docs/guide/java/row-format.md

title: Row Format sidebar_position: 9 id: java_row_format license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

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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

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

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++

#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