docs/guide/java/row-format.md

---
title: Row Format
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id: java_row_format
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  this work for additional information regarding copyright ownership.
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---

Apache Fory™ provides a random-access row format that enables reading nested fields from binary data without full deserialization. This drastically reduces overhead when working with large objects where only partial data access is needed.

## Overview

Row format is a cache-friendly binary random access format that supports:

- **Zero-copy access**: Read fields directly from binary without allocating objects
- **Partial deserialization**: Access only the fields you need
- **Skipping serialization**: Skip serialization of fields you don't need
- **Cross-language compatibility**: Works across Python, Java, C++, and other languages
- **Column format conversion**: Can convert to Apache Arrow columnar format automatically

## Basic Usage

```java
public class Bar {
  String f1;
  List<Long> f2;
}

public class Foo {
  int f1;
  List<Integer> f2;
  Map<String, Integer> f3;
  List<Bar> f4;
}

RowEncoder<Foo> encoder = Encoders.bean(Foo.class);

// Create large dataset
Foo foo = new Foo();
foo.f1 = 10;
foo.f2 = IntStream.range(0, 1_000_000).boxed().collect(Collectors.toList());
foo.f3 = IntStream.range(0, 1_000_000).boxed().collect(Collectors.toMap(i -> "k" + i, i -> i));
List<Bar> bars = new ArrayList<>(1_000_000);
for (int i = 0; i < 1_000_000; i++) {
  Bar bar = new Bar();
  bar.f1 = "s" + i;
  bar.f2 = LongStream.range(0, 10).boxed().collect(Collectors.toList());
  bars.add(bar);
}
foo.f4 = bars;

// Encode to row format (cross-language compatible with Python/C++)
BinaryRow binaryRow = encoder.toRow(foo);

// Zero-copy random access without full deserialization
BinaryArray f2Array = binaryRow.getArray(1);              // Access f2 list
BinaryArray f4Array = binaryRow.getArray(3);              // Access f4 list
BinaryRow bar10 = f4Array.getStruct(10);                  // Access 11th Bar
long value = bar10.getArray(1).getInt64(5);               // Access 6th element of bar.f2

// Partial deserialization - only deserialize what you need
RowEncoder<Bar> barEncoder = Encoders.bean(Bar.class);
Bar bar1 = barEncoder.fromRow(f4Array.getStruct(10));     // Deserialize 11th Bar only
Bar bar2 = barEncoder.fromRow(f4Array.getStruct(20));     // Deserialize 21st Bar only

// Full deserialization when needed
Foo newFoo = encoder.fromRow(binaryRow);
```

## Key Benefits

| Feature                 | Description                                            |
| ----------------------- | ------------------------------------------------------ |
| Zero-Copy Access        | Read nested fields without deserializing entire object |
| Memory Efficiency       | Memory-map large datasets directly from disk           |
| Cross-Language          | Binary format compatible between Java, Python, C++     |
| Partial Deserialization | Deserialize only specific elements you need            |
| High Performance        | Skip unnecessary data parsing for analytics workloads  |

## When to Use Row Format

Row format is ideal for:

- **Analytics workloads**: When you only need to access specific fields
- **Large datasets**: When full deserialization is too expensive
- **Memory-mapped files**: Working with data larger than RAM
- **Data pipelines**: Processing data without full object reconstruction
- **Cross-language data sharing**: When data needs to be accessed from multiple languages

## Cross-Language Compatibility

Row format works seamlessly across languages. The same binary data can be accessed from:

### Python

```python
import pyfory
import pyarrow as pa
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class Bar:
    f1: str
    f2: List[pa.int64]

@dataclass
class Foo:
    f1: pa.int32
    f2: List[pa.int32]
    f3: Dict[str, pa.int32]
    f4: List[Bar]

encoder = pyfory.encoder(Foo)
binary: bytes = encoder.to_row(foo).to_bytes()

# Zero-copy access
foo_row = pyfory.RowData(encoder.schema, binary)
print(foo_row.f2[100000])
print(foo_row.f4[100000].f1)
```

### C++

```cpp
#include "fory/encoder/row_encoder.h"
#include "fory/row/writer.h"

struct Bar {
  std::string f1;
  std::vector<int64_t> f2;
};

FORY_FIELD_INFO(Bar, f1, f2);

struct Foo {
  int32_t f1;
  std::vector<int32_t> f2;
  std::map<std::string, int32_t> f3;
  std::vector<Bar> f4;
};

FORY_FIELD_INFO(Foo, f1, f2, f3, f4);

fory::encoder::RowEncoder<Foo> encoder;
encoder.Encode(foo);
auto row = encoder.GetWriter().ToRow();

// Zero-copy random access
auto f2_array = row->GetArray(1);
auto f4_array = row->GetArray(3);
auto bar10 = f4_array->GetStruct(10);
int64_t value = bar10->GetArray(1)->GetInt64(5);
std::string str = bar10->GetString(0);
```

## Performance Comparison

| Operation            | Object Format                 | Row Format                      |
| -------------------- | ----------------------------- | ------------------------------- |
| Full deserialization | Allocates all objects         | Zero allocation                 |
| Single field access  | Full deserialization required | Direct offset read              |
| Memory usage         | Full object graph in memory   | Only accessed fields            |
| Suitable for         | Small objects, full access    | Large objects, selective access |

## Related Topics

- [Cross-Language Serialization](cross-language.md) - XLANG mode
- [Advanced Features](advanced-features.md) - Zero-copy serialization
- [Row Format Specification](https://fory.apache.org/docs/specification/row_format_spec) - Protocol details