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Operations Manual | Non-Deterministic Behavior | Several operators in DuckDB exhibit non-deterministic behavior.
Most notably, SQL uses set semantics, which allows results to be returned in a different order.
DuckDB exploits this to improve performance, particularly when performing multi-threaded query execution.
Other factors, such as using different compilers, operating systems, and hardware architectures, can also cause changes in ordering.
This page documents the cases where non-determinism is an _expected behavior_.
If you would like to make your queries determinisic, see the [“Working Around Non-Determinism” section](#::working-around-non-determinism). | 123 | [
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||
Operations Manual | Non-Deterministic Behavior | Set Semantics | One of the most common sources of non-determinism is the set semantics used by SQL.
E.g., if you run the following query repeatedly, you may get two different results:
```sql
SELECT *
FROM (
SELECT 'A' AS x
UNION
SELECT 'B' AS x
);
```
Both results `A`, `B` and `B`, `A` are correct. | 83 | [
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|
Operations Manual | Non-Deterministic Behavior | Different Results on Different Platforms: `array_distinct` | The `array_distinct` function may return results [in a different order on different platforms](https://github.com/duckdb/duckdb/issues/13746):
```sql
SELECT array_distinct(['A', 'A', 'B', NULL, NULL]) AS arr;
```
For this query, both `[A, B]` and `[B, A]` are valid results. | 82 | [
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|
Operations Manual | Non-Deterministic Behavior | Floating-Point Aggregate Operations with Multi-Threading | Floating-point inaccuracies may produce different results when run in a multi-threaded configurations:
For example, [`stddev` and `corr` may produce non-deterministic results](https://github.com/duckdb/duckdb/issues/13763):
```sql
CREATE TABLE tbl AS
SELECT 'ABCDEFG'[floor(random() * 7 + 1)::INT] AS s, 3.7 AS x, i AS y
FROM range(1, 1_000_000) r(i);
SELECT s, stddev(x) AS standard_deviation, corr(x, y) AS correlation FROM tbl
GROUP BY s
ORDER BY s;
```
The expected standard deviations and correlations from this query are 0 for all values of `s`.
However, when executed on multiple threads, the query may return small numbers (` 0 <= z < 10e-16`) due to floating-point inaccuracies. | 188 | [
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|
Operations Manual | Non-Deterministic Behavior | Working Around Non-Determinism | For the majority of use cases, non-determinism is not causing any issues.
However, there are some cases where deterministic results are desirable.
In these cases, try the following workarounds:
1. Limit the number of threads to prevent non-determinism introduced by multi-threading.
```sql
SET threads = 1;
```
2. Enforce ordering. For example, you can use the [`ORDER BY ALL` clause](#docs:sql:query_syntax:orderby::order-by-all):
```sql
SELECT *
FROM (
SELECT 'A' AS x
UNION
SELECT 'B' AS x
)
ORDER BY ALL;
```
You can also sort lists using [`list_sort`](#docs:sql:functions:list::list_sortlist):
```sql
SELECT list_sort(array_distinct(['A', 'A', 'B', NULL, NULL])) AS i
ORDER BY i;
```
It's also possible to introduce a [deterministic shuffling](https://duckdb.org/2024/08/19/duckdb-tricks-part-1#shuffling-data). | 231 | [
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|
Operations Manual | Embedding DuckDB | CLI Client | The [Command Line Interface (CLI) client](#docs:api:cli:overview) is intended for interactive use cases and not for embedding.
As a result, it has more features that could be abused by a malicious actor.
For example, the CLI client has the `.sh` feature that allows executing arbitrary shell commands.
This feature is only present in the CLI client and not in any other DuckDB clients.
```sql
.sh ls
```
> **Tip. ** Calling DuckDB's CLI client via shell commands is **not recommended** for embedding DuckDB. It is recommended to use one of the client libraries, e.g., [Python](#docs:api:python:overview), [R](#docs:api:r), [Java](#docs:api:java), etc. | 165 | [
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|
Operations Manual | DuckDB's Footprint | Global Files and Directories | DuckDB creates the following global files and directories in the user's home directory (denoted with `~`):
| Location | Description | Shared between versions | Shared between clients |
|-------|-------------------|--|--|
| `~/.duckdbrc` | The content of this file is executed when starting the [DuckDB CLI client](#docs:api:cli:overview). The commands can be both [dot command](#docs:api:cli:dot_commands) and SQL statements. The naming of this file follows the `~/.bashrc` and `~/.zshrc` “run commands” files. | Yes | Only used by CLI |
| `~/.duckdb_history` | History file, similar to `~/.bash_history` and `~/.zsh_history`. Used by the [DuckDB CLI client](#docs:api:cli:overview). | Yes | Only used by CLI |
| `~/.duckdb/extensions` | Binaries of installed [extensions](#docs:extensions:overview). | No | Yes |
| `~/.duckdb/stored_secrets` | [Persistent secrets](#docs:configuration:secrets_manager::persistent-secrets) created by the [Secrets manager](#docs:configuration:secrets_manager). | Yes | Yes | | 273 | [
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|
Operations Manual | DuckDB's Footprint | Local Files and Directories | DuckDB creates the following files and directories in the working directory (for in-memory connections) or relative to the database file (for persistent connections):
| Name | Description | Example |
|-------|-------------------|---|
| `⟨database_filename⟩` | Database file. Only created in on-disk mode. The file can have any extension with typical extensions being `.duckdb`, `.db`, and `.ddb`. | `weather.duckdb` |
| `.tmp/` | Temporary directory. Only created in in-memory mode. | `.tmp/` |
| `⟨database_filename⟩.tmp/` | Temporary directory. Only created in on-disk mode. | `weather.tmp/` |
| `⟨database_filename⟩.wal` | [Write-ahead log](https://en.wikipedia.org/wiki/Write-ahead_logging) file. If DuckDB exits normally, the WAL file is deleted upon exit. If DuckDB crashes, the WAL file is required to recover data. | `weather.wal` |
If you are working in a Git repository and would like to disable tracking these files by Git,
see the instructions on using [`.gitignore` for DuckDB](#docs:operations_manual:footprint_of_duckdb:gitignore_for_duckdb). | 265 | [
0.11890102177858353,
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|
Operations Manual | DuckDB's Footprint | Gitignore for DuckDB | If you work in a Git repository, you may want to configure your [Gitignore](https://git-scm.com/docs/gitignore) to disable tracking [files created by DuckDB](#docs:operations_manual:footprint_of_duckdb:files_created_by_duckdb).
These potentially include the DuckDB database, write ahead log, temporary files. | 73 | [
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|
Operations Manual | DuckDB's Footprint | Sample Gitignore Files | In the following, we present sample Gitignore configuration snippets for DuckDB.
#### Ignore Temporary Files but Keep Database {#docs:operations_manual:footprint_of_duckdb:gitignore_for_duckdb::ignore-temporary-files-but-keep-database}
This configuration is useful if you would like to keep the database file in the version control system:
```text
*.wal
*.tmp/
```
#### Ignore Database and Temporary Files {#docs:operations_manual:footprint_of_duckdb:gitignore_for_duckdb::ignore-database-and-temporary-files}
If you would like to ignore both the database and the temporary files, extend the Gitignore file to include the database file.
The exact Gitignore configuration to achieve this depends on the extension you use for your DuckDB databases (` .duckdb`, `.db`, `.ddb`, etc.).
For example, if your DuckDB files use the `.duckdb` extension, add the following lines to your `.gitignore` file:
```text
*.duckdb*
*.wal
*.tmp/
``` | 223 | [
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Operations Manual | DuckDB's Footprint | Reclaiming Space | DuckDB uses a single-file format, which has some inherent limitations w.r.t. reclaiming disk space. | 23 | [
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|
Operations Manual | DuckDB's Footprint | `CHECKPOINT` | To reclaim space after deleting rows, use the [`CHECKPOINT` statement](#docs:sql:statements:checkpoint). | 24 | [
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|
Operations Manual | DuckDB's Footprint | `VACUUM` | The [`VACUUM` statement](#docs:sql:statements:vacuum) does _not_ trigger vacuuming deletes and hence does not reclaim space. | 34 | [
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|
Operations Manual | DuckDB's Footprint | Compacting a Database by Copying | To compact the database, you can create a fresh copy of the database using the [`COPY FROM DATABASE` statement](#docs:sql:statements:copy::copy-from-database--to). In the following example, we first connect to the original database `db1`, then the new (empty) database `db2`. Then, we copy the content of `db1` to `db2`.
```sql
ATTACH 'db1.db' AS db1;
ATTACH 'db2.db' AS db2;
COPY FROM DATABASE db1 TO db2;
``` | 118 | [
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|
Operations Manual | Securing DuckDB | Securing DuckDB | DuckDB is quite powerful, which can be problematic, especially if untrusted SQL queries are run, e.g., from public-facing user inputs.
This page lists some options to restrict the potential fallout from malicious SQL queries.
The approach to securing DuckDB varies depending on your use case, environment, and potential attack models.
Therefore, consider the security-related configuration options carefully, especially when working with confidential data sets.
If you plan to embed DuckDB in your application, please consult the [“Embedding DuckDB”](#docs:operations_manual:embedding_duckdb) page. | 120 | [
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|
Operations Manual | Securing DuckDB | Reporting Vulnerabilities | If you discover a potential vulnerability, please [report it confidentially via GitHub](https://github.com/duckdb/duckdb/security/advisories/new). | 32 | [
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|
Operations Manual | Securing DuckDB | Disabling File Access | DuckDB can list directories and read arbitrary files via its CSV parser’s [`read_csv` function](#docs:data:csv:overview) or read text via the [`read_text` function](#docs:sql:functions:char::read_textsource). For example:
```sql
SELECT *
FROM read_csv('/etc/passwd', sep = ':');
```
This can be disabled either by disabling external access altogether (` enable_external_access`) or disabling individual file systems. For example:
```sql
SET disabled_filesystems = 'LocalFileSystem';
``` | 114 | [
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|
Operations Manual | Securing DuckDB | Secrets | [Secrets](#docs:configuration:secrets_manager) are used to manage credentials to log into third party services like AWS or Azure. DuckDB can show a list of secrets using the `duckdb_secrets()` table function. This will redact any sensitive information such as security keys by default. The `allow_unredacted_secrets` option can be set to show all information contained within a security key. It is recommended not to turn on this option if you are running untrusted SQL input.
Queries can access the secrets defined in the Secrets Manager. For example, if there is a secret defined to authenticate with a user, who has write privileges to a given AWS S3 bucket, queries may write to that bucket. This is applicable for both persistent and temporary secrets.
[Persistent secrets](#docs:configuration:secrets_manager::persistent-secrets) are stored in unencrypted binary format on the disk. These have the same permissions as SSH keys, `600`, i.e., only user who is running the DuckDB (parent) process can read and write them. | 221 | [
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|
Operations Manual | Securing DuckDB | Locking Configurations | Security-related configuration settings generally lock themselves for safety reasons. For example, while we can disable [community extensions](#docs:extensions:community_extensions) using the `SET allow_community_extensions = false`, we cannot re-enable them again after the fact without restarting the database. Trying to do so will result in an error:
```console
Invalid Input Error: Cannot upgrade allow_community_extensions setting while database is running
```
This prevents untrusted SQL input from re-enabling settings that were explicitly disabled for security reasons.
Nevertheless, many configuration settings do not disable themselves, such as the resource constraints. If you allow users to run SQL statements unrestricted on your own hardware, it is recommended that you lock the configuration after your own configuration has finished using the following command:
```sql
SET lock_configuration = true;
```
This prevents any configuration settings from being modified from that point onwards. | 179 | [
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|
Operations Manual | Securing DuckDB | Constrain Resource Usage | DuckDB can use quite a lot of CPU, RAM, and disk space. To avoid denial of service attacks, these resources can be limited.
The number of CPU threads that DuckDB can use can be set using, for example:
```sql
SET threads = 4;
```
Where 4 is the number of allowed threads.
The maximum amount of memory (RAM) can also be limited, for example:
```sql
SET memory_limit = '4GB';
```
The size of the temporary file directory can be limited with:
```sql
SET max_temp_directory_size = '4GB';
``` | 129 | [
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|
Operations Manual | Securing DuckDB | Extensions | DuckDB has a powerful extension mechanism, which have the same privileges as the user running DuckDB's (parent) process.
This introduces security considerations. Therefore, we recommend reviewing the configuration options for [securing extensions](#docs:operations_manual:securing_duckdb:securing_extensions). | 61 | [
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|
Operations Manual | Securing DuckDB | Generic Solutions | Securing DuckDB can also be supported via proven means, for example:
* Scoping user privileges via [`chroot`](https://en.wikipedia.org/wiki/Chroot), relying on the operating system
* Containerization, e.g., Docker and Podman
* Running DuckDB in WebAssembly | 61 | [
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|
Operations Manual | Securing DuckDB | Securing Extensions | DuckDB has a powerful extension mechanism, which have the same privileges as the user running DuckDB's (parent) process.
This introduces security considerations. Therefore, we recommend reviewing the configuration options listed on this page and setting them according to your attack models. | 52 | [
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|
Operations Manual | Securing DuckDB | DuckDB Signature Checks | DuckDB extensions are checked on every load using the signature of the binaries.
There are currently three categories of extensions:
* Signed with a `core` key. Only extensions vetted by the core DuckDB team are signed with these keys.
* Signed with a `community` key. These are open-source extensions distributed via the [DuckDB Community Extensions repository](#community_extensions:index).
* Unsigned. | 83 | [
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|
Operations Manual | Securing DuckDB | Overview of Security Levels for Extensions | DuckDB offers the following security levels for extensions.
| Usable extensions | Description | Configuration |
|-----|---|---|
| core | Extensions can only be installed from the `core` repository. | `SET allow_community_extensions = false` |
| core and community | Extensions can only be installed from the `core` and `community` repositories. | This is the default security level. |
| any extension incl. unsigned | Any extensions can be installed. | `SET allow_unsigned_extensions = true` |
Security-related configuration settings [lock themselves](#docs:operations_manual:securing_duckdb:overview::locking-configurations), i.e., it is only possible to restrict capabilities in the current process.
For example, attempting the following configuration changes will result in an error:
```sql
SET allow_community_extensions = false;
SET allow_community_extensions = true;
```
```console
Invalid Input Error: Cannot upgrade allow_community_extensions setting while database is running
``` | 202 | [
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|
Operations Manual | Securing DuckDB | Community Extensions | DuckDB has a [Community Extensions repository](#docs:extensions:community_extensions), which allows convenient installation of third-party extensions.
Community extension repositories like pip or npm are essentially enabling remote code execution by design. This is less dramatic than it sounds. For better or worse, we are quite used to piping random scripts from the web into our shells, and routinely install a staggering amount of transitive dependencies without thinking twice. Some repositories like CRAN enforce a human inspection at some point, but that’s no guarantee for anything either.
We’ve studied several different approaches to community extension repositories and have picked what we think is a sensible approach: we do not attempt to review the submissions, but require that the *source code of extensions is available*. We do take over the complete build, sign and distribution process. Note that this is a step up from pip and npm that allow uploading arbitrary binaries but a step down from reviewing everything manually. We allow users to [report malicious extensions](https://github.com/duckdb/community-extensions/security/advisories/new) and show adoption statistics like GitHub stars and download count. Because we manage the repository, we can remove problematic extensions from distribution quickly.
Despite this, installing and loading DuckDB extensions from the community extension repository will execute code written by third party developers, and therefore *can* be dangerous. A malicious developer could create and register a harmless-looking DuckDB extension that steals your crypto coins. If you’re running a web service that executes untrusted SQL from users with DuckDB, it is probably a good idea to disable community extension installation and loading entirely. This can be done like so:
```sql
SET allow_community_extensions = false;
``` | 342 | [
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|
Operations Manual | Securing DuckDB | Disabling Autoinstalling and Autoloading Known Extensions | By default, DuckDB automatically installs and loads known extensions.
To disable autoinstalling known extensions, run:
```sql
SET autoinstall_known_extensions = false;
```
To disable autoloading known extensions, run:
```sql
SET autoload_known_extensions = false;
```
To lock this configuration, use the [`lock_configuration` option](#docs:operations_manual:securing_duckdb:overview::locking-configurations):
```sql
SET lock_configuration = true;
``` | 102 | [
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|
Operations Manual | Securing DuckDB | Always Require Signed Extensions | By default, DuckDB requires extensions to be either signed as core extensions (created by the DuckDB developers) or community extensions (created by third-party developers but distributed by the DuckDB developers). The `allow_unsigned_extensions` setting can be enabled on start-up to allow running extensions that are not signed at all. While useful for extension development, enabling this setting will allow DuckDB to load any extensions, which means more care must be taken to ensure malicious extensions are not loaded. | 96 | [
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||
Development | DuckDB Repositories | Main Repositories | * [`duckdb`](https://github.com/duckdb/duckdb): core DuckDB project
* [`duckdb-wasm`](https://github.com/duckdb/duckdb-wasm): WebAssembly version of DuckDB
* [`duckdb-web`](https://github.com/duckdb/duckdb-web): documentation and blog | 69 | [
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|
Development | DuckDB Repositories | Clients | * [`duckdb-java`](https://github.com/duckdb/duckdb-java): Java (JDBC) client
* [`duckdb-node`](https://github.com/duckdb/duckdb-node): Node.js client
* [`duckdb-node-neo`](https://github.com/duckdb/duckdb-node-neo): Node.js client, second iteration (currently experimental)
* [`duckdb-odbc`](https://github.com/duckdb/duckdb-odbc): ODBC client
* [`duckdb-r`](https://github.com/duckdb/duckdb-r): R client
* [`duckdb-rs`](https://github.com/duckdb/duckdb-rs): Rust client
* [`duckdb-swift`](https://github.com/duckdb/duckdb-swift): Swift client
* [`duckplyr`](https://github.com/tidyverse/duckplyr): a drop-in replacement for dplyr in R
* [`go-duckdb`](https://github.com/marcboeker/go-duckdb): Go client | 223 | [
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|
Development | DuckDB Repositories | Connectors | * [`dbt-duckdb`](https://github.com/duckdb/dbt-duckdb): dbt
* [`duckdb_mysql`](https://github.com/duckdb/duckdb_mysql): MySQL connector
* [`pg_duckdb`](https://github.com/duckdb/pg_duckdb): official PostgreSQL extension for DuckDB (run DuckDB in PostgreSQL)
* [`postgres_scanner`](https://github.com/duckdb/postgres_scanner): PostgreSQL connector (connect to PostgreSQL from DuckDB)
* [`sqlite_scanner`](https://github.com/duckdb/sqlite_scanner): SQLite connector | 125 | [
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|
Development | DuckDB Repositories | Extensions | * Core extension repositories are linked in the [Official Extensions page](#docs:extensions:core_extensions)
* Community extensions are built in the [Community Extensions repository](#community_extensions:index) | 38 | [
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|
Development | Testing | How is DuckDB Tested? | Testing is vital to make sure that DuckDB works properly and keeps working properly. For that reason, we put a large emphasis on thorough and frequent testing:
* We run a batch of small tests on every commit using [GitHub Actions](https://github.com/duckdb/duckdb/actions), and run a more exhaustive batch of tests on pull requests and commits in the master branch.
* We use a [fuzzer](https://github.com/duckdb/duckdb-fuzzer), which automatically reports of issues found through fuzzing DuckDB.
* We use [SQLsmith](#docs:extensions:sqlsmith) for generating random queries. | 130 | [
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|
Development | Testing | sqllogictest Introduction | For testing plain SQL, we use an extended version of the SQL logic test suite, adopted from [SQLite](https://www.sqlite.org/sqllogictest/doc/trunk/about.wiki). Every test is a single self-contained file located in the `test/sql` directory.
To run tests located outside of the default `test` directory, specify `--test-dir <root_directory>` and make sure provided test file paths are relative to that root directory.
The test describes a series of SQL statements, together with either the expected result, a `statement ok` indicator, or a `statement error` indicator. An example of a test file is shown below:
```sql
# name: test/sql/projection/test_simple_projection.test
# group [projection]
# enable query verification
statement ok
PRAGMA enable_verification
# create table
statement ok
CREATE TABLE a (i INTEGER, j INTEGER);
# insertion: 1 affected row
statement ok
INSERT INTO a VALUES (42, 84);
query II
SELECT * FROM a;
----
4284
```
In this example, three statements are executed. The first statements are expected to succeed (prefixed by `statement ok`). The third statement is expected to return a single row with two columns (indicated by `query II`). The values of the row are expected to be `42` and `84` (separated by a tab character). For more information on query result verification, see the [result verification section](#docs:dev:sqllogictest:result_verification).
The top of every file should contain a comment describing the name and group of the test. The name of the test is always the relative file path of the file. The group is the folder that the file is in. The name and group of the test are relevant because they can be used to execute *only* that test in the unittest group. For example, if we wanted to execute *only* the above test, we would run the command `unittest test/sql/projection/test_simple_projection.test`. If we wanted to run all tests in a specific directory, we would run the command `unittest "[projection]"`.
Any tests that are placed in the `test` directory are automatically added to the test suite. Note that the extension of the test is significant. The sqllogictests should either use the `.test` extension, or the `.test_slow` extension. The `.test_slow` extension indicates that the test takes a while to run, and will only be run when all tests are explicitly run using `unittest *`. Tests with the extension `.test` will be included in the fast set of tests. | 541 | [
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|
Development | Testing | Query Verification | Many simple tests start by enabling query verification. This can be done through the following `PRAGMA` statement:
```sql
statement ok
PRAGMA enable_verification
```
Query verification performs extra validation to ensure that the underlying code runs correctly. The most important part of that is that it verifies that optimizers do not cause bugs in the query. It does this by running both an unoptimized and optimized version of the query, and verifying that the results of these queries are identical.
Query verification is very useful because it not only discovers bugs in optimizers, but also finds bugs in e.g., join implementations. This is because the unoptimized version will typically run using cross products instead. Because of this, query verification can be very slow to do when working with larger data sets. It is therefore recommended to turn on query verification for all unit tests, except those involving larger data sets (more than ~10-100 rows). | 188 | [
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|
Development | Testing | Editors & Syntax Highlighting | The sqllogictests are not exactly an industry standard, but several other systems have adopted them as well. Parsing sqllogictests is intentionally simple. All statements have to be separated by empty lines. For that reason, writing a syntax highlighter is not extremely difficult.
A syntax highlighter exists for [Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=benesch.sqllogictest). We have also [made a fork that supports the DuckDB dialect of the sqllogictests](https://github.com/Mytherin/vscode-sqllogictest). You can use the fork by installing the original, then copying the `syntaxes/sqllogictest.tmLanguage.json` into the installed extension (on macOS this is located in `~/.vscode/extensions/benesch.sqllogictest-0.1.1`).
A syntax highlighter is also available for [CLion](https://plugins.jetbrains.com/plugin/15295-sqltest). It can be installed directly on the IDE by searching SQLTest on the marketplace. A [GitHub repository](https://github.com/pdet/SQLTest) is also available, with extensions and bug reports being welcome.
#### Temporary Files {#docs:dev:sqllogictest:intro::temporary-files}
For some tests (e.g., CSV/Parquet file format tests) it is necessary to create temporary files. Any temporary files should be created in the temporary testing directory. This directory can be used by placing the string `__TEST_DIR__` in a query. This string will be replaced by the path of the temporary testing directory.
```sql
statement ok
COPY csv_data TO '__TEST_DIR__/output_file.csv.gz' (COMPRESSION GZIP);
```
#### Require & Extensions {#docs:dev:sqllogictest:intro::require--extensions}
To avoid bloating the core system, certain functionality of DuckDB is available only as an extension. Tests can be build for those extensions by adding a `require` field in the test. If the extension is not loaded, any statements that occurs after the require field will be skipped. Examples of this are `require parquet` or `require icu`.
Another usage is to limit a test to a specific vector size. For example, adding `require vector_size 512` to a test will prevent the test from being run unless the vector size greater than or equal to 512. This is useful because certain functionality is not supported for low vector sizes, but we run tests using a vector size of 2 in our CI. | 533 | [
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|
Development | Testing | Development and Testing | It is crucial that any new features that get added have correct tests that not only test the “happy path”, but also test edge cases and incorrect usage of the feature. In this section, we describe how DuckDB tests are structured and how to make new tests for DuckDB.
The tests can be run by running the `unittest` program located in the `test` folder. For the default compilations this is located in either `build/release/test/unittest` (release) or `build/debug/test/unittest` (debug). | 108 | [
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|
Development | Testing | Philosophy | When testing DuckDB, we aim to route all the tests through SQL. We try to avoid testing components individually because that makes those components more difficult to change later on. As such, almost all of our tests can (and should) be expressed in pure SQL. There are certain exceptions to this, which we will discuss in [Catch Tests](#docs:dev:sqllogictest:catch). However, in most cases you should write your tests in plain SQL. | 95 | [
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|
Development | Testing | Frameworks | SQL tests should be written using the [sqllogictest framework](#docs:dev:sqllogictest:intro).
C++ tests can be written using the [Catch framework](#docs:dev:sqllogictest:catch). | 51 | [
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|
Development | Testing | Client Connector Tests | DuckDB also has tests for various client connectors. These are generally written in the relevant client language, and can be found in `tools/*/tests`.
They also double as documentation of what should be doable from a given client. | 47 | [
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|
Development | Testing | Functions for Generating Test Data | DuckDB has built-in functions for generating test data.
#### `test_all_types` Function {#docs:dev:sqllogictest:writing_tests::test_all_types-function}
The `test_all_types` table function generates a table whose columns correspond to types (` BOOL`, `TINYINT`, etc.).
The table has three rows encoding the minimum value, the maximum value, and the null value for each type.
```sql
FROM test_all_types();
```
```text
┌─────────┬─────────┬──────────┬─────────────┬──────────────────────┬──────────────────────┬───┬──────────────────────┬──────────────────────┬──────────────────────┬──────────────────────┬──────────────────────┐
│ bool │ tinyint │ smallint │ int │ bigint │ hugeint │ … │ struct │ struct_of_arrays │ array_of_structs │ map │ union │
│ boolean │ int8 │ int16 │ int32 │ int64 │ int128 │ │ struct(a integer, … │ struct(a integer[]… │ struct(a integer, … │ map(varchar, varch… │ union("name" varch… │
├─────────┼─────────┼──────────┼─────────────┼──────────────────────┼──────────────────────┼───┼──────────────────────┼──────────────────────┼──────────────────────┼──────────────────────┼──────────────────────┤
│ false │ -128 │ -32768 │ -2147483648 │ -9223372036854775808 │ -17014118346046923… │ … │ {'a': NULL, 'b': N… │ {'a': NULL, 'b': N… │ [] │ {} │ Frank │
│ true │ 127 │ 32767 │ 2147483647 │ 9223372036854775807 │ 170141183460469231… │ … │ {'a': 42, 'b': 🦆… │ {'a': [42, 999, NU… │ [{'a': NULL, 'b': … │ {key1=🦆🦆🦆🦆🦆🦆… │ 5 │
│ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ NULL │ NULL │
├─────────┴─────────┴──────────┴─────────────┴──────────────────────┴──────────────────────┴───┴──────────────────────┴──────────────────────┴──────────────────────┴──────────────────────┴──────────────────────┤
│ 3 rows 44 columns (11 shown) │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
#### `test_vector_types` Function {#docs:dev:sqllogictest:writing_tests::test_vector_types-function}
The `test_vector_types` table function takes _n_ arguments `col1`, ..., `coln` and an optional `BOOLEAN` argument `all_flat`.
The function generates a table with _n_ columns `test_vector`, `test_vector2`, ..., `test_vectorn`.
In each row, each field contains values conforming to the type of their respective column.
```sql
FROM test_vector_types(NULL::BIGINT);
```
```text
┌──────────────────────┐
│ test_vector │
│ int64 │
├──────────────────────┤
│ -9223372036854775808 │
│ 9223372036854775807 │
│ NULL │
│ ... │
└──────────────────────┘
```
```sql
FROM test_vector_types(NULL::ROW(i INTEGER, j VARCHAR, k DOUBLE), NULL::TIMESTAMP);
```
```text
┌──────────────────────────────────────────────────────────────────────┬──────────────────────────────┐
│ test_vector │ test_vector2 │
│ struct(i integer, j varchar, k double) │ timestamp │
├──────────────────────────────────────────────────────────────────────┼──────────────────────────────┤
│ {'i': -2147483648, 'j': 🦆🦆🦆🦆🦆🦆, 'k': -1.7976931348623157e+308} │ 290309-12-22 (BC) 00:00:00 │
│ {'i': 2147483647, 'j': goo\0se, 'k': 1.7976931348623157e+308} │ 294247-01-10 04:00:54.775806 │
│ {'i': NULL, 'j': NULL, 'k': NULL} │ NULL │
│ ... │
└─────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
`test_vector_types` has an optional argument called `all_flat` of type `BOOL`. This only affects the internal representation of the vector.
```sql
FROM test_vector_types(NULL::ROW(i INTEGER, j VARCHAR, k DOUBLE), NULL::TIMESTAMP, all_flat = true);
-- the output is the same as above but with a different internal representation
``` | 1,243 | [
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|
Development | Testing | Debugging | The purpose of the tests is to figure out when things break. Inevitably changes made to the system will cause one of the tests to fail, and when that happens the test needs to be debugged.
First, it is always recommended to run in debug mode. This can be done by compiling the system using the command `make debug`. Second, it is recommended to only run the test that breaks. This can be done by passing the filename of the breaking test to the test suite as a command line parameter (e.g., `build/debug/test/unittest test/sql/projection/test_simple_projection.test`). For more options on running a subset of the tests see the [Triggering which tests to run](#::triggering-which-tests-to-run) section.
After that, a debugger can be attached to the program and the test can be debugged. In the sqllogictests it is normally difficult to break on a specific query, however, we have expanded the test suite so that a function called `query_break` is called with the line number `line` as parameter for every query that is run. This allows you to put a conditional breakpoint on a specific query. For example, if we want to break on line number 43 of the test file we can create the following break point:
```text
gdb: break query_break if line==43
lldb: break s -n query_break -c line==43
```
You can also skip certain queries from executing by placing `mode skip` in the file, followed by an optional `mode unskip`. Any queries between the two statements will not be executed. | 335 | [
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|
Development | Testing | Triggering Which Tests to Run | When running the unittest program, by default all the fast tests are run. A specific test can be run by adding the name of the test as an argument. For the sqllogictests, this is the relative path to the test file.
To run only a single test:
```bash
build/debug/test/unittest test/sql/projection/test_simple_projection.test
```
All tests in a given directory can be executed by providing the directory as a parameter with square brackets.
To run all tests in the “projection” directory:
```bash
build/debug/test/unittest "[projection]"
```
All tests, including the slow tests, can be run by running the tests with an asterisk.
To run all tests, including the slow tests:
```bash
build/debug/test/unittest "*"
```
We can run a subset of the tests using the `--start-offset` and `--end-offset` parameters.
To run the tests 200..250:
```bash
build/debug/test/unittest --start-offset=200 --end-offset=250
```
These are also available in percentages. To run tests 10% - 20%:
```bash
build/debug/test/unittest --start-offset-percentage=10 --end-offset-percentage=20
```
The set of tests to run can also be loaded from a file containing one test name per line, and loaded using the `-f` command.
```bash
cat test.list
```
```text
test/sql/join/full_outer/test_full_outer_join_issue_4252.test
test/sql/join/full_outer/full_outer_join_cache.test
test/sql/join/full_outer/test_full_outer_join.test
```
To run only the tests labeled in the file:
```bash
build/debug/test/unittest -f test.list
``` | 371 | [
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|
Development | Testing | Result Verification | The standard way of verifying results of queries is using the `query` statement, followed by the letter `I` times the number of columns that are expected in the result. After the query, four dashes (` ----`) are expected followed by the result values separated by tabs. For example,
```sql
query II
SELECT 42, 84 UNION ALL SELECT 10, 20;
----
4284
1020
```
For legacy reasons the letters `R` and `T` are also accepted to denote columns.
> **Deprecated. ** DuckDB deprecated the usage of types in the sqllogictest. The DuckDB test runner does not use or need them internally – therefore, only `I` should be used to denote columns. | 155 | [
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|
Development | Testing | NULL Values and Empty Strings | Empty lines have special significance for the SQLLogic test runner: they signify an end of the current statement or query. For that reason, empty strings and NULL values have special syntax that must be used in result verification. NULL values should use the string `NULL`, and empty strings should use the string `(empty)`, e.g.:
```sql
query II
SELECT NULL, ''
----
NULL
(empty)
``` | 83 | [
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|
Development | Testing | Error Verification | In order to signify that an error is expected, the `statement error` indicator can be used. The `statement error` also takes an optional expected result – which is interpreted as the *expected error message*. Similar to `query`, the expected error should be placed after the four dashes (` ----`) following the query. The test passes if the error message *contains* the text under `statement error` – the entire error message does not need to be provided. It is recommended that you only use a subset of the error message, so that the test does not break unnecessarily if the formatting of error messages is changed.
```sql
statement error
SELECT * FROM non_existent_table;
----
Table with name non_existent_table does not exist!
``` | 152 | [
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|
Development | Testing | Regex | In certain cases result values might be very large or complex, and we might only be interested in whether or not the result *contains* a snippet of text. In that case, we can use the `<REGEX>:` modifier followed by a certain regex. If the result value matches the regex the test is passed. This is primarily used for query plan analysis.
```sql
query II
EXPLAIN SELECT tbl.a FROM "data/parquet-testing/arrow/alltypes_plain.parquet" tbl(a) WHERE a = 1 OR a = 2
----
physical_plan<REGEX>:.*PARQUET_SCAN.*Filters: a=1 OR a=2.*
```
If we instead want the result *not* to contain a snippet of text, we can use the `<!REGEX>:` modifier. | 167 | [
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|
Development | Testing | File | As results can grow quite large, and we might want to re-use results over multiple files, it is also possible to read expected results from files using the `<FILE>` command. The expected result is read from the given file. As convention the file path should be provided as relative to the root of the GitHub repository.
```sql
query I
PRAGMA tpch(1)
----
<FILE>:extension/tpch/dbgen/answers/sf1/q01.csv
``` | 98 | [
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|
Development | Testing | Row-Wise vs. Value-Wise Result Ordering | The result values of a query can be either supplied in row-wise order, with the individual values separated by tabs, or in value-wise order. In value wise order the individual *values* of the query must appear in row, column order each on an individual line. Consider the following example in both row-wise and value-wise order:
```sql
# row-wise
query II
SELECT 42, 84 UNION ALL SELECT 10, 20;
----
4284
1020
# value-wise
query II
SELECT 42, 84 UNION ALL SELECT 10, 20;
----
42
84
10
20
``` | 132 | [
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|
Development | Testing | Hashes and Outputting Values | Besides direct result verification, the sqllogic test suite also has the option of using MD5 hashes for value comparisons. A test using hashes for result verification looks like this:
```sql
query I
SELECT g, string_agg(x,',') FROM strings GROUP BY g
----
200 values hashing to b8126ea73f21372cdb3f2dc483106a12
```
This approach is useful for reducing the size of tests when results have many output rows. However, it should be used sparingly, as hash values make the tests more difficult to debug if they do break.
After it is ensured that the system outputs the correct result, hashes of the queries in a test file can be computed by adding `mode output_hash` to the test file. For example:
```sql
mode output_hash
query II
SELECT 42, 84 UNION ALL SELECT 10, 20;
----
4284
1020
```
The expected output hashes for every query in the test file will then be printed to the terminal, as follows:
```text
================================================================================
SQL Query
SELECT 42, 84 UNION ALL SELECT 10, 20;
================================================================================
4 values hashing to 498c69da8f30c24da3bd5b322a2fd455
================================================================================
```
In a similar manner, `mode output_result` can be used in order to force the program to print the result to the terminal for every query run in the test file. | 307 | [
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|
Development | Testing | Result Sorting | Queries can have an optional field that indicates that the result should be sorted in a specific manner. This field goes in the same location as the connection label. Because of that, connection labels and result sorting cannot be mixed.
The possible values of this field are `nosort`, `rowsort` and `valuesort`. An example of how this might be used is given below:
```sql
query I rowsort
SELECT 'world' UNION ALL SELECT 'hello'
----
hello
world
```
In general, we prefer not to use this field and rely on `ORDER BY` in the query to generate deterministic query answers. However, existing sqllogictests use this field extensively, hence it is important to know of its existence. | 151 | [
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Development | Testing | Query Labels | Another feature that can be used for result verification are `query labels`. These can be used to verify that different queries provide the same result. This is useful for comparing queries that are logically equivalent, but formulated differently. Query labels are provided after the connection label or sorting specifier.
Queries that have a query label do not need to have a result provided. Instead, the results of each of the queries with the same label are compared to each other. For example, the following script verifies that the queries `SELECT 42+1` and `SELECT 44-1` provide the same result:
```sql
query I nosort r43
SELECT 42+1;
----
query I nosort r43
SELECT 44-1;
----
``` | 153 | [
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|
Development | Testing | Persistent Testing | By default, all tests are run in in-memory mode (unless `--force-storage` is enabled). In certain cases, we want to force the usage of a persistent database. We can initiate a persistent database using the `load` command, and trigger a reload of the database using the `restart` command.
```sql
# load the DB from disk
load __TEST_DIR__/storage_scan.db
statement ok
CREATE TABLE test (a INTEGER);
statement ok
INSERT INTO test VALUES (11), (12), (13), (14), (15), (NULL)
# ...
restart
query I
SELECT * FROM test ORDER BY a
----
NULL
11
12
13
14
15
```
Note that by default the tests run with `SET wal_autocheckpoint = '0KB'` – meaning a checkpoint is triggered after every statement. WAL tests typically run with the following settings to disable this behavior:
```sql
statement ok
PRAGMA disable_checkpoint_on_shutdown
statement ok
PRAGMA wal_autocheckpoint = '1TB'
``` | 218 | [
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|
Development | Testing | Loops | Loops can be used in sqllogictests when it is required to execute the same query many times but with slight modifications in constant values. For example, suppose we want to fire off 100 queries that check for the presence of the values `0..100` in a table:
```sql
# create the table 'integers' with values 0..100
statement ok
CREATE TABLE integers AS SELECT * FROM range(0, 100, 1) t1(i);
# verify individually that all 100 values are there
loop i 0 100
# execute the query, replacing the value
query I
SELECT count(*) FROM integers WHERE i = ${i};
----
1
# end the loop (note that multiple statements can be part of a loop)
endloop
```
Similarly, `foreach` can be used to iterate over a set of values.
```sql
foreach partcode millennium century decade year quarter month day hour minute second millisecond microsecond epoch
query III
SELECT i, date_part('${partcode}', i) AS p, date_part(['${partcode}'], i) AS st
FROM intervals
WHERE p <> st['${partcode}'];
----
endloop
```
`foreach` also has a number of preset combinations that should be used when required. In this manner, when new combinations are added to the preset, old tests will automatically pick up these new combinations.
<div class="narrow_table monospace_table"></div>
| Preset | Expansion |
|----------------|--------------------------------------------------------------|
| ⟨compression⟩ | none uncompressed rle bitpacking dictionary fsst chimp patas |
| ⟨signed⟩ | tinyint smallint integer bigint hugeint |
| ⟨unsigned⟩ | utinyint usmallint uinteger ubigint uhugeint |
| ⟨integral⟩ | ⟨signed⟩ ⟨unsigned⟩ |
| ⟨numeric⟩ | ⟨integral⟩ float double |
| ⟨alltypes⟩ | ⟨numeric⟩ bool interval varchar json |
> Use large loops sparingly. Executing hundreds of thousands of SQL statements will slow down tests unnecessarily. Do not use loops for inserting data. | 455 | [
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|
Development | Testing | Data Generation without Loops | Loops should be used sparingly. While it might be tempting to use loops for inserting data using insert statements, this will considerably slow down the test cases. Instead, it is better to generate data using the built-in `range` and `repeat` functions.
To create the table `integers` with the values `[0, 1, .., 98, 99]`, run:
```sql
CREATE TABLE integers AS SELECT * FROM range(0, 100, 1) t1(i);
```
To create the table `strings` with 100 times the value `hello`, run:
```sql
CREATE TABLE strings AS SELECT * FROM repeat('hello', 100) t1(s);
```
Using these two functions, together with clever use of cross products and other expressions, many different types of datasets can be efficiently generated. The `random()` function can also be used to generate random data.
An alternative option is to read data from an existing CSV or Parquet file. There are several large CSV files that can be loaded from the directory `test/sql/copy/csv/data/real` using a `COPY INTO` statement or the `read_csv_auto` function.
The TPC-H and TPC-DS extensions can also be used to generate synthetic data, using e.g. `CALL dbgen(sf = 1)` or `CALL dsdgen(sf = 1)`. | 293 | [
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|
Development | Testing | Multiple Connections | For tests whose purpose is to verify that the transactional management or versioning of data works correctly, it is generally necessary to use multiple connections. For example, if we want to verify that the creation of tables is correctly transactional, we might want to start a transaction and create a table in `con1`, then fire a query in `con2` that checks that the table is not accessible yet until committed.
We can use multiple connections in the sqllogictests using `connection labels`. The connection label can be optionally appended to any `statement` or `query`. All queries with the same connection label will be executed in the same connection. A test that would verify the above property would look as follows:
```sql
statement ok con1
BEGIN TRANSACTION
statement ok con1
CREATE TABLE integers (i INTEGER);
statement error con2
SELECT * FROM integers;
``` | 180 | [
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|
Development | Testing | Concurrent Connections | Using connection modifiers on the statement and queries will result in testing of multiple connections, but all the queries will still be run *sequentially* on a single thread. If we want to run code from multiple connections *concurrently* over multiple threads, we can use the `concurrentloop` construct. The queries in `concurrentloop` will be run concurrently on separate threads at the same time.
```sql
concurrentloop i 0 10
statement ok
CREATE TEMP TABLE t2 AS (SELECT 1);
statement ok
INSERT INTO t2 VALUES (42);
statement ok
DELETE FROM t2
endloop
```
One caveat with `concurrentloop` is that results are often unpredictable - as multiple clients can hammer the database at the same time we might end up with (expected) transaction conflicts. `statement maybe` can be used to deal with these situations. `statement maybe` essentially accepts both a success, and a failure with a specific error message.
```sql
concurrentloop i 1 10
statement maybe
CREATE OR REPLACE TABLE t2 AS (SELECT -54124033386577348004002656426531535114 FROM t2 LIMIT 70%);
----
write-write conflict
endloop
``` | 256 | [
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|
Development | Testing | Catch C/C++ Tests | While we prefer the sqllogic tests for testing most functionality, for certain tests only SQL is not sufficient. This typically happens when you want to test the C++ API. When using pure SQL is really not an option it might be necessary to make a C++ test using Catch.
Catch tests reside in the test directory as well. Here is an example of a catch test that tests the storage of the system:
```cpp
#include "catch.hpp"
#include "test_helpers.hpp"
TEST_CASE("Test simple storage", "[storage]") {
auto config = GetTestConfig();
unique_ptr<QueryResult> result;
auto storage_database = TestCreatePath("storage_test");
// make sure the database does not exist
DeleteDatabase(storage_database);
{
// create a database and insert values
DuckDB db(storage_database, config.get());
Connection con(db);
REQUIRE_NO_FAIL(con.Query("CREATE TABLE test (a INTEGER, b INTEGER);"));
REQUIRE_NO_FAIL(con.Query("INSERT INTO test VALUES (11, 22), (13, 22), (12, 21), (NULL, NULL)"));
REQUIRE_NO_FAIL(con.Query("CREATE TABLE test2 (a INTEGER);"));
REQUIRE_NO_FAIL(con.Query("INSERT INTO test2 VALUES (13), (12), (11)"));
}
// reload the database from disk a few times
for (idx_t i = 0; i < 2; i++) {
DuckDB db(storage_database, config.get());
Connection con(db);
result = con.Query("SELECT * FROM test ORDER BY a");
REQUIRE(CHECK_COLUMN(result, 0, {Value(), 11, 12, 13}));
REQUIRE(CHECK_COLUMN(result, 1, {Value(), 22, 21, 22}));
result = con.Query("SELECT * FROM test2 ORDER BY a");
REQUIRE(CHECK_COLUMN(result, 0, {11, 12, 13}));
}
DeleteDatabase(storage_database);
}
```
The test uses the `TEST_CASE` wrapper to create each test. The database is created and queried using the C++ API. Results are checked using either `REQUIRE_FAIL`/`REQUIRE_NO_FAIL` (corresponding to statement ok and statement error) or `REQUIRE(CHECK_COLUMN(...))` (corresponding to query with a result check). Every test that is created in this way needs to be added to the corresponding `CMakeLists.txt`. | 500 | [
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|
Development | Profiling | Profiling is essential to help understand why certain queries exhibit specific performance characteristics.
DuckDB contains several built-in features to enable query profiling, which this page covers. | 33 | [
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||
Development | Profiling | `EXPLAIN` Statement | The first step to profiling a query can include examining the query plan.
The [`EXPLAIN`](#docs:guides:meta:explain) statement shows the query plan and describes what is going on under the hood. | 44 | [
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|
Development | Profiling | `EXPLAIN ANALYZE` Statement | The query plan helps developers understand the performance characteristics of the query.
However, it is often also necessary to examine the performance numbers of individual operators and the cardinalities that pass through them.
The [`EXPLAIN ANALYZE`](#docs:guides:meta:explain_analyze) statement enables obtaining these, as it pretty-prints the query plan and also executes the query.
Thus, it provides the actual run-time performance numbers. | 87 | [
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|
Development | Profiling | Pragmas | DuckDB supports several pragmas for turning profiling on and off and controlling the level of detail in the profiling output.
The following pragmas are available and can be set using either `PRAGMA` or `SET`.
They can also be reset using `RESET`, followed by the setting name.
For more information, see the [“Profiling”](#docs:configuration:pragmas::profiling) section of the pragmas page.
| Setting | Description | Default | Options |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------|-----------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|
| [`enable_profiling`](#docs:configuration:pragmas::enable_profiling), [`enable_profile`](#docs:configuration:pragmas::enable_profiling) | Turn on profiling. | `query_tree` | `query_tree`, `json`, `query_tree_optimizer`, `no_output` |
| [`profiling_output`](#docs:configuration:pragmas::profiling_output) | Set a profiling output file. | Console | A filepath. |
| [`profiling_mode`](#docs:configuration:pragmas::profiling_mode) | Toggle additional optimizer and planner metrics. | `standard` | `standard`, `detailed` |
| [`custom_profiling_settings`](#docs:configuration:pragmas::custom_profiling_metrics) | Enable or disable specific metrics. | All metrics except those activated by detailed profiling. | A JSON object that matches the following: `{"METRIC_NAME": "boolean", ...}`. See the [metrics](#::metrics) section below. |
| [`disable_profiling`](#docs:configuration:pragmas::disable_profiling), [`disable_profile`](#docs:configuration:pragmas::disable_profiling) | Turn off profiling. | | | | 394 | [
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|
Development | Profiling | Metrics | The query tree has two types of nodes: the `QUERY_ROOT` and `OPERATOR` nodes.
The `QUERY_ROOT` refers exclusively to the top-level node, and the metrics it contains are measured over the entire query.
The `OPERATOR` nodes refer to the individual operators in the query plan.
Some metrics are only available for `QUERY_ROOT` nodes, while others are only for `OPERATOR` nodes.
The table below describes each metric and which nodes they are available for.
Other than `QUERY_NAME` and `OPERATOR_TYPE`, it is possible to turn all metrics on or off.
| Metric | Return type | Unit | Query | Operator | Description |
|-------------------------|-------------|----------|:-----:|:--------:|-------------------------------------------------------------------------------------------------------------------------------|
| `BLOCKED_THREAD_TIME` | `double` | seconds | ✅ | | The total time threads are blocked. |
| `EXTRA_INFO` | `string` | | ✅ | ✅ | Unique operator metrics. |
| `LATENCY` | `double` | seconds | ✅ | | The total elapsed query execution time. |
| `OPERATOR_CARDINALITY` | `uint64` | absolute | | ✅ | The cardinality of each operator, i.e., the number of rows it returns to its parent. Operator equivalent of `ROWS_RETURNED`. |
| `OPERATOR_ROWS_SCANNED` | `uint64` | absolute | | ✅ | The total rows scanned by each operator. |
| `OPERATOR_TIMING` | `double` | seconds | | ✅ | The time taken by each operator. Operator equivalent of `LATENCY`. |
| `OPERATOR_TYPE` | `string` | | | ✅ | The name of each operator. |
| `QUERY_NAME` | `string` | | ✅ | | The query string. |
| `RESULT_SET_SIZE` | `uint64` | bytes | ✅ | ✅ | The size of the result. |
| `ROWS_RETURNED` | `uint64` | absolute | ✅ | | The number of rows returned by the query. |
#### Cumulative Metrics {#docs:dev:profiling::cumulative-metrics}
DuckDB also supports several cumulative metrics that are available in all nodes.
In the `QUERY_ROOT` node, these metrics represent the sum of the corresponding metrics across all operators in the query.
The `OPERATOR` nodes represent the sum of the operator's specific metric and those of all its children recursively.
These cumulative metrics can be enabled independently, even if the underlying specific metrics are disabled.
The table below shows the cumulative metrics.
It also depicts the metric based on which DuckDB calculates the cumulative metric.
| Metric | Unit | Metric calculated cumulatively |
|---------------------------|----------|--------------------------------|
| `CPU_TIME` | seconds | `OPERATOR_TIMING` |
| `CUMULATIVE_CARDINALITY` | absolute | `OPERATOR_CARDINALITY` |
| `CUMULATIVE_ROWS_SCANNED` | absolute | `OPERATOR_ROWS_SCANNED` |
`CPU_TIME` measures the cumulative operator timings.
It does not include time spent in other stages, like parsing, query planning, etc.
Thus, for some queries, the `LATENCY` in the `QUERY_ROOT` can be greater than the `CPU_TIME`. | 786 | [
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|
Development | Profiling | Detailed Profiling | When the `profiling_mode` is set to `detailed`, an extra set of metrics are enabled, which are only available in the `QUERY_ROOT` node.
These include [`OPTIMIZER`](#::optimizer-metrics), [`PLANNER`](#::planner-metrics), and [`PHYSICAL_PLANNER`](#::physical-planner-metrics) metrics.
They are measured in seconds and returned as a `double`.
It is possible to toggle each of these additional metrics individually.
#### Optimizer Metrics {#docs:dev:profiling::optimizer-metrics}
At the `QUERY_ROOT` node, there are metrics that measure the time taken by each [optimizer](#docs:internals:overview::optimizer).
These metrics are only available when the specific optimizer is enabled.
The available optimizations can be queried using the [`duckdb_optimizers() table function`](#docs:sql:meta:duckdb_table_functions::duckdb_optimizers).
Each optimizer has a corresponding metric that follows the template: `OPTIMIZER_⟨OPTIMIZER_NAME⟩`.
For example, the `OPTIMIZER_JOIN_ORDER` metric corresponds to the `JOIN_ORDER` optimizer.
Additionally, the following metrics are available to support the optimizer metrics:
* `ALL_OPTIMIZERS`: Enables all optimizer metrics and measures the time the optimizer parent node takes.
* `CUMMULATIVE_OPTIMIZER_TIMING`: The cumulative sum of all optimizer metrics. It is usable without turning on all optimizer metrics.
#### Planner Metrics {#docs:dev:profiling::planner-metrics}
The planner is responsible for generating the logical plan. Currently, DuckDB measures two metrics in the planner:
* `PLANNER`: The time to generate the logical plan from the parsed SQL nodes.
* `PLANNER_BINDING`: The time taken to bind the logical plan.
#### Physical Planner Metrics {#docs:dev:profiling::physical-planner-metrics}
The physical planner is responsible for generating the physical plan from the logical plan.
The following are the metrics supported in the physical planner:
* `PHYSICAL_PLANNER`: The time spent generating the physical plan.
* `PHYSICAL_PLANNER_COLUMN_BINDING`: The time spent binding the columns in the logical plan to physical columns.
* `PHYSICAL_PLANNER_RESOLVE_TYPES`: The time spent resolving the types in the logical plan to physical types.
* `PHYSICAL_PLANNER_CREATE_PLAN`: The time spent creating the physical plan. | 516 | [
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|
Development | Profiling | Custom Metrics Examples | The following examples demonstrate how to enable custom profiling and set the output format to `json`.
In the first example, we enable profiling and set the output to a file.
We only enable `EXTRA_INFO`, `OPERATOR_CARDINALITY`, and `OPERATOR_TIMING`.
```sql
CREATE TABLE students (name VARCHAR, sid INTEGER);
CREATE TABLE exams (eid INTEGER, subject VARCHAR, sid INTEGER);
INSERT INTO students VALUES ('Mark', 1), ('Joe', 2), ('Matthew', 3);
INSERT INTO exams VALUES (10, 'Physics', 1), (20, 'Chemistry', 2), (30, 'Literature', 3);
PRAGMA enable_profiling = 'json';
PRAGMA profiling_output = '/path/to/file.json';
PRAGMA custom_profiling_settings = '{"CPU_TIME": "false", "EXTRA_INFO": "true", "OPERATOR_CARDINALITY": "true", "OPERATOR_TIMING": "true"}';
SELECT name
FROM students
JOIN exams USING (sid)
WHERE name LIKE 'Ma%';
```
The file's content after executing the query:
```json
{
"extra_info": {},
"query_name": "SELECT name\nFROM students\nJOIN exams USING (sid)\nWHERE name LIKE 'Ma%';",
"children": [
{
"operator_timing": 0.000001,
"operator_cardinality": 2,
"operator_type": "PROJECTION",
"extra_info": {
"Projections": "name",
"Estimated Cardinality": "1"
},
"children": [
{
"extra_info": {
"Join Type": "INNER",
"Conditions": "sid = sid",
"Build Min": "1",
"Build Max": "3",
"Estimated Cardinality": "1"
},
"operator_cardinality": 2,
"operator_type": "HASH_JOIN",
"operator_timing": 0.00023899999999999998,
"children": [
...
```
The second example adds detailed metrics to the output.
```sql
PRAGMA profiling_mode = 'detailed';
SELECT name
FROM students
JOIN exams USING (sid)
WHERE name LIKE 'Ma%';
```
The contents of the outputted file:
```json
{
"all_optimizers": 0.001413,
"cumulative_optimizer_timing": 0.0014120000000000003,
"planner": 0.000873,
"planner_binding": 0.000869,
"physical_planner": 0.000236,
"physical_planner_column_binding": 0.000005,
"physical_planner_resolve_types": 0.000001,
"physical_planner_create_plan": 0.000226,
"optimizer_expression_rewriter": 0.000029,
"optimizer_filter_pullup": 0.000002,
"optimizer_filter_pushdown": 0.000102,
...
"optimizer_column_lifetime": 0.000009999999999999999,
"rows_returned": 2,
"latency": 0.003708,
"cumulative_rows_scanned": 6,
"cumulative_cardinality": 11,
"extra_info": {},
"cpu_time": 0.000095,
"optimizer_build_side_probe_side": 0.000017,
"result_set_size": 32,
"blocked_thread_time": 0.0,
"query_name": "SELECT name\nFROM students\nJOIN exams USING (sid)\nWHERE name LIKE 'Ma%';",
"children": [
{
"operator_timing": 0.000001,
"operator_rows_scanned": 0,
"cumulative_rows_scanned": 6,
"operator_cardinality": 2,
"operator_type": "PROJECTION",
"cumulative_cardinality": 11,
"extra_info": {
"Projections": "name",
"Estimated Cardinality": "1"
},
"result_set_size": 32,
"cpu_time": 0.000095,
"children": [
...
``` | 827 | [
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|
Development | Profiling | Query Graphs | It is also possible to render the profiling output as a query graph.
The query graph visually represents the query plan, showing the operators and their relationships.
The query plan must be output in the `json` format and stored in a file.
After writing a profiling output to its designated file, the Python script can render it as a query graph.
The script requires the `duckdb` Python module to be installed.
It generates an HTML file and opens it in your web browser.
```bash
python -m duckdb.query_graph /path/to/file.json
``` | 114 | [
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Development | Profiling | Notation in Query Plans | In query plans, the [hash join](https://en.wikipedia.org/wiki/Hash_join) operators adhere to the following convention:
the _probe side_ of the join is the left operand, while the _build side_ is the right operand.
Join operators in the query plan show the join type used:
* Inner joins are denoted as `INNER`.
* Left outer joins and right outer joins are denoted as `LEFT` and `RIGHT`, respectively.
* Full outer joins are denoted as `FULL`. | 105 | [
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|
Development | Building | Building DuckDB from Source | > DuckDB binaries are available for stable and nightly builds on the [installation page](https://duckdb.org/docs/installation/).
> You should only build DuckDB under specific circumstances, such as when running on a specific architecture or building an unmerged pull request. | 54 | [
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|
Development | Building | Prerequisites | DuckDB needs CMake and a C++11-compliant compiler (e.g., GCC, Apple-Clang, MSVC).
Additionally, we recommend using the [Ninja build system](https://ninja-build.org/), which automatically parallelizes the build process. | 56 | [
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|
Development | Building | UNIX-Like Systems | #### macOS Packages {#docs:dev:building:build_instructions::macos-packages}
Install Xcode and [Homebrew](https://brew.sh/). Then, install the required packages with:
```bash
brew install git cmake ninja
```
#### Linux Packages {#docs:dev:building:build_instructions::linux-packages}
Install the required packages with the package manager of your distribution. | 83 | [
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|
Development | Building | UNIX-Like Systems | Ubuntu and Debian | ```bash
sudo apt-get update && sudo apt-get install -y git g++ cmake ninja-build libssl-dev
``` | 25 | [
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] |
Development | Building | UNIX-Like Systems | Fedora, CentOS, and Red Hat | ```bash
sudo yum install -y git g++ cmake ninja-build openssl-devel
``` | 18 | [
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Development | Building | UNIX-Like Systems | Alpine Linux | ```bash
apk add g++ git make cmake ninja
```
Note that Alpine Linux uses the musl libc as its C standard library.
There are no official binaries distributed for musl libc but DuckDB can be build with it manually following the instructions on this page.
#### Cloning the Repository {#docs:dev:building:build_instructions::cloning-the-repository}
Clone the DuckDB repository:
```bash
git clone https://github.com/duckdb/duckdb
```
We recommend creating a full clone of the repository. Note that the directory uses approximately 1.3 GB of disk space.
#### Building DuckDB {#docs:dev:building:build_instructions::building-duckdb}
To build DuckDB, we use a Makefile which in turn calls into CMake. We also advise using [Ninja](https://ninja-build.org/manual.html) as the generator for CMake.
```bash
GEN=ninja make
```
> **Best practice. ** It is not advised to directly call CMake, as the Makefile sets certain variables that are crucial to properly building the package.
Once the build finishes successfully, you can find the `duckdb` binary in the `build` directory:
```bash
build/release/duckdb
```
#### Linking Extensions {#docs:dev:building:build_instructions::linking-extensions}
For testing, it can be useful to build DuckDB with statically linked core extensions. To do so, run:
```bash
CORE_EXTENSIONS='autocomplete;httpfs;icu;parquet;json' GEN=ninja make
```
This option also accepts out-of-tree extensions:
```bash
CORE_EXTENSIONS='autocomplete;httpfs;icu;parquet;json;delta' GEN=ninja make
```
For more details, see the [“Building Extensions” page](#docs:dev:building:building_extensions). | 404 | [
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Development | Building | Windows | On Windows, DuckDB requires the [Microsoft Visual C++ Redistributable package](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist) both as a build-time and runtime dependency. Note that unlike the build process on UNIX-like systems, the Windows builds directly call CMake.
#### Visual Studio {#docs:dev:building:build_instructions::visual-studio}
To build DuckDB on Windows, we recommend using the Visual Studio compiler.
To use it, follow the instructions in the [CI workflow](https://github.com/duckdb/duckdb/blob/52b43b166091c82b3f04bf8af15f0ace18207a64/.github/workflows/Windows.yml#L73):
```batch
python scripts/windows_ci.py
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_GENERATOR_PLATFORM=x64 \
-DENABLE_EXTENSION_AUTOLOADING=1 \
-DENABLE_EXTENSION_AUTOINSTALL=1 \
-DDUCKDB_EXTENSION_CONFIGS="${GITHUB_WORKSPACE}/.github/config/bundled_extensions.cmake" \
-DDISABLE_UNITY=1 \
-DOVERRIDE_GIT_DESCRIBE="$OVERRIDE_GIT_DESCRIBE"
cmake --build . --config Release --parallel
```
#### MSYS2 and MinGW64 {#docs:dev:building:build_instructions::msys2-and-mingw64}
DuckDB on Windows can also be built with [MSYS2](https://www.msys2.org/) and [MinGW64](https://www.mingw-w64.org/).
Note that this build is only supported for compatibility reasons and should only be used if the Visual Studio build is not feasible on a given platform.
To build DuckDB with MinGW64, install the required dependencies using Pacman.
When prompted with `Enter a selection (default=all)`, select the default option by pressing `Enter`.
```batch
pacman -Syu git mingw-w64-x86_64-toolchain mingw-w64-x86_64-cmake mingw-w64-x86_64-ninja
git clone https://github.com/duckdb/duckdb
cd duckdb
cmake -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DBUILD_EXTENSIONS="icu;parquet;json"
cmake --build . --config Release
```
Once the build finishes successfully, you can find the `duckdb.exe` binary in the repository's directory:
```batch
./duckdb.exe
``` | 527 | [
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|
Development | Building | Raspberry Pi (32-bit) | On 32-bit Raspberry Pi boards, you need to add the [`-latomic` link flag](https://github.com/duckdb/duckdb/issues/13855#issuecomment-2341539339).
As extensions are not distributed for this platform, it's recommended to also include them in the build.
For example:
```batch
mkdir build
cd build
cmake .. \
-DCORE_EXTENSIONS="httpfs;json;parquet" \
-DDUCKDB_EXTRA_LINK_FLAGS="-latomic"
make -j4
``` | 111 | [
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|
Development | Building | Troubleshooting | #### The Build Runs Out of Memory {#docs:dev:building:build_instructions::the-build-runs-out-of-memory}
Ninja parallelizes the build, which can cause out-of-memory issues on systems with limited resources.
These issues have also been reported to occur on Alpine Linux, especially on machines with limited resources.
In these cases, avoid using Ninja by setting the Makefile generator to empty (` GEN=`):
```bash
GEN= make
``` | 94 | [
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|
Development | Building | Build Types | DuckDB can be built in many different settings, most of these correspond directly to CMake but not all of them.
#### `release` {#docs:dev:building:build_configuration::release}
This build has been stripped of all the assertions and debug symbols and code, optimized for performance.
#### `debug` {#docs:dev:building:build_configuration::debug}
This build runs with all the debug information, including symbols, assertions and `#ifdef DEBUG` blocks.
Due to these, binaries of this build are expected to be slow.
Note: the special debug defines are not automatically set for this build.
#### `relassert` {#docs:dev:building:build_configuration::relassert}
This build does not trigger the `#ifdef DEBUG` code blocks but it still has debug symbols that make it possible to step through the execution with line number information and `D_ASSERT` lines are still checked in this build.
Binaries of this build mode are significantly faster than those of the `debug` mode.
#### `reldebug` {#docs:dev:building:build_configuration::reldebug}
This build is similar to `relassert` in many ways, only assertions are also stripped in this build.
#### `benchmark` {#docs:dev:building:build_configuration::benchmark}
This build is a shorthand for `release` with `BUILD_BENCHMARK=1` set.
#### `tidy-check` {#docs:dev:building:build_configuration::tidy-check}
This creates a build and then runs [Clang-Tidy](https://clang.llvm.org/extra/clang-tidy/) to check for issues or style violations through static analysis.
The CI will also run this check, causing it to fail if this check fails.
#### `format-fix` | `format-changes` | `format-main` {#docs:dev:building:build_configuration::format-fix--format-changes--format-main}
This doesn't actually create a build, but uses the following format checkers to check for style issues:
* [clang-format](https://clang.llvm.org/docs/ClangFormat.html) to fix format issues in the code.
* [cmake-format](https://cmake-format.readthedocs.io/en/latest/) to fix format issues in the `CMakeLists.txt` files.
The CI will also run this check, causing it to fail if this check fails. | 513 | [
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|
Development | Building | Extension Selection | [Core DuckDB extensions](#docs:extensions:core_extensions) are the ones maintaned by the DuckDB team. These are hosted in the `duckdb` GitHub organization and are served by the `core` extension repository.
Core extensions can be built as part of DuckDB via the `CORE_EXTENSION` flag, then listing the names of the extensions that are to be built.
```bash
CORE_EXTENSION='tpcd;httpfs;fts;json;parquet' make
```
More on this topic at [building duckdb extensions](#docs:dev:building:building_extensions). | 124 | [
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|
Development | Building | Package Flags | For every package that is maintained by core DuckDB, there exists a flag in the Makefile to enable building the package.
These can be enabled by either setting them in the current `env`, through set up files like `bashrc` or `zshrc`, or by setting them before the call to `make`, for example:
```bash
BUILD_PYTHON=1 make debug
```
#### `BUILD_PYTHON` {#docs:dev:building:build_configuration::build_python}
When this flag is set, the [Python](#docs:api:python:overview) package is built.
#### `BUILD_SHELL` {#docs:dev:building:build_configuration::build_shell}
When this flag is set, the [CLI](#docs:api:cli:overview) is built, this is usually enabled by default.
#### `BUILD_BENCHMARK` {#docs:dev:building:build_configuration::build_benchmark}
When this flag is set, DuckDB's in-house benchmark suite is built.
More information about this can be found [here](https://github.com/duckdb/duckdb/blob/main/benchmark/README.md).
#### `BUILD_JDBC` {#docs:dev:building:build_configuration::build_jdbc}
When this flag is set, the [Java](#docs:api:java) package is built.
#### `BUILD_ODBC` {#docs:dev:building:build_configuration::build_odbc}
When this flag is set, the [ODBC](#docs:api:odbc:overview) package is built. | 336 | [
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|
Development | Building | Miscellaneous Flags | #### `DISABLE_UNITY` {#docs:dev:building:build_configuration::disable_unity}
To improve compilation time, we use [Unity Build](https://cmake.org/cmake/help/latest/prop_tgt/UNITY_BUILD.html) to combine translation units.
This can however hide include bugs, this flag disables using the unity build so these errors can be detected.
#### `DISABLE_SANITIZER` {#docs:dev:building:build_configuration::disable_sanitizer}
In some situations, running an executable that has been built with sanitizers enabled is not support / can cause problems. Julia is an example of this.
With this flag enabled, the sanitizers are disabled for the build. | 144 | [
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|
Development | Building | Overriding Git Hash and Version | It is possible to override the Git hash and version when building from source using the `OVERRIDE_GIT_DESCRIBE` environment variable.
This is useful when building from sources that are not part of a complete Git repository (e.g., an archive file with no information on commit hashes and tags).
For example:
```bash
OVERRIDE_GIT_DESCRIBE=v0.10.0-843-g09ea97d0a9 GEN=ninja make
```
Will result in the following output when running `./build/release/duckdb`:
```text
v0.10.1-dev843 09ea97d0a9
...
``` | 136 | [
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|
Development | Building | Building Extensions | [Extensions]({% link docs/extensions/overview.md %}) can be built from source and installed from the resulting local binary. | 26 | [
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|
Development | Building | Building Extensions Using Build Flags | To build using extension flags, set the `CORE_EXTENSIONS` flag to the list of extensions that you want to be buiold.
Extension will in most cases by directly linked the resulting DuckDB executable.
For example, to build DuckDB with the [`httpfs` extension](#docs:extensions:httpfs:overview), run the following script:
```bash
GEN=ninja CORE_EXTENSIONS='httpfs' make
```
#### Special Extension Flags {#docs:dev:building:building_extensions::special-extension-flags} | 109 | [
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|
Development | Building | Building Extensions Using Build Flags | `BUILD_JEMALLOC` | When this flag is set, the [`jemalloc` extension](#docs:extensions:jemalloc) is built. | 24 | [
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Development | Building | Building Extensions Using Build Flags | `BUILD_TPCE` | When this flag is set, the [TPCE](https://www.tpc.org/tpce/) libray is built. Unlike TPC-H and TPC-DS this is not a proper extension and it's not distributed as such. Enablign this allows TPC-E enabled queries through our test suite.
#### Debug Flags {#docs:dev:building:building_extensions::debug-flags} | 83 | [
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Development | Building | Building Extensions Using Build Flags | `CRASH_ON_ASSERT` | `D_ASSERT(condition)` is used all throughout the code, these will throw an InternalException in debug builds.
With this flag enabled, when the assertion triggers it will instead directly cause a crash. | 39 | [
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Development | Building | Building Extensions Using Build Flags | `DISABLE_STRING_INLINE` | In our execution format `string_t` has the feature to “inline” strings that are under a certain length (12 bytes), this means they don't require a separate allocation.
When this flag is set, we disable this and don't inline small strings. | 52 | [
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Development | Building | Building Extensions Using Build Flags | `DISABLE_MEMORY_SAFETY` | Our data structures that are used extensively throughout the non-performance-critical code have extra checks to ensure memory safety, these checks include:
* Making sure `nullptr` is never dereferenced.
* Making sure index out of bounds accesses don't trigger a crash.
With this flag enabled we remove these checks, this is mostly done to check that the performance hit of these checks is negligible. | 79 | [
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Development | Building | Building Extensions Using Build Flags | `DESTROY_UNPINNED_BLOCKS` | When previously pinned blocks in the BufferManager are unpinned, with this flag enabled we destroy them instantly to make sure that there aren't situations where this memory is still being used, despite not being pinned. | 41 | [
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Development | Building | Building Extensions Using Build Flags | `DEBUG_STACKTRACE` | When a crash or assertion hit occurs in a test, print a stack trace.
This is useful when debugging a crash that is hard to pinpoint with a debugger attached. | 33 | [
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Development | Building | Using a CMake Configuration File | To build using a CMake configuration file, create an extension configuration file named `extension_config.cmake` with e.g., the following content:
```cmake
duckdb_extension_load(autocomplete)
duckdb_extension_load(fts)
duckdb_extension_load(inet)
duckdb_extension_load(icu)
duckdb_extension_load(json)
duckdb_extension_load(parquet)
```
Build DuckDB as follows:
```bash
GEN=ninja EXTENSION_CONFIGS="extension_config.cmake" make
```
Then, to install the extensions in one go, run:
```bash
# for release builds
cd build/release/extension/
# for debug builds
cd build/debug/extension/
# install extensions
for EXTENSION in *; do
../duckdb -c "INSTALL '${EXTENSION}/${EXTENSION}.duckdb_extension';"
done
``` | 176 | [
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|
Development | Building | Supported Platforms | DuckDB officially supports the following platforms:
| Platform name | Description |
|--------------------|--------------------------------------------|
| `linux_amd64` | Linux AMD64 |
| `linux_arm64` | Linux ARM64 |
| `osx_amd64` | macOS 12+ (Intel CPUs) |
| `osx_arm64` | macOS 12+ (Apple Silicon: M1, M2, M3 CPUs) |
| `windows_amd64` | Windows 10+ on Intel and AMD CPUs (x86_64) |
| `windows_arm64` | Windows 10+ on ARM CPUs (AArch64) | | 146 | [
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|
Development | Building | Other Platforms | There are several platforms with varying levels of support.
For some, DuckDB binaries and extensions (or a [subset of extensions](#docs:extensions:working_with_extensions::platforms)) are distributed.
For most platforms, DuckDB can often be [built from source](#::building-duckdb-from-source).
| Platform name | Description |
|------------------------|------------------------------------------------------------------------------------------------------|
| `freebsd_amd64` | FreeBSD AMD64 (x64_64) |
| `freebsd_arm64` | FreeBSD ARM64 |
| `linux_arm64_android` | Android ARM64 |
| `linux_arm64_gcc4` | Linux AMD64 with GCC 4 (e.g., CentOS 7) |
| `wasm_eh` | WebAssembly Exception Handling |
| `wasm_mvp` | WebAssembly Minimum Viable Product |
| `windows_amd64_mingw` | Windows 10+ AMD64 (x86_64) with MinGW |
| `windows_amd64_rtools` | Windows 10+ AMD64 (x86_64) for [RTools](https://cran.r-project.org/bin/windows/Rtools/) (deprecated) |
| `windows_arm64_mingw` | Windows 10+ AMD64 (x86_64) with MinGW |
32-bit architectures are officially not supported but it is possible to build DuckDB manually for some of these platforms, e.g., for [Raspberry Pi boards](#docs:dev:building:build_instructions::raspberry-pi-32-bit). | 341 | [
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|
Development | Building | Building DuckDB from Source | DuckDB can be [built from source](#docs:dev:building:build_instructions) for several other platforms such as FreeBSD, Linux distributions using musl libc, and macOS 11.
For details on free and commercial support, see the [support policy blog post](https://duckdblabs.com/news/2023/10/02/support-policy#platforms). | 77 | [
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|
Development | Building | R Package: The Build Only Uses a Single Thread | **Problem:**
By default, R compiles packages using a single thread, which causes the build to be slow.
**Solution:**
To parallelize the compilation, create or edit the `~/.R/Makevars` file, and add a line like the following:
```ini
MAKEFLAGS = -j8
```
The above will parallelize the compilation using 8 threads. On Linux/macOS, you can add the following to use all of the machine's threads:
```ini
MAKEFLAGS = -j$(nproc)
```
However, note that, the more threads that are used, the higher the RAM consumption. If the system runs out of RAM while compiling, then the R session will crash. | 151 | [
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|
Development | Building | R Package on Linux AArch64: `too many GOT entries` Build Error | **Problem:**
Building the R package on Linux running on an ARM64 architecture (AArch64) may result in the following error message:
```console
/usr/bin/ld: /usr/include/c++/10/bits/basic_string.tcc:206:
warning: too many GOT entries for -fpic, please recompile with -fPIC
```
**Solution:**
Create or edit the `~/.R/Makevars` file. This example also contains the [flag to parallelize the build](#::r-package-the-build-only-uses-a-single-thread):
```ini
ALL_CXXFLAGS = $(PKG_CXXFLAGS) -fPIC $(SHLIB_CXXFLAGS) $(CXXFLAGS)
MAKEFLAGS = -j$(nproc)
```
When making this change, also consider [making the build parallel](#::r-package-the-build-only-uses-a-single-thread). | 186 | [
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|
Development | Building | Python Package: `No module named 'duckdb.duckdb'` Build Error | **Problem:**
Building the Python package succeeds but the package cannot be imported:
```batch
cd tools/pythonpkg/
python3 -m pip install .
python3 -c "import duckdb"
```
This returns the following error message:
```console
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/duckdb/tools/pythonpkg/duckdb/__init__.py", line 4, in <module>
import duckdb.functional as functional
File "/duckdb/tools/pythonpkg/duckdb/functional/__init__.py", line 1, in <module>
from duckdb.duckdb.functional import (
ModuleNotFoundError: No module named 'duckdb.duckdb'
```
**Solution:**
The problem is caused by Python trying to import from the current working directory.
To work around this, navigate to a different directory (e.g., `cd ..`) and try running Python import again. | 197 | [
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|
Development | Building | Python Package on macOS: Building the httpfs Extension Fails | **Problem:**
The build fails on macOS when both the [`httpfs` extension](#docs:extensions:httpfs:overview) and the Python package are included:
```bash
GEN=ninja BUILD_PYTHON=1 CORE_EXTENSIONS="httpfs" make
```
```console
ld: library not found for -lcrypto
clang: error: linker command failed with exit code 1 (use -v to see invocation)
error: command '/usr/bin/clang++' failed with exit code 1
ninja: build stopped: subcommand failed.
make: *** [release] Error 1
```
**Solution:**
In general, we recommended using the nightly builds, available under GitHub main on the [installation page](https://duckdb.org/docs/installation/).
If you would like to build DuckDB from source, avoid using the `BUILD_PYTHON=1` flag unless you are actively developing the Python library.
Instead, first build the `httpfs` extension (if required), then build and install the Python package separately using pip:
```bash
GEN=ninja CORE_EXTENSIONS="httpfs" make
```
If the next line complains about pybind11 being missing, or `--use-pep517` not being supported, make sure you're using a modern version of pip and setuptools.
`python3-pip` on your OS may not be modern, so you may need to run `python3 -m pip install pip -U` first.
```bash
python3 -m pip install tools/pythonpkg --use-pep517 --user
``` | 327 | [
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