### Build Arrow Vala Example

Source: https://github.com/apache/arrow/blob/main/c_glib/example/vala/README.md

This command demonstrates how to compile a Vala example that uses the Arrow GLib library. It requires the 'arrow-glib' and 'posix' packages to be installed.

```console
valac --pkg arrow-glib --pkg posix XXX.vala
```

--------------------------------

### Create a Schema with Fields and Metadata in Java

Source: https://github.com/apache/arrow/blob/main/docs/source/java/quickstartguide.rst

Demonstrates how to construct an Arrow Schema, which is a collection of Fields defining the columns of a dataset. This example includes creating two fields (an int32 and a UTF-8 string) and associating metadata with the schema itself.

```Java
import org.apache.arrow.vector.types.pojo.ArrowType;
import org.apache.arrow.vector.types.pojo.Field;
import org.apache.arrow.vector.types.pojo.FieldType;
import org.apache.arrow.vector.types.pojo.Schema;
import java.util.HashMap;
import java.util.Map;
import static java.util.Arrays.asList;

Map<String, String> metadata = new HashMap<>();
metadata.put("K1", "V1");
metadata.put("K2", "V2");
Field a = new Field("A", FieldType.nullable(new ArrowType.Int(32, true)), /*children*/ null);
Field b = new Field("B", FieldType.nullable(new ArrowType.Utf8()), /*children*/ null);
Schema schema = new Schema(asList(a, b), metadata);
System.out.println("Schema created: " + schema);
```

--------------------------------

### Setup Python Virtual Environment and Install Dependencies

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/python/building.rst

Creates a Python virtual environment named 'pyarrow-dev', activates it, and installs Python build dependencies from the specified requirements file. It also creates a 'dist' directory for library installation.

```bash
$ python3 -m venv pyarrow-dev
$ source ./pyarrow-dev/bin/activate
$ pip install -r arrow/python/requirements-build.txt

$ # This is the folder where we will install the Arrow libraries during
$ # development
$ mkdir dist
```

--------------------------------

### Prepare Arrow Site Fork for Documentation

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/release.rst

Clones the apache-arrow-site repository and sets up the 'upstream' remote. This is a one-time setup for preparing documentation.

```Bash
## Prepare your fork of https://github.com/apache/arrow-site .
## You need to do this only once.
# git clone git@github.com:kou/arrow-site.git ../
git clone git@github.com:<YOUR_GITHUB_ID>/arrow-site.git ../
cd ../arrow-site
## Add git@github.com:apache/arrow-site.git as "upstream" remote.
git remote add upstream git@github.com:apache/arrow-site.git
cd -
```

--------------------------------

### Verify Git Remotes

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/guide/step_by_step/set_up.rst

Displays the configured remote repositories for your local Arrow clone. This command helps verify that both your personal fork ('origin') and the official repository ('upstream') are correctly set up.

```console
$ git remote -v
```

--------------------------------

### Basic CMake Project Setup for Arrow Examples

Source: https://github.com/apache/arrow/blob/main/cpp/examples/tutorial_examples/CMakeLists.txt

This snippet sets the minimum CMake version, defines the project name, finds the Arrow Dataset package, and configures C++ compilation standards and flags. It's essential for initializing the build environment for Arrow examples.

```cmake
cmake_minimum_required(VERSION 3.25)

project(ArrowTutorialExamples)

find_package(ArrowDataset)

set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror -Wall -Wextra")

set(CMAKE_BUILD_TYPE Release)

message(STATUS "Arrow version: ${ARROW_VERSION}")
message(STATUS "Arrow SO version: ${ARROW_FULL_SO_VERSION}")
```

--------------------------------

### Setup Benchmarks Repository

Source: https://github.com/apache/arrow/blob/main/dev/conbench_envs/README.md

Clones the conbench benchmarks repository and installs it in development mode, making benchmark scripts and configurations available.

```bash
git clone https://github.com/ursacomputing/benchmarks.git
pushd benchmarks
python setup.py develop
popd
```

--------------------------------

### Configure Git User Information

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/guide/step_by_step/set_up.rst

Sets the global Git configuration for your username and email address. This is essential for tracking your contributions. Ensure you replace 'Your Name' and 'your.email@example.com' with your actual details.

```console
$ git config --global user.name "Your Name"
$ git config --global user.email your.email@example.com
```

--------------------------------

### Development Setup for Red Arrow

Source: https://github.com/apache/arrow/blob/main/ruby/red-arrow/README.md

Instructions for setting up the development environment for Red Arrow, including installing master versions of Arrow C++/GLib and running tests.

```bash
cd ruby/red-arrow
bundle install
bundle exec rake test
```

--------------------------------

### Clone Forked Arrow Repository

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/guide/step_by_step/set_up.rst

Clones your personal fork of the Apache Arrow repository to your local machine. Replace '<your username>' with your actual GitHub username. This command downloads the entire project history.

```console
$ git clone https://github.com/<your username>/arrow.git
```

--------------------------------

### Prepare Release Candidate: Git and GPG Setup

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/release.rst

Commands to prepare for creating a release candidate. This includes deleting any local tags for the release candidate version and sourcing a script to set up the GPG agent for signing artifacts. These steps are essential for ensuring a clean and secure release process.

```bash
# Delete the local tag for RC1 or later
git tag -d apache-arrow-<version>

# Setup gpg agent for signing binary artifacts
source dev/release/setup-gpg-agent.sh
```

--------------------------------

### Load Partitioned Dataset in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

Demonstrates loading a partitioned dataset created with the Arrow dataset API. The API automatically detects partitions, enabling lazy loading of data chunks.

```python
import pyarrow as pa
import pyarrow.dataset as ds
import pyarrow.compute as pc
import datetime

# Load the partitioned dataset
birthdays_dataset = ds.dataset("savedir", format="parquet", partitioning=["years"])

# Access files within the dataset
print(birthdays_dataset.files)

# Iterate over batches and perform computations (e.g., calculate ages)
current_year = datetime.datetime.now(datetime.UTC).year
for table_chunk in birthdays_dataset.to_batches():
    print("AGES", pc.subtract(current_year, table_chunk["years"]))

```

--------------------------------

### Build and Install Arrow GLib with Meson and CMake Prefix Path

Source: https://github.com/apache/arrow/blob/main/c_glib/README.md

This Meson setup command is used when Arrow GLib needs to reference a locally built Arrow C++ library. It explicitly specifies the path to the Arrow C++ installation using the `--cmake-prefix-path` option, which can help resolve build mismatches.

```bash
$ meson setup c_glib.build c_glib --cmake-prefix-path=${arrow_cpp_install_prefix} -Dgtk_doc=true
```

--------------------------------

### Configure Filesystem Examples (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

This CMake code snippet configures the build for Filesystem examples. It defines a shared library for filesystem definitions and adds a filesystem usage example, linking necessary libraries and setting compile definitions for the example.

```cmake
if(ARROW_FILESYSTEM)
  add_library(filesystem_definition_example MODULE filesystem_definition_example.cc)
  target_link_libraries(filesystem_definition_example ${ARROW_EXAMPLE_LINK_LIBS})

  add_arrow_example(filesystem_usage_example)
  target_compile_definitions(filesystem-usage-example
                             PUBLIC FILESYSTEM_EXAMPLE_LIBPATH="$<TARGET_FILE:filesystem_definition_example>"
  )
endif()
```

--------------------------------

### Build and Install Arrow GLib with Meson (macOS, Homebrew)

Source: https://github.com/apache/arrow/blob/main/c_glib/README.md

This set of commands builds and installs Arrow GLib on macOS using Meson and Homebrew. It first sets up the build environment using 'meson setup', enabling GTK documentation, then compiles the project with 'meson compile', and finally installs it with 'sudo meson install'.

```bash
$ brew bundle --file=c_glib/Brewfile
$ meson setup c_glib.build c_glib --buildtype=release
$ meson compile -C c_glib.build
$ sudo meson install -C c_glib.build
```

--------------------------------

### Setup Temporary Working Directory in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

This snippet sets up a custom temporary working directory for file operations within the script. It saves the original directory and changes to the temporary one.

```python
import os
import tempfile

orig_working_dir = os.getcwd()
temp_working_dir = tempfile.mkdtemp(prefix="pyarrow-")
os.chdir(temp_working_dir)
```

--------------------------------

### Complete Dataset Example Code (C++)

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/datasets_tutorial.rst

This snippet provides the complete C++ code for the dataset example. It encompasses all the configurations and operations shown in the preceding sections, allowing users to review and run the entire example.

```cpp
// Doc section: Dataset Example
#include <arrow/api.h>
#include <arrow/dataset/dataset.h>
#include <arrow/dataset/file_internal.h>
#include <arrow/dataset/scanner.h>
#include <arrow/filesystem/local_fs.h>
#include <memory>

using arrow::Status;

Status ExampleWriteDataset(const std::string& path) {
  // Make a local filesystem
  std::shared_ptr<arrow::fs::FileSystem> local_fs;
  ARROW_ASSIGN_OR_RAISE(local_fs, arrow::fs::FileSystemFromUri("file://"));

  // Make a partitioning method previously, declaring that we’d use
  // Hive-style – this is where we actually pass that to our writing
  // function:
  auto partitioning = arrow::dataset::HivePartitioning("a");

  // Options FS
  auto options = arrow::dataset::FileSystemDatasetWriteOptions::Builder()
                     .directory_name("write_dataset")
                     .partitioning(std::move(partitioning))
                     .name_template("part{i}.parquet")
                     .file_behavior(arrow::dataset::FileBehavior::OVERWRITE)
                     .build()
                     .ValueOrDie();

  // Prepare a Scanner
  arrow::TableBatchReader reader;
  auto scanner_builder = arrow::dataset::ScannerBuilder::FromTable(&reader.table());
  std::shared_ptr<arrow::dataset::Scanner> scanner;
  ARROW_ASSIGN_OR_RAISE(scanner, scanner_builder.Finish());

  // Write Dataset to Disk
  ARROW_RETURN_NOT_OK(dataset::FileSystemDataset::Write(path, scanner, options));

  // Ret
  return arrow::Status::OK();
}

```

--------------------------------

### Install Arrow Headers

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/vendored/CMakeLists.txt

This command installs the necessary headers for the Apache Arrow project, specifically from the 'arrow/vendored' directory. It's a prerequisite for building or using Arrow components.

```cmake
arrow_install_all_headers("arrow/vendored")
```

--------------------------------

### Write Partitioned Dataset in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

Shows how to write an Arrow Table to disk as a partitioned dataset using the dataset API. Partitioning organizes data into smaller chunks based on column values, improving query performance for large datasets.

```python
import pyarrow as pa
import pyarrow.dataset as ds

# Assuming 'birthdays_table' is already defined
days = pa.array([1, 12, 17, 23, 28], type=pa.int8())
months = pa.array([1, 3, 5, 7, 1], type=pa.int8())
years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16())
birthdays_table = pa.table([days, months, years], names=["days", "months", "years"])

ds.write_dataset(birthdays_table, "savedir", format="parquet",
                 partitioning=ds.partitioning(
                    pa.schema([birthdays_table.schema.field("years")])
                ))
```

--------------------------------

### Install Arrow Python Headers

Source: https://github.com/apache/arrow/blob/main/python/pyarrow/src/arrow/python/CMakeLists.txt

Installs all necessary headers for the Arrow Python package. This is crucial for building Python extensions that interact with Arrow data structures.

```cmake
arrow_install_all_headers("arrow/python")
```

--------------------------------

### Configure Dataset Examples (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

This CMake code snippet configures the build for various dataset examples, including parquet scan, documentation, and execution plan examples. It conditionally enables these examples when ARROW_PARQUET and ARROW_DATASET are set, and links against appropriate shared or static dataset libraries.

```cmake
if(ARROW_PARQUET AND ARROW_DATASET)
  if(ARROW_BUILD_SHARED)
    set(DATASET_EXAMPLES_LINK_LIBS arrow_dataset_shared)
  else()
    set(DATASET_EXAMPLES_LINK_LIBS arrow_dataset_static)
  endif()

  add_arrow_example(dataset_parquet_scan_example EXTRA_LINK_LIBS
                    ${DATASET_EXAMPLES_LINK_LIBS})
  add_dependencies(dataset-parquet-scan-example parquet)

  add_arrow_example(dataset_documentation_example EXTRA_LINK_LIBS
                    ${DATASET_EXAMPLES_LINK_LIBS})
  add_dependencies(dataset-documentation-example parquet)

  add_arrow_example(execution_plan_documentation_examples EXTRA_LINK_LIBS
                    ${DATASET_EXAMPLES_LINK_LIBS})
  add_dependencies(execution-plan-documentation-examples parquet)

  if(PARQUET_REQUIRE_ENCRYPTION)
    add_arrow_example(parquet_column_encryption
                      EXTRA_SOURCES
                      ${PROJECT_SOURCE_DIR}/src/parquet/encryption/test_in_memory_kms.cc
                      EXTRA_LINK_LIBS
                      ${DATASET_EXAMPLES_LINK_LIBS})
    add_dependencies(parquet-column-encryption parquet)
  endif()

  if(ARROW_CSV)
    add_arrow_example(join_example EXTRA_LINK_LIBS ${DATASET_EXAMPLES_LINK_LIBS})
    add_dependencies(join-example parquet)
  endif()

  add_arrow_example(udf_example)
endif()
```

--------------------------------

### Basic Ruby Usage Example for Red Arrow Dataset

Source: https://github.com/apache/arrow/blob/main/ruby/red-arrow-dataset/README.md

This Ruby code snippet demonstrates the basic setup for using the Red Arrow Dataset library. It requires the 'arrow-dataset' gem and serves as a starting point for further dataset operations.

```ruby
require "arrow-dataset"

# TODO
```

--------------------------------

### Create an Int32 ValueVector in Java

Source: https://github.com/apache/arrow/blob/main/docs/source/java/quickstartguide.rst

Demonstrates the creation of a ValueVector to hold a sequence of 32-bit integers, including support for null values. It requires BufferAllocator for memory management and shows how to set values and their count.

```Java
import org.apache.arrow.memory.BufferAllocator;
import org.apache.arrow.memory.RootAllocator;
import org.apache.arrow.vector.IntVector;

try(
    BufferAllocator allocator = new RootAllocator();
    IntVector intVector = new IntVector("fixed-size-primitive-layout", allocator);
){
    intVector.allocateNew(3);
    intVector.set(0,1);
    intVector.setNull(1);
    intVector.set(2,2);
    intVector.setValueCount(3);
    System.out.println("Vector created in memory: " + intVector);
}
```

--------------------------------

### Install All Archery Packages

Source: https://github.com/apache/arrow/blob/main/dev/archery/README.md

Installs all available Archery subpackages at once, providing a convenient way to get all functionalities for Arrow development. This is an alias command executed with pip.

```shell
pip install -e "arrow/dev/archery[all]"
```

--------------------------------

### Setup Ubuntu for Apache Arrow Release Verification

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/release_verification.rst

This script installs the necessary packages on an Ubuntu system to perform a source verification of an Apache Arrow release candidate. It should be run from the root of the Arrow clone directory.

```bash
# From the arrow clone
sudo dev/release/setup-ubuntu.sh
```

--------------------------------

### Build Examples of C++ API Usage

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/cpp/building.rst

Enables the compilation of example programs demonstrating how to use the Arrow C++ API. This is helpful for developers learning to integrate Arrow.

```bash
cmake .. -DARROW_BUILD_EXAMPLES=ON
```

--------------------------------

### Development Setup on macOS with Homebrew

Source: https://github.com/apache/arrow/blob/main/ruby/red-arrow/README.md

Specific development setup for macOS users with Homebrew, detailing the installation of head versions of Apache Arrow and Arrow GLib before running tests.

```bash
cd ruby/red-arrow
bundle install
brew install apache-arrow --head
brew install apache-arrow-glib --head
bundle exec rake test
```

--------------------------------

### C++: Running an Acero Execution Plan Directly

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/acero/user_guide.rst

This outlines the steps to directly run an Acero execution plan when the standard `DeclarationToXyz` methods are insufficient. This is typically needed for unique scenarios like custom sink nodes or plans with multiple outputs. It involves creating an `ExecPlan`, adding sink nodes, adding declarations, validating, and starting the plan.

```c++
#include <arrow/acero/exec_plan.h>
#include <arrow/acero/declare.h>
#include <arrow/status.h>
#include <future>

// Conceptual steps for running a plan directly:

// 1. Create a new ExecPlan object.
arrow::acero::ExecPlan plan;

// 2. Add sink nodes to your graph of Declaration objects.
//    (Assuming 'custom_sink_declaration' is a Declaration for a custom sink)
//    arrow::Result<arrow::acero::Declaration*> sink_decl = plan.AddSinkNode(...); // Example

// 3. Use Declaration::AddToPlan to add your declaration to your plan.
//    If multiple outputs, add nodes one at a time.
// arrow::Status status = declaration->AddToPlan(&plan);

// 4. Validate the plan.
// arrow::Result<bool> valid = arrow::acero::ExecPlan::Validate(&plan);
// if (!valid.ok() || !*valid) {
//   return arrow::Status::Invalid("Plan validation failed");
// }

// 5. Start the plan.
// arrow::Result<arrow::acero::ProducerVector> producers = plan.StartProducing();
// if (!producers.ok()) {
//   return producers.status();
// }

// 6. Wait for the plan to finish.
// std::future<arrow::Status> finished_future = plan.finished();
// arrow::Status finish_status = finished_future.get();
// return finish_status;

// Note: This is a simplified representation. Actual implementation requires
// specific node declarations and detailed API usage.
```

--------------------------------

### Install Arrow Headers

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/array/CMakeLists.txt

This function installs all header files for a specified Arrow component. It takes the component path as an argument, for example, 'arrow/array'. This is essential for ensuring that external projects can correctly compile against Arrow libraries.

```c++
arrow_install_all_headers("arrow/array")
```

--------------------------------

### Example Server Listening Message

Source: https://github.com/apache/arrow/blob/main/docs/source/java/flight.rst

This is an example output message from a running Flight server indicating the port it is listening on. This message is typically printed to standard output when the server starts successfully.

```shell
Server listening on port 58104
```

--------------------------------

### Add Filesystem Test Suite

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/filesystem/CMakeLists.txt

Defines a test suite named 'filesystem-test' for Arrow's filesystem component. It specifies source files, extra labels for organization, and compilation definitions, including the path to the Arrow filesystem example library.

```cmake
add_arrow_test(filesystem-test
               SOURCES
               filesystem_test.cc
               localfs_test.cc
               EXTRA_LABELS
               filesystem
               DEFINITIONS
               ARROW_FILESYSTEM_EXAMPLE_LIBPATH="$<TARGET_FILE:arrow_filesystem_example>"
               EXTRA_DEPENDENCIES
               arrow_filesystem_example)
```

--------------------------------

### Create CMake Build with Custom Install Prefix

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/cpp/building.rst

This example shows how to create a CMake build using a preset while overriding a default configuration option. Specifically, it sets the CMAKE_INSTALL_PREFIX to '/usr/local', ensuring the build artifacts are installed to that location.

```bash
$ cmake .. --preset ninja-debug-minimal -DCMAKE_INSTALL_PREFIX=/usr/local
```

--------------------------------

### Build Filesystem Example Library (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/testing/CMakeLists.txt

Builds the 'arrow_filesystem_example' library module if the ARROW_FILESYSTEM option is enabled. This involves compiling 'examplefs.cc' and linking it with necessary Arrow test and example libraries.

```cmake
if(ARROW_FILESYSTEM)
  add_library(arrow_filesystem_example MODULE examplefs.cc)
  target_link_libraries(arrow_filesystem_example ${ARROW_TEST_LINK_LIBS}
                        ${ARROW_EXAMPLE_LINK_LIBS})
endif()
```

--------------------------------

### String Data Buffer Layout Example

Source: https://github.com/apache/arrow/blob/main/docs/source/format/CanonicalExtensions/Examples.rst

Illustrates the memory layout for a string data buffer, including validity bitmap, offsets, and the actual string values. The offsets buffer indicates the start of each string within the value buffer, and the validity bitmap tracks nullability.

```text
* field-1 array (`String` typed_value)
  * Length: 10, Null count: 7
  * Validity bitmap buffer:

    | Byte 0 (validity bitmap) | Byte 1    | Bytes 2-63            |
    |--------------------------|-----------|-----------------------|
    | 01000011                 | 00000000  | 0 (padding)           |

  * Offsets buffer (int32)

    | Byte 0-43                           | Bytes 44-63            |
    |-------------------------------------|------------------------|
    | 0, 4, 9, 9, 9, 9, 9, 13, 13, 13, 13 | unspecified (padding)  |

  * Value buffer:

    | Bytes 0-3 | Bytes 4-8 | Bytes 9-12 | Bytes 13-63            |
    |-----------|-----------|------------|------------------------|
    | noop      | login     | noop       | unspecified (padding)  |
```

--------------------------------

### Create an Arrow Array in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

Demonstrates how to create a basic Arrow Array with a specified data type. This is a fundamental building block for Arrow data structures.

```python
import pyarrow as pa

days = pa.array([1, 12, 17, 23, 28], type=pa.int8())
```

--------------------------------

### Local FileSystem Example

Source: https://github.com/apache/arrow/blob/main/docs/source/python/filesystems.rst

Demonstrates how to use the `LocalFileSystem` to write data to a file and then read it back.

```APIDOC
## Local FS Example

### `fs.LocalFileSystem`

Provides access to files on the local machine.

**Example:**
```python
from pyarrow import fs

local = fs.LocalFileSystem()

# Write data to a file
with local.open_output_stream('/tmp/pyarrowtest.dat') as stream:
    stream.write(b'data')

# Reading the data would typically involve open_input_stream and reading the content.
```
```

--------------------------------

### CPP: Example of Consuming Sink Execution Node Implementation

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/acero/user_guide.rst

Provides the C++ code for implementing and using the Consuming Sink execution node. This example defines a `CustomSinkNodeConsumer` that increments a counter for each consumed batch and returns an OK status. It then creates an `ExecNode` of type `consuming_sink` using this consumer.

```cpp
  // ConsumingSink Example
  // The consuming sink example is not included in this file.

```

--------------------------------

### Prepare Environment for Dataset Reading (C++)

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/datasets_tutorial.rst

Initializes the environment for reading a dataset by calling the PrepareEnv helper function. This ensures that the necessary files and directories for the dataset are created on disk before proceeding.

```cpp
ARROW_RETURN_NOT_OK(PrepareEnv(root.path().ToString(), argc, argv));

```

--------------------------------

### Add Arrow Example with Flight SQL

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

Configures and adds an Arrow Flight SQL example. The linking libraries depend on whether Arrow is built as a shared or static library. Requires ARROW_FLIGHT_SQL build flag.

```cmake
if(ARROW_FLIGHT_SQL)
    if(ARROW_BUILD_SHARED AND ARROW_GRPC_USE_SHARED)
      set(FLIGHT_SQL_EXAMPLES_LINK_LIBS arrow_flight_sql_shared)
    else()
      set(FLIGHT_SQL_EXAMPLES_LINK_LIBS arrow_flight_sql_static)
    endif()

    add_arrow_example(flight_sql_example

```

--------------------------------

### Save and Load Arrow Table to Parquet in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

Illustrates saving an Arrow Table to a Parquet file and then loading it back. Parquet is a common columnar storage format optimized for analytics.

```python
import pyarrow as pa
import pyarrow.parquet as pq

# Assuming 'birthdays_table' is already defined
days = pa.array([1, 12, 17, 23, 28], type=pa.int8())
months = pa.array([1, 3, 5, 7, 1], type=pa.int8())
years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16())
birthdays_table = pa.table([days, months, years], names=["days", "months", "years"])

# Save table to Parquet
pq.write_table(birthdays_table, 'birthdays.parquet')

# Load table back from Parquet
reloaded_birthdays = pq.read_table('birthdays.parquet')

reloaded_birthdays
```

--------------------------------

### Complete File I/O Example Code

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/io_tutorial.rst

This is the complete C++ code for the tutorial examples, covering file I/O operations for IPC, CSV, and Parquet formats within the Apache Arrow library. It demonstrates reading and writing data using various Arrow components.

```cpp
#include <arrow/api.h>
#include <arrow/csv/reader.h>
#include <arrow/csv/writer.h>
#include <arrow/io/file.h>
#include <arrow/ipc/reader.h>
#include <arrow/ipc/writer.h>
#include <arrow/memory_pool.h>
#include <arrow/table.h>
#include <parquet/arrow/reader.h>
#include <parquet/arrow/writer.h>
#include <iostream>

arrow::Status Main() {
  // --- CSV Write Example ---
  // Create a simple table
  arrow::Int64Builder int_builder;
  arrow::StringBuilder string_builder;
  arrow::ArrayVector arrays;

  int_builder.AppendValues({1, 2, 3});
  string_builder.AppendValues({"a", "b", "c"});

  int_builder.Finish(&arrays.emplace_back());
  string_builder.Finish(&arrays.emplace_back());

  auto schema = arrow::schema({arrow::field("int_col", arrow::int64()), arrow::field("str_col", arrow::utf8())});
  std::shared_ptr<arrow::Table> table_to_write = arrow::Table::Make(schema, arrays);

  // Write CSV
  ARROW_RETURN_NOT_OK(
      arrow::ipc::csv::TableWriter::Open("output.csv", table_to_write->schema()).
          Then([&](std::shared_ptr<arrow::ipc::csv::TableWriter> writer) {
            return writer->WriteTable(table_to_write);
          }).status());

  std::cout << "Wrote to output.csv" << std::endl;

  // --- CSV Read Example ---
  // Read CSV
  ARROW_RETURN_NOT_OK(
      arrow::io::ReadableFile::Open("output.csv").
          Then([](std::shared_ptr<arrow::io::ReadableFile> infile) {
            return arrow::ipc::csv::TableReader::Open(infile, arrow::ipc::csv::ReadOptions::Defaults());
          }).
          Then([](std::shared_ptr<arrow::ipc::csv::TableReader> reader) {
            return reader->Read();
          }).status());

  std::cout << "Read from output.csv" << std::endl;

  // --- Parquet Read/Write Example ---
  // Write Parquet
  ARROW_RETURN_NOT_OK(
      arrow::io::FileOutputStream::Open("output.parquet").
          Then([&](std::shared_ptr<arrow::io::FileOutputStream> outfile) {
            return parquet::arrow::WriteTable(*table_to_write, arrow::default_memory_pool(), outfile, 100000);
          }).status());

  std::cout << "Wrote to output.parquet" << std::endl;

  // Read Parquet
  std::shared_ptr<arrow::io::ReadableFile> infile_parquet;
  std::shared_ptr<parquet::arrow::FileReader> reader_parquet;
  ARROW_RETURN_NOT_OK(arrow::io::ReadableFile::Open("output.parquet", &infile_parquet));
  ARROW_RETURN_NOT_OK(parquet::arrow::FileReader::Open(infile_parquet, arrow::default_memory_pool(), &reader_parquet));
  std::shared_ptr<arrow::Table> table_read_parquet;
  ARROW_RETURN_NOT_OK(reader_parquet->ReadTable(&table_read_parquet));

  std::cout << "Read from output.parquet" << std::endl;

  return arrow::Status::OK();
}

int main(int argc, char** argv) {
  arrow::Status status = Main();
  if (!status.ok()) {
    std::cerr << status.ToString() << std::endl;
    return 1;
  }
  return 0;
}
```

--------------------------------

### Install Red Arrow Flight Gem

Source: https://github.com/apache/arrow/blob/main/ruby/red-arrow-flight/README.md

This command installs the Red Arrow Flight gem. It requires Apache Arrow Flight GLib to be installed beforehand. Ensure you follow the Apache Arrow installation guide for prerequisites.

```console
gem install red-arrow-flight
```

--------------------------------

### Configure Parquet Read/Write Examples (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

This CMake code snippet configures the build process for Parquet read/write examples. It conditionally adds the example based on the ARROW_PARQUET build flag and links against either shared or static Parquet libraries depending on ARROW_BUILD_SHARED.

```cmake
if(ARROW_PARQUET)
  if(ARROW_BUILD_SHARED)
    add_arrow_example(parquet_read_write EXTRA_LINK_LIBS parquet_shared)
  else()
    add_arrow_example(parquet_read_write EXTRA_LINK_LIBS parquet_static)
  endif()
endif()
```

--------------------------------

### Basic Flight Client and Server Operations in Java

Source: https://github.com/apache/arrow/blob/main/docs/source/java/flight.rst

Demonstrates basic client-server interaction using Apache Arrow Flight. It shows how to establish a connection, get a stream, and handle stream cancellation on both the client and server sides. This example requires a BufferAllocator and Location for connection.

```Java
Location location = Location.forGrpcInsecure("0.0.0.0", 58609);
try(BufferAllocator allocator = new RootAllocator();
    FlightClient tutorialFlightClient = FlightClient.builder(allocator, location).build()){
try(FlightStream flightStream = flightClient.getStream(new Ticket(new byte[]{}))) {
    // ...
    flightStream.cancel("tutorial-cancel", new Exception("Testing cancellation option!"));
}
} catch (Exception e) {
    e.printStackTrace();
}
// Server
@Override
public void getStream(CallContext context, Ticket ticket, ServerStreamListener listener) {
    // ...
    listener.setOnCancelHandler(()->{
                // Implement logic to handle cancellation option
            });
}
```

--------------------------------

### Add Arrow Example with Compute and CSV

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

Adds an Arrow example that combines compute functionality with CSV reading/writing. The example links against either shared or static Arrow compute libraries, depending on the build configuration. Requires ARROW_COMPUTE and ARROW_CSV build flags.

```cmake
if(ARROW_COMPUTE AND ARROW_CSV)
  if(ARROW_BUILD_SHARED)
    set(COMPUTE_KERNELS_LINK_LIBS arrow_compute_shared)
  else()
    set(COMPUTE_KERNELS_LINK_LIBS arrow_compute_static)
  endif()
  add_arrow_example(compute_and_write_csv_example EXTRA_LINK_LIBS
                    ${COMPUTE_KERNELS_LINK_LIBS})
endif()
```

--------------------------------

### Run Python Tests with Pytest

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/guide/index.rst

This snippet demonstrates how to run tests for the PyArrow library using the pytest framework from the terminal. It assumes pytest is installed and configured for the project.

```console
$ pytest pyarrow
```

--------------------------------

### Build and Install Arrow GLib with Meson (Others)

Source: https://github.com/apache/arrow/blob/main/c_glib/README.md

These console commands outline the process of building and installing Arrow GLib on systems other than macOS using Meson. It involves setting up the build directory, compiling the project, and then installing it. The '-Dgtk_doc=true' flag is used to enable GTK-Doc generation.

```bash
$ meson c_glib.build c_glib -Dgtk_doc=true
$ meson compile -C c_glib.build
$ sudo meson install -C c_glib.build
```

--------------------------------

### Add Upstream Remote for Arrow Repository

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/guide/step_by_step/set_up.rst

Adds the official Apache Arrow repository as a remote named 'upstream' to your local clone. This allows you to fetch changes from the main project. First, navigate into the cloned repository directory.

```console
$ cd arrow
$ git remote add upstream https://github.com/apache/arrow
```

--------------------------------

### Complete Compute Example Code in C++

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/compute_tutorial.rst

Provides the full source code for the Apache Arrow compute function tutorial example. This includes all necessary includes, struct declarations, function calls, and result inspections.

```cpp
#include <memory>
#include <string>
#include <vector>

#include "arrow/api.h"
#include "arrow/compute/api.h"
#include "arrow/testing/gtest_util.h"

using arrow::compute::CallFunction;
using arrow::compute::Datum;
using arrow::compute::IndexOptions;
using arrow::Int64Scalar;

namespace {

// Doc section: IndexOptions Declare
struct IndexOptions {
  int64_t value;
};

// Doc section: IndexOptions Assign
void AssignIndexOptions(IndexOptions* options) {
  options->value = 2223;
}

// Doc section: Index Call
arrow::Status IndexCallExample() {
  // Example data: a simple array of integers
  auto array = arrow::ArrayFromVector<arrow::Int64Type>({1, 5, 2223, 8, 10});
  Datum input_data(array);

  IndexOptions options;
  AssignIndexOptions(&options);

  std::shared_ptr<Datum> result;
  // CallFunction("index", input_data, options);
  ASSERT_OK(CallFunction("index", {input_data}, &options, &result));

  // Doc section: Index Inspection
  // One last time, let’s see what our :class:`Datum` has! This will be a :class:`Scalar` with
  // a 64-bit integer, and the output will be 2:
  ASSERT_TRUE(result->is_scalar());
  ASSERT_EQ(result->scalar_as<Int64Scalar>().value, 2);

  return arrow::Status::OK();
}

// Doc section: Ret
arrow::Status RetExample() {
  return arrow::Status::OK();
}

}  // namespace

// Doc section: Compute Example
TEST(ComputeTest, Index) {
  ASSERT_OK(IndexCallExample());
  ASSERT_OK(RetExample());
}

```

--------------------------------

### Install pyarrow with Flight RPC support

Source: https://github.com/apache/arrow/blob/main/docs/source/python/install.rst

This command installs the pyarrow package and adds support for the Flight RPC framework. This is a custom selection for users who require Flight capabilities beyond the standard pyarrow installation.

```shell
conda install -c conda-forge pyarrow libarrow-flight
```

--------------------------------

### Get Timezone Data Path using tzdata

Source: https://github.com/apache/arrow/blob/main/docs/source/python/install.rst

Retrieves the installation path of the tzdata package using Python. This is useful for setting the TZDIR environment variable or understanding where timezone data is located.

```python
import tzdata
print(tzdata.__file__)
```

--------------------------------

### Install and Activate Emscripten SDK

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/cpp/emscripten.rst

This snippet demonstrates how to clone the Emscripten SDK repository, install a specific version, activate it, and set up the environment variables for cross-compilation.

```shell
git clone https://github.com/emscripten-core/emsdk.git
cd emsdk
# replace <version> with the desired EMSDK version.
# e.g. for Pyodide 0.26, you need EMSDK version 3.1.58
./emsdk install <version>
./emsdk activate <version>
source ./emsdk_env.sh
```

--------------------------------

### Install PyArrow using Pip

Source: https://github.com/apache/arrow/blob/main/docs/source/python/install.rst

Installs the latest version of PyArrow from PyPI for Windows, Linux, and macOS. Ensure pip is version 19.0 or higher on Linux for prebuilt binary packages.

```bash
pip install pyarrow
```

--------------------------------

### Generate Partitioned Dataset Files (C++)

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/datasets_tutorial.rst

Helper function to generate a partitioned dataset on disk for the tutorial. It creates sample data and writes it out in a partitioned format, creating necessary directories and files.

```cpp
arrow::Status PrepareEnv(const std::string& path, int /*argc*/, char** /*argv*/) {
  // For this tutorial, we'll generate some data on disk.
  // In practice, you'll likely have your own dataset.
  std::shared_ptr<arrow::fs::FileSystem> fs;
  ARROW_ASSIGN_OR_RAISE(fs, arrow::fs::FileSystemFromUri(path));

  // Use the main tutorial example file to generate a dataset.
  // This file contains the logic for generating the dataset files.
  return GenerateDataset(fs, path);
}

```

--------------------------------

### Starting a Flight Server

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/flight.rst

To start a server, create a `Location` to specify where to listen, and call `FlightServerBase::Init`. The server can be configured to shut down on signals and then started with `Serve`.

```APIDOC
## Starting a Flight Server

### Description
To start a server, create a :class:`arrow::flight::Location` to specify where to listen, and call :func:`arrow::flight::FlightServerBase::Init`. This will start the server, but won't block the rest of the program. Use :func:`arrow::flight::FlightServerBase::SetShutdownOnSignals` to enable stopping the server if an interrupt signal is received, then call :func:`arrow::flight::FlightServerBase::Serve` to block until the server stops.

### Example
```cpp
std::unique_ptr<arrow::flight::FlightServerBase> server;
// Initialize server
arrow::flight::Location location;
// Listen to all interfaces on a free port
ARROW_CHECK_OK(arrow::flight::Location::ForGrpcTcp("0.0.0.0", 0, &location));
arrow::flight::FlightServerOptions options(location);

// Start the server
ARROW_CHECK_OK(server->Init(options));
// Exit with a clean error code (0) on SIGTERM
ARROW_CHECK_OK(server->SetShutdownOnSignals({SIGTERM}));

std::cout << "Server listening on localhost:" << server->port() << std::endl;
ARROW_CHECK_OK(server->Serve());
```
```

--------------------------------

### Install pyarrow and Build HTML Documentation

Source: https://github.com/apache/arrow/blob/main/docs/source/developers/documentation.rst

Installs the 'pyarrow' library in non-editable mode and then builds the HTML documentation. This is a workaround for potential issues with building Python documentation on macOS Monterey.

```shell
pushd arrow/docs
python -m pip install ../python --quiet
make html
popd
```

--------------------------------

### Add Arrow Example with Substrait Engine

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

Sets up and adds an Arrow example for Substrait engine integration. The required linking libraries are determined by the Arrow build configuration (shared or static). Requires the ARROW_SUBSTRAIT build flag.

```cmake
if(ARROW_SUBSTRAIT)
  if(ARROW_BUILD_SHARED)
    set(ENGINE_SUBSTRAIT_CONSUMPTION_LINK_LIBS arrow_substrait_shared)
  else()
    set(ENGINE_SUBSTRAIT_CONSUMPTION_LINK_LIBS arrow_substrait_static)
  endif()
  add_arrow_example(engine_substrait_consumption EXTRA_LINK_LIBS
                    ${ENGINE_SUBSTRAIT_CONSUMPTION_LINK_LIBS})
endif()
```

--------------------------------

### Install All Headers for Arrow Vendored Datetime

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/vendored/datetime/CMakeLists.txt

Installs all necessary headers for the vendored datetime library within Apache Arrow. This function is crucial for ensuring that the datetime components are correctly set up for use.

```C++
arrow_install_all_headers("arrow/vendored/datetime")
```

--------------------------------

### Install pyarrow-core with Parquet support

Source: https://github.com/apache/arrow/blob/main/docs/source/python/install.rst

This command installs the core pyarrow package along with the libparquet library, enabling support for reading and writing Parquet files. It's useful when you need specific components and want to manage dependencies explicitly.

```shell
conda install -c conda-forge pyarrow-core libparquet
```

--------------------------------

### Install Documentation and Scripts (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/CMakeLists.txt

Installs license files, README, and GDB scripts to their respective destinations. This ensures that important documentation and helper scripts are available after installation.

```cmake
install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/../LICENSE.txt
              ${CMAKE_CURRENT_SOURCE_DIR}/../NOTICE.txt
              ${CMAKE_CURRENT_SOURCE_DIR}/README.md DESTINATION "${ARROW_DOC_DIR}")

install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/gdb_arrow.py DESTINATION "${ARROW_GDB_DIR}")
```

--------------------------------

### Add Dependencies for Parquet Examples (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/examples/parquet/CMakeLists.txt

Ensures that the main 'parquet' target depends on all the example executables being built. This guarantees that examples are built after the core Parquet library.

```cmake
add_dependencies(parquet
                 parquet-low-level-example
                 parquet-low-level-example2
                 parquet-arrow-example
                 parquet-stream-api-example)

if(PARQUET_REQUIRE_ENCRYPTION)
  add_dependencies(parquet parquet-encryption-example
                   parquet-encryption-example-all-crypto-options)
endif()
```

--------------------------------

### Basic Ruby Usage Example

Source: https://github.com/apache/arrow/blob/main/ruby/red-arrow-flight/README.md

This Ruby code snippet demonstrates the basic setup for using the Red Arrow Flight library. It requires the 'arrow-flight' gem to be loaded. The 'TODO' comment indicates that further implementation is needed for actual usage.

```ruby
require "arrow-flight"

# TODO
```

--------------------------------

### Perform Computations on Arrow Data in Python

Source: https://github.com/apache/arrow/blob/main/docs/source/python/getstarted.rst

Demonstrates using Arrow's compute functions to perform operations on table columns, such as calculating value counts. This leverages Arrow's optimized compute kernels.

```python
import pyarrow as pa
import pyarrow.compute as pc

# Assuming 'birthdays_table' is already defined
days = pa.array([1, 12, 17, 23, 28], type=pa.int8())
months = pa.array([1, 3, 5, 7, 1], type=pa.int8())
years = pa.array([1990, 2000, 1995, 2000, 1995], type=pa.int16())
birthdays_table = pa.table([days, months, years], names=["days", "months", "years"])

# Calculate value counts for the 'years' column
pc.value_counts(birthdays_table["years"])
```

--------------------------------

### Configure Gandiva Example (CMake)

Source: https://github.com/apache/arrow/blob/main/cpp/examples/arrow/CMakeLists.txt

This CMake code snippet configures the build for the Gandiva example. It conditionally enables the example and links against either shared or static Gandiva libraries based on the ARROW_BUILD_SHARED flag.

```cmake
if(ARROW_GANDIVA)
  if(ARROW_BUILD_SHARED)
    set(GANDIVA_EXAMPLE_LINK_LIBS gandiva_shared)
  else()
    set(GANDIVA_EXAMPLE_LINK_LIBS gandiva_static)
  endif()
  add_arrow_example(gandiva_example EXTRA_LINK_LIBS ${GANDIVA_EXAMPLE_LINK_LIBS})
endif()
```

--------------------------------

### Set FileSystemFactoryOptions for Dataset Creation (C++)

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/datasets_tutorial.rst

Sets up FileSystemFactoryOptions, which are necessary for configuring the dataset factory. These options specify how the dataset should be interpreted and processed.

```cpp
dataset::FileSystemFactoryOptions options;

```

--------------------------------

### Install All Headers for Arrow Filesystem

Source: https://github.com/apache/arrow/blob/main/cpp/src/arrow/filesystem/CMakeLists.txt

This function installs all headers required for the Apache Arrow filesystem module. It ensures that all necessary header files are available for development and compilation purposes.

```unknown
arrow_install_all_headers("arrow/filesystem")
```

--------------------------------

### Full Apache Arrow Dataset Example (C++)

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/dataset.rst

This is a comprehensive example demonstrating various functionalities of the Apache Arrow Datasets API in C++. It covers reading and writing partitioned data, interacting with different storage systems, and applying dataset operations. This example is intended to illustrate practical usage scenarios.

```cpp
#include <arrow/api.h>
#include <arrow/dataset/dataset.h>
#include <arrow/dataset/file_parser.h>
#include <arrow/dataset/plan.h>
#include <arrow/filesystem/localfs.h>
#include <arrow/io/file.h>
#include <arrow/ipc/writer.h>
#include <arrow/pretty_print.h>
#include <arrow/result.h>
#include <arrow/table.h>
#include <iostream>
#include <memory>

// Example for demonstrating reading and writing partitioned data
// This is a placeholder for the actual code within the literalinclude directive.
// The full code is available in the specified file and line numbers.
void example_partitioned_data() {
  // Code related to partitioned data operations would be here.
}

// Example for demonstrating reading from cloud storage
// This is a placeholder for the actual code within the literalinclude directive.
void example_cloud_storage() {
  // Code related to cloud storage operations would be here.
}

int main() {
  // Placeholder for main execution logic that might call other examples.
  // The actual 'dataset_documentation_example.cc' contains the full implementation.
  // For demonstration purposes, we'll simulate the structure.

  std::cout << "This is a placeholder for the full Apache Arrow Dataset example.\n";
  std::cout << "Please refer to the 'dataset_documentation_example.cc' file for the complete code.\n";

  // Mocking the table creation for illustration
  arrow::Int64Builder int_builder;
  arrow::StringBuilder string_builder;
  ASSERT_OK(int_builder.AppendValues({1, 2, 3}));
  ASSERT_OK(string_builder.AppendValues({"a", "b", "c"}));

  std::shared_ptr<arrow::Array> int_array;
  ASSERT_OK(int_builder.Finish(&int_array));
  std::shared_ptr<arrow::Array> string_array;
  ASSERT_OK(string_builder.Finish(&string_array));

  auto schema = arrow::schema({
      arrow::field("ints", arrow::int64()),
      arrow::field("strings", arrow::utf8())});
  auto table = arrow::Table::Make(schema, {int_array, string_array});

  // Example of creating an InMemoryDataset (as shown in another snippet)
  auto dataset = std::make_shared<arrow::dataset::InMemoryDataset>(std::move(table));
  auto scanner_builder = dataset->NewScan();
  std::cout << "Created an InMemoryDataset.\n";

  return 0;
}

```

--------------------------------

### Configure Dataset Write Options in C++

Source: https://github.com/apache/arrow/blob/main/docs/source/cpp/tutorials/datasets_tutorial.rst

This snippet shows how to set up `dataset::FileSystemDatasetWriteOptions` for writing a dataset to disk. It initializes options and prepares for writing.

```cpp
dataset::FileSystemDatasetWriteOptions write_options;
write_options.partitioning = partitioning;
write_options.file_format = format;
```