility is provided on a **best effort** basis. While stability of the storage format is important – there are still many improvements and innovations that we want to make to the storage format in the future. As such, forward compatibility may be (partially) broken on occasion. | 350 | [
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|
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 statement `EXPORT DATABASE 'tmp'`. This allows you to save the whole state of the current database in use inside folder `tmp`.
The content of the `tmp` folder will be overridden, so choose an empty/non yet existing location. Then, start the newer DuckDB and execute `IMPORT DATABASE 'tmp'` (pointing to the previously populated folder) to load the database, which can be then saved to the file you pointed DuckDB to.
A bash one-liner (to be adapted with the file names and executable locations) is:
```bash
/older/version/duckdb mydata.db -c "EXPORT DATABASE 'tmp'" && /newer/duckdb mydata.new.db -c "IMPORT DATABASE 'tmp'"
```
After this, `mydata.db` will remain in the old format, `mydata.new.db` will contain the same data but in a format accessible by the more recent DuckDB version, and the folder `tmp` will hold the same data in a universal format as different files.
Check [`EXPORT` documentation](#docs:sql:statements:export) for more details on the syntax. | 298 | [
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|
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 00 00 00 00 |....|
00000014
```
A simple example of reading the storage version using Python is below.
```python
import struct
pattern = struct.Struct('<8x4sQ')
with open('test/sql/storage_version/storage_version.db', 'rb') as fh:
print(pattern.unpack(fh.read(pattern.size)))
``` | 197 | [
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|
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 version | DuckDB version(s) |
|----------------:|---------------------------------|
| 64 | v0.9.x, v0.10.x, v1.0.0, v1.1.x |
| 51 | v0.8.x |
| 43 | v0.7.x |
| 39 | v0.6.x |
| 38 | v0.5.x |
| 33 | v0.3.3, v0.3.4, v0.4.0 |
| 31 | v0.3.2 |
| 27 | v0.3.1 |
| 25 | v0.3.0 |
| 21 | v0.2.9 |
| 18 | v0.2.8 |
| 17 | v0.2.7 |
| 15 | v0.2.6 |
| 13 | v0.2.5 |
| 11 | v0.2.4 |
| 6 | v0.2.3 |
| 4 | v0.2.2 |
| 1 | v0.2.1 and prior | | 357 | [
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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 supported by DuckDB include the following:
* [Constant Encoding](https://duckdb.org/2022/10/28/lightweight-compression#constant-encoding)
* [Run-Length Encoding (RLE)](https://duckdb.org/2022/10/28/lightweight-compression#run-length-encoding-rle)
* [Bit Packing](https://duckdb.org/2022/10/28/lightweight-compression#bit-packing)
* [Frame of Reference (FOR)](https://duckdb.org/2022/10/28/lightweight-compression#frame-of-reference)
* [Dictionary Encoding](https://duckdb.org/2022/10/28/lightweight-compression#dictionary-encoding)
* [Fast Static Symbol Table (FSST)](https://duckdb.org/2022/10/28/lightweight-compression#fsst) – [VLDB 2020 paper](https://www.vldb.org/pvldb/vol13/p2649-boncz.pdf)
* [Adaptive Lossless Floating-Point Compression (ALP)](https://duckdb.org/2024/02/13/announcing-duckdb-0100#adaptive-lossless-floating-point-compression-alp) – [SIGMOD 2024 paper](https://ir.cwi.nl/pub/33334/33334.pdf)
* [Chimp](https://duckdb.org/2022/10/28/lightweight-compression#chimp--patas) – [VLDB 2022 paper](https://www.vldb.org/pvldb/vol15/p3058-liakos.pdf)
* [Patas](https://duckdb.org/2022/11/14/announcing-duckdb-060#compression-improvements) | 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 will require 120 GB of disk space. | 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://duckdb.org/2022/10/28/lightweight-compression) are based on row groups. | 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: unable to open database "...": Serialization Error: Failed to deserialize: ...
```
The message implies that the database file was created with a newer DuckDB version and uses features that are backward incompatible with the DuckDB version used to read the file.
There are two potential workarounds:
1. Update your DuckDB version to the latest stable version.
2. Open the database with the latest version of DuckDB, export it to a standard format (e.g., Parquet), then import it using to any version of DuckDB. See the [`EXPORT/IMPORT DATABASE` statements](#docs:sql:statements:export) for details. | 197 | [
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|
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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||
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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|
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 throughout the system. Below the list of supported vector formats is shown.
#### Flat Vectors {#docs:internals:vector::flat-vectors}
Flat vectors are physically stored as a contiguous array, this is the standard uncompressed vector format.
For flat vectors the logical and physical representations are identical.
<img src="/images/internals/flat.png" alt="Flat Vector example" style="max-width:40%;width:40%;height:auto;margin:auto"/>
#### Constant Vectors {#docs:internals:vector::constant-vectors}
Constant vectors are physically stored as a single constant value.
<img src="/images/internals/constant.png" alt="Constant Vector example" style="max-width:40%;width:40%;height:auto;margin:auto"/>
Constant vectors are useful when data elements are repeated – for example, when representing the result of a constant expression in a function call, the constant vector allows us to only store the value once.
```sql
SELECT lst || 'duckdb'
FROM range(1000) tbl(lst);
```
Since `duckdb` is a string literal, the value of the literal is the same for every row. In a flat vector, we would have to duplicate the literal 'duckdb' once for every row. The constant vector allows us to only store the literal once.
Constant vectors are also emitted by the storage when decompressing from constant compression.
#### Dictionary Vectors {#docs:internals:vector::dictionary-vectors}
Dictionary vectors are physically stored as a child vector, and a selection vector that contains indexes into the child vector.
<img src="/images/internals/dictionary.png" alt="Dictionary Vector example" style="max-width:40%;width:40%;height:auto;margin:auto"/>
Dictionary vectors are emitted by the storage when decompressing from dictionary
Just like constant vectors, dictionary vectors are also emitted by the storage.
When deserializing a dictionary compressed column segment, we store this in a dictionary vector so we can keep the data compressed during query execution.
#### Sequence Vectors {#docs:internals:vector::sequence-vectors}
Sequence vectors are physically stored as an offset and an increment value.
<img src="/images/internals/sequence.png" alt="Sequence Vector example" style="max-width:40%;width:40%;height:auto;margin:auto"/>
Sequence vectors are useful for efficiently storing incremental sequences. They are generally emitted for row identifiers.
#### Unified Vector Format {#docs:internals:vector::unified-vector-format}
These properties of the different vector formats are great for optimization purposes, for example you can imagine the scenario where all the parameters to a function are constant, we can just compute the result once and emit a constant vector.
But writing specialized code for every combination of vector types for every function is unfeasible due to the combinatorial explosion of possibilities.
Instead of doing this, whenever you want to generically use a vector regardless of the type, the UnifiedVectorFormat can be used.
This format essentially acts as a generic view over the contents of the Vector. Every type of Vector can convert to this format. | 695 | [
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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 (` <= 12 bytes`) are inlined into the structure, while larger strings are stored with a pointer to the data in the auxiliary string buffer. The length is used throughout the functions to avoid having to call `strlen` and having to continuously check for null-pointers. The prefix is used for comparisons as an early out (when the prefix does not match, we know the strings are not equal and don't need to chase any pointers).
#### List Vectors {#docs:internals:vector::list-vectors}
List vectors are stored as a series of *list entries* together with a child Vector. The child vector contains the *values* that are present in the list, and the list entries specify how each individual list is constructed.
```cpp
struct list_entry_t {
idx_t offset;
idx_t length;
};
```
The offset refers to the start row in the child Vector, the length keeps track of the size of the list of this row.
List vectors can be stored recursively. For nested list vectors, the child of a list vector is again a list vector.
For example, consider this mock representation of a Vector of type `BIGINT[][]`:
```json
{
"type": "list",
"data": "list_entry_t",
"child": {
"type": "list",
"data": "list_entry_t",
"child": {
"type": "bigint",
"data": "int64_t"
}
}
}
```
#### Struct Vectors {#docs:internals:vector::struct-vectors}
Struct vectors store a list of child vectors. The number and types of the child vectors is defined by the schema of the struct.
#### Map Vectors {#docs:internals:vector::map-vectors}
Internally map vectors are stored as a `LIST[STRUCT(key KEY_TYPE, value VALUE_TYPE)]`.
#### Union Vectors {#docs:internals:vector::union-vectors}
Internally `UNION` utilizes the same structure as a `STRUCT`.
The first “child” is always occupied by the Tag Vector of the `UNION`, which records for each row which of the `UNION`'s types apply to that row. | 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.
Additional pre-processing steps are required if the columns to be created when pivoting are detected dynamically (which occurs when the `IN` clause is not in use).
DuckDB, like most SQL engines, requires that all column names and types be known at the start of a query.
In order to automatically detect the columns that should be created as a result of a `PIVOT` statement, it must be translated into multiple queries.
[`ENUM` types](#docs:sql:data_types:enum) are used to find the distinct values that should become columns.
Each `ENUM` is then injected into one of the `PIVOT` statement's `IN` clauses.
After the `IN` clauses have been populated with `ENUM`s, the query is re-written again into a set of aggregations into lists.
For example:
```sql
PIVOT cities
ON year
USING sum(population);
```
is initially translated into:
```sql
CREATE TEMPORARY TYPE __pivot_enum_0_0 AS ENUM (
SELECT DISTINCT
year::VARCHAR
FROM cities
ORDER BY
year
);
PIVOT cities
ON year IN __pivot_enum_0_0
USING sum(population);
```
and finally translated into:
```sql
SELECT country, name, list(year), list(population_sum)
FROM (
SELECT country, name, year, sum(population) AS population_sum
FROM cities
GROUP BY ALL
)
GROUP BY ALL;
```
This produces the result:
<div class="narrow_table"></div>
| country | name | list("year") | list(population_sum) |
|---------|---------------|--------------------|----------------------|
| NL | Amsterdam | [2000, 2010, 2020] | [1005, 1065, 1158] |
| US | Seattle | [2000, 2010, 2020] | [564, 608, 738] |
| US | New York City | [2000, 2010, 2020] | [8015, 8175, 8772] |
The `PhysicalPivot` operator converts those lists into column names and values to return this result:
<div class="narrow_table"></div>
| country | name | 2000 | 2010 | 2020 |
|---------|---------------|-----:|-----:|-----:|
| NL | Amsterdam | 1005 | 1065 | 1158 |
| US | Seattle | 564 | 608 | 738 |
| US | New York City | 8015 | 8175 | 8772 | | 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 to calculate the column list.
For example:
```sql
UNPIVOT monthly_sales
ON jan, feb, mar, apr, may, jun
INTO
NAME month
VALUE sales;
```
is translated into:
```sql
SELECT
empid,
dept,
unnest(['jan', 'feb', 'mar', 'apr', 'may', 'jun']) AS month,
unnest(["jan", "feb", "mar", "apr", "may", "jun"]) AS sales
FROM monthly_sales;
```
Note the single quotes to build a list of text strings to populate `month`, and the double quotes to pull the column values for use in `sales`.
This produces the same result as the initial example:
<div class="narrow_table"></div>
| empid | dept | month | sales |
|------:|-------------|-------|------:|
| 1 | electronics | jan | 1 |
| 1 | electronics | feb | 2 |
| 1 | electronics | mar | 3 |
| 1 | electronics | apr | 4 |
| 1 | electronics | may | 5 |
| 1 | electronics | jun | 6 |
| 2 | clothes | jan | 10 |
| 2 | clothes | feb | 20 |
| 2 | clothes | mar | 30 |
| 2 | clothes | apr | 40 |
| 2 | clothes | may | 50 |
| 2 | clothes | jun | 60 |
| 3 | cars | jan | 100 |
| 3 | cars | feb | 200 |
| 3 | cars | mar | 300 |
| 3 | cars | apr | 400 |
| 3 | cars | may | 500 |
| 3 | cars | jun | 600 | | 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 document are provided by [Twemoji](https://twemoji.twitter.com/) under the [CC-BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).
The syntax highlighter uses the [Bluloco Light theme](https://github.com/uloco/theme-bluloco-light) by Umut Topuzoğlu. | 160 | [
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