C Data Interface

The Arrow C Data Interface provides a minimal, stable set of C definitions for zero-copy data exchange between different libraries, languages, and runtimes. It enables efficient integration without requiring the full Arrow library implementation.

Overview

The C Data Interface consists of three main components:

ArrowSchema: Describes the data type and metadata
ArrowArray: Contains actual data buffers
ArrowArrayStream: Streams multiple arrays

Key benefits:

Zero-copy: Data is shared, not copied
Language agnostic: Works across C, C++, Python, R, Julia, Rust, etc.
ABI stable: Fixed C structures that never change
Minimal dependencies: Can be vendored into any project
Producer-consumer model: Clear ownership and lifetime semantics

Data Structures

ArrowSchema

Describes the data type:

struct ArrowSchema {
  const char* format;         // Type format string
  const char* name;           // Field name (may be NULL)
  const char* metadata;       // Optional metadata
  int64_t flags;              // Flags (nullable, etc.)
  int64_t n_children;         // Number of child schemas
  struct ArrowSchema** children;  // Child schemas
  struct ArrowSchema* dictionary; // Dictionary (if applicable)
  void (*release)(struct ArrowSchema*);  // Cleanup callback
  void* private_data;         // Implementation-specific data
};

ArrowArray

Contains the actual data:

struct ArrowArray {
  int64_t length;             // Number of elements
  int64_t null_count;         // Number of nulls (-1 if unknown)
  int64_t offset;             // Logical offset into buffers
  int64_t n_buffers;          // Number of buffers
  int64_t n_children;         // Number of child arrays
  const void** buffers;       // Array of buffer pointers
  struct ArrowArray** children;  // Child arrays
  struct ArrowArray* dictionary; // Dictionary (if applicable)
  void (*release)(struct ArrowArray*);  // Cleanup callback
  void* private_data;         // Implementation-specific data
};

ArrowArrayStream

Streams multiple arrays:

struct ArrowArrayStream {
  int (*get_schema)(struct ArrowArrayStream*, struct ArrowSchema* out);
  int (*get_next)(struct ArrowArrayStream*, struct ArrowArray* out);
  const char* (*get_last_error)(struct ArrowArrayStream*);
  void (*release)(struct ArrowArrayStream*);
  void* private_data;
};

Exporting Data (Producer)

C++
Python
Rust

#include <arrow/c/bridge.h>
#include <arrow/api.h>

using namespace arrow;

// Export an Array
auto array = ArrayFromJSON(int64(), "[1, 2, 3, null, 5]");

struct ArrowArray c_array;
struct ArrowSchema c_schema;

// Export to C structures
ARROW_RETURN_NOT_OK(
    ExportArray(*array, &c_array, &c_schema));

// Pass c_array and c_schema to consumer
// Consumer must call release callbacks when done

// Export a RecordBatch
auto schema = arrow::schema({
    field("id", int64()),
    field("name", utf8())
});

auto batch = RecordBatch::Make(
    schema, 3,
    {ArrayFromJSON(int64(), "[1, 2, 3]"),
     ArrayFromJSON(utf8(), "[\"Alice\", \"Bob\", \"Charlie\"]")
    });

ARROW_RETURN_NOT_OK(
    ExportRecordBatch(*batch, &c_array, &c_schema));

// Export a Schema only
ARROW_RETURN_NOT_OK(
    ExportSchema(*schema, &c_schema));

// Export a RecordBatchReader (stream)
std::vector<std::shared_ptr<RecordBatch>> batches = {batch};
ARROW_ASSIGN_OR_RAISE(auto reader,
    RecordBatchReader::Make(batches, schema));

struct ArrowArrayStream c_stream;
ARROW_RETURN_NOT_OK(
    ExportRecordBatchReader(reader, &c_stream));

import pyarrow as pa
from pyarrow.cffi import ffi

# Create data
array = pa.array([1, 2, 3, None, 5])

# Export array - returns pointers to C structures
# These can be passed to other libraries/processes
schema_ptr = ffi.new("struct ArrowSchema*")
array_ptr = ffi.new("struct ArrowArray*")

array._export_to_c(array_ptr[0], schema_ptr[0])

# Pass schema_ptr and array_ptr to consumer
# Consumer must call release when done

# Export a table as stream
table = pa.table({
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie']
})

stream_ptr = ffi.new("struct ArrowArrayStream*")
table._export_to_c_as_stream(stream_ptr[0])

use arrow::array::{Int64Array, Array};
use arrow::ffi;

// Create array
let array = Int64Array::from(vec![Some(1), Some(2), None, Some(4)]);

// Export to C Data Interface
let (array_ffi, schema_ffi) = ffi::to_ffi(&array.to_data()).unwrap();

// Pass to consumer (C code, Python, etc.)
// Consumer is responsible for calling release

// array_ffi and schema_ffi are FFI structs
// that can be passed across language boundaries

Importing Data (Consumer)

C++
Python
Rust

#include <arrow/c/bridge.h>

// Import an Array
struct ArrowArray c_array;  // Received from producer
struct ArrowSchema c_schema;

ARROW_ASSIGN_OR_RAISE(auto array,
    ImportArray(&c_array, &c_schema));

// c_array and c_schema are now "moved" and invalid
// Producer's release callback will be called when array is destroyed

std::cout << "Array length: " << array->length() << std::endl;

// Import a RecordBatch
ARROW_ASSIGN_OR_RAISE(auto batch,
    ImportRecordBatch(&c_array, &c_schema));

std::cout << "Batch rows: " << batch->num_rows() << std::endl;

// Import a Schema only
ARROW_ASSIGN_OR_RAISE(auto schema,
    ImportSchema(&c_schema));

// Import a RecordBatchReader (stream)
struct ArrowArrayStream c_stream;  // Received from producer

ARROW_ASSIGN_OR_RAISE(auto reader,
    ImportRecordBatchReader(&c_stream));

// Read batches
std::shared_ptr<RecordBatch> batch;
while (true) {
  ARROW_ASSIGN_OR_RAISE(batch, reader->Next());
  if (!batch) break;
  // Process batch
}

import pyarrow as pa
from pyarrow.cffi import ffi

# Receive C structures from producer
# schema_ptr and array_ptr are FFI pointers

# Import array
array = pa.Array._import_from_c(array_ptr[0], schema_ptr[0])

print(f"Array length: {len(array)}")

# Import as RecordBatch
batch = pa.RecordBatch._import_from_c(array_ptr[0], schema_ptr[0])

print(f"Batch rows: {batch.num_rows}")

# Import stream
reader = pa.RecordBatchReader._import_from_c(stream_ptr[0])

for batch in reader:
    print(f"Batch: {batch.num_rows} rows")

# Convert to table
table = reader.read_all()

use arrow::ffi;
use arrow::array::ArrayData;

// Receive FFI structures from producer
// array_ffi: FFIArrowArray
// schema_ffi: FFIArrowSchema

// Import from C Data Interface
let array_data = unsafe {
    ffi::from_ffi(array_ffi, &schema_ffi).unwrap()
};

// Convert to typed array
use arrow::array::Int64Array;
let array = Int64Array::from(array_data);

println!("Array length: {}", array.len());

Device Memory Support

The C Data Interface supports GPU and device memory:

C++
Python

#include <arrow/c/bridge.h>

// Export device array (e.g., CUDA)
std::shared_ptr<Array> cuda_array;  // Array with CUDA buffers
std::shared_ptr<Device::SyncEvent> sync_event;

struct ArrowDeviceArray c_device_array;
struct ArrowSchema c_schema;

ARROW_RETURN_NOT_OK(
    ExportDeviceArray(*cuda_array, sync_event,
                     &c_device_array, &c_schema));

// c_device_array includes device_type and device_id
// Consumer can synchronize using sync_event if needed

// Import device array
ARROW_ASSIGN_OR_RAISE(auto imported_array,
    ImportDeviceArray(&c_device_array, &c_schema));

// Array buffers are on the device specified in c_device_array

import pyarrow as pa
from pyarrow.cffi import ffi

# Export device array (e.g., CUDA)
# Assuming cuda_array is on GPU

device_array_ptr = ffi.new("struct ArrowDeviceArray*")
schema_ptr = ffi.new("struct ArrowSchema*")

cuda_array._export_to_c_device(
    device_array_ptr[0], schema_ptr[0]
)

# Import device array
imported_array = pa.Array._import_from_c_device(
    device_array_ptr[0], schema_ptr[0]
)

Integration Examples

C++ to Python (Zero-Copy)

C++
Python

// C++ extension module
#include <pybind11/pybind11.h>
#include <arrow/c/bridge.h>
#include <arrow/python/pyarrow.h>

namespace py = pybind11;

py::object create_arrow_array() {
  auto array = arrow::ArrayFromJSON(
      arrow::int64(), "[1, 2, 3, 4, 5]"
  ).ValueOrDie();
  
  // Export to Python using C Data Interface
  return py::cast(arrow::py::wrap_array(array));
}

PYBIND11_MODULE(example, m) {
  m.def("create_arrow_array", &create_arrow_array);
}

import example  # C++ extension
import pyarrow as pa

# Get array from C++ (zero-copy)
array = example.create_arrow_array()

print(f"Type: {array.type}")
print(f"Length: {len(array)}")
print(f"Data: {array.to_pylist()}")

# No data was copied - array shares memory with C++

Python to R (via C Interface)

Python
R

import pyarrow as pa
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri

# Create Arrow table in Python
table = pa.table({
    'x': [1, 2, 3],
    'y': ['a', 'b', 'c']
})

# Export to R using C Data Interface
# R's arrow package can import via C interface
ro.r('''
library(arrow)
receive_table <- function(ptr) {
  # Import from C Data Interface pointer
  arrow::Array$import_from_c(ptr)
}
''')

library(arrow)

# Create table in R
tbl <- arrow_table(
  x = c(1, 2, 3),
  y = c("a", "b", "c")
)

# Export to external process via C Data Interface
# The C struct pointers can be passed to any consumer
schema_ptr <- tbl$schema$export_to_c()
array_ptr <- tbl$export_to_c()

# Pass pointers to consumer (Python, C++, etc.)

Rust Integration

Rust

use arrow::array::{Int64Array, RecordBatch};
use arrow::ffi;
use arrow::datatypes::{Schema, Field, DataType};
use std::sync::Arc;

// Create data in Rust
fn export_data() -> (ffi::FFIArrowArray, ffi::FFIArrowSchema) {
    let array = Int64Array::from(vec![1, 2, 3, 4, 5]);
    let data = array.to_data();
    
    // Export to C Data Interface
    ffi::to_ffi(&data).unwrap()
}

// Import from C Data Interface
fn import_data(
    array_ffi: ffi::FFIArrowArray,
    schema_ffi: &ffi::FFIArrowSchema
) -> Int64Array {
    // Safety: Ensure FFI structs are valid
    let array_data = unsafe {
        ffi::from_ffi(array_ffi, schema_ffi).unwrap()
    };
    
    Int64Array::from(array_data)
}

// Export RecordBatch
fn export_batch() -> (ffi::FFIArrowArray, ffi::FFIArrowSchema) {
    let schema = Arc::new(Schema::new(vec![
        Field::new("a", DataType::Int64, false),
        Field::new("b", DataType::Utf8, false),
    ]));
    
    let batch = RecordBatch::try_new(
        schema.clone(),
        vec![
            Arc::new(Int64Array::from(vec![1, 2, 3])),
            Arc::new(arrow::array::StringArray::from(vec!["a", "b", "c"])),
        ]
    ).unwrap();
    
    ffi::to_ffi(&batch.to_data()).unwrap()
}

Streaming Protocol

C++
Python

// Producer: Export a stream
std::vector<std::shared_ptr<RecordBatch>> batches;
// ... populate batches ...

ARROW_ASSIGN_OR_RAISE(auto reader,
    RecordBatchReader::Make(batches, schema));

struct ArrowArrayStream c_stream;
ARROW_RETURN_NOT_OK(
    ExportRecordBatchReader(reader, &c_stream));

// Consumer: Import and iterate stream
ARROW_ASSIGN_OR_RAISE(auto imported_reader,
    ImportRecordBatchReader(&c_stream));

// Get schema
auto schema = imported_reader->schema();

// Iterate batches
std::shared_ptr<RecordBatch> batch;
while (true) {
  ARROW_ASSIGN_OR_RAISE(batch, imported_reader->Next());
  if (!batch) break;  // End of stream
  
  std::cout << "Received batch with " 
            << batch->num_rows() << " rows" << std::endl;
}

from pyarrow.cffi import ffi

# Producer: Export a stream
table = pa.table({'x': range(1000)})

stream_ptr = ffi.new("struct ArrowArrayStream*")
table._export_to_c_as_stream(stream_ptr[0])

# Consumer: Import and iterate stream
reader = pa.RecordBatchReader._import_from_c(stream_ptr[0])

# Get schema
schema = reader.schema

# Iterate batches
for batch in reader:
    print(f"Received batch with {batch.num_rows} rows")

# Or read all at once
table = reader.read_all()

Memory Management

Key points about memory management:

Producer owns data: Until release callback is called
Consumer calls release: Must call exactly once when done
Move semantics: Import functions “move” the C structs
No double-free: Release callback handles cleanup
Reference counting: Arrow implementations use refcounting internally

// Example of proper release handling
struct ArrowArray c_array;
struct ArrowSchema c_schema;

// Export (producer)
ExportArray(*array, &c_array, &c_schema);

// At this point:
// - c_array.release != NULL (producer set cleanup callback)
// - Producer's data is kept alive

// Import (consumer) - MOVES the structs
auto imported = ImportArray(&c_array, &c_schema);

// After import:
// - c_array.release == NULL (moved)
// - c_schema.release == NULL (moved)
// - imported holds the data
// - When imported is destroyed, original release callbacks are called

Performance Best Practices

Zero-copy whenever possible: C Data Interface enables true zero-copy
Minimize conversions: Keep data in Arrow format across boundaries
Batch operations: Transfer record batches, not individual rows
Use streams: For large datasets, stream data incrementally
Device placement: Keep GPU data on GPU when possible

Arrow Flight RPC - Network transport layer
IPC Streaming - Serialization format
Arrow Flight SQL - SQL interface

Working with Data

File Formats

Data Processing

Data Transfer

Advanced Topics

Overview

Data Structures

ArrowSchema

ArrowArray

ArrowArrayStream

Exporting Data (Producer)

Importing Data (Consumer)

Device Memory Support

Integration Examples

C++ to Python (Zero-Copy)

Python to R (via C Interface)

Rust Integration

Streaming Protocol

Memory Management

Performance Best Practices

Build docs developers (and LLMs) love

Working with Data

File Formats

Data Processing

Data Transfer

Advanced Topics

​Overview

​Data Structures

​ArrowSchema

​ArrowArray

​ArrowArrayStream

​Exporting Data (Producer)

​Importing Data (Consumer)

​Device Memory Support

​Integration Examples

​C++ to Python (Zero-Copy)

​Python to R (via C Interface)

​Rust Integration

​Streaming Protocol

​Memory Management

​Performance Best Practices

​Related Topics

Build docs developers (and LLMs) love

Overview

Data Structures

ArrowSchema

ArrowArray

ArrowArrayStream

Exporting Data (Producer)

Importing Data (Consumer)

Device Memory Support

Integration Examples

C++ to Python (Zero-Copy)

Python to R (via C Interface)

Rust Integration

Streaming Protocol

Memory Management

Performance Best Practices

Related Topics