Split View: 바이너리 직렬화 완전 가이드 2025: Protobuf, Thrift, Avro, MessagePack, FlatBuffers, Cap'n Proto — 성능 vs 유연성의 선택

Binary Serialization Complete Guide 2025: Protobuf, Thrift, Avro, MessagePack, FlatBuffers, Cap'n Proto — Choosing Between Performance and Flexibility

Introduction: Is JSON Really the Best Choice?

A Common Scenario

When building a REST API, most developers naturally pick this:

{
  "id": 12345,
  "name": "Alice",
  "email": "alice@example.com",
  "age": 30
}

JSON is human-readable, language-neutral, and supported everywhere. It looks perfect.

But what about a service handling 1 billion requests per day? If each request is 1 KB of JSON, that's 1 TB of data per day. Parsing cost, network bandwidth, storage — all get expensive.

The same data in Protobuf is ~100 bytes. Parsing is over 10x faster. 10 GB instead of 100 GB per day. Over a year, that's tens of TB of difference.

Why Binary Serialization?

JSON's weaknesses:

• Size: Field names repeat every time. "email" dozens of times.
• Parsing cost: Converting text to numbers/objects.
• No type information: Runtime type checking required.
• No schema: Documentation and validation separate.

Strengths of binary formats:

• Small: Numeric tags instead of field names.
• Fast: Direct memory mapping or simple decoding.
• Type-safe: Schema-based.
• Evolvable: Schema evolution support.

What This Post Compares

1. Protocol Buffers (Google): The most widely used standard.
2. Thrift (Facebook/Apache): RPC integration.
3. Avro (Apache Hadoop): The de facto standard for big data.
4. MessagePack: Binary replacement for JSON.
5. FlatBuffers (Google): Zero-copy reads.
6. Cap'n Proto (Sandstorm): Zero-copy plus RPC.

This post dives 720 lines into each format's internal structure, encoding scheme, and when to use it.

1. Protocol Buffers: King of the Standard

History

• 2001: Google starts internal development.
• 2008: Open-sourced (proto2).
• 2016: proto3 released. Simpler syntax.
• 2023: Edition 2023 released. Gradual feature additions.

The default format for gRPC. All of Google uses it, and externally it's the most popular binary serialization.

IDL (Interface Definition Language)

Protobuf defines schemas in .proto files:

syntax = "proto3";

message Person {
  int32 id = 1;
  string name = 2;
  string email = 3;
  optional int32 age = 4;
  repeated string phone_numbers = 5;
}

• Each field has a tag number (1, 2, 3...).
• The tag number is the key in wire format. Field names are used only for code generation.

Code Generation

The protoc compiler generates code for the target language:

protoc --java_out=. --python_out=. --go_out=. person.proto

Result: Java, Python, and Go classes. Each includes serialize/deserialize methods.

Wire Format: The Core Idea

Protobuf encoding is a key-value stream. Each field is:

[tag + wire_type][value]

Wire types:

• 0: Varint (integers, boolean, enum).
• 1: 64-bit (fixed64, double).
• 2: Length-delimited (string, bytes, message, packed repeated).
• 5: 32-bit (fixed32, float).

Varint: Variable-Length Integer

Protobuf's key optimization: varint encoding. Small numbers use fewer bytes:

150 as varint:
150 = 0b10010110

Step 1: Split into 7-bit groups
      → [0b0000001, 0b0010110]
Step 2: Use MSB of each byte as "continuation flag"
      → [0b10010110, 0b00000001]
Step 3: Little-endian order
      → [0x96, 0x01]

Result:

• 0~127: 1 byte.
• 128~16383: 2 bytes.
• 16384~2097151: 3 bytes.
• And so on.

Small numbers take 1 byte, larger ones take more. Since most fields are small, this is efficient on average.

Encoding Example

message Person {
  int32 id = 1;
  string name = 2;
}

id = 150, name = "Bob"

Wire:

Field 1 (id):
  tag = (1 << 3) | 0 = 0x08  (field 1, wire type 0 = varint)
  value = 150 varint = 0x96 0x01
Field 2 (name):
  tag = (2 << 3) | 2 = 0x12  (field 2, wire type 2 = length-delimited)
  length = 3
  value = "Bob" = 0x42 0x6F 0x62

Total: 08 96 01 12 03 42 6F 62  (8 bytes)

8 bytes! The JSON is:

{"id":150,"name":"Bob"}

23 bytes. 2.9x smaller.

ZigZag Encoding: Optimizing for Negative Numbers

Varint is optimized for positive numbers. Negative numbers become very large varints (due to two's complement):

-1 → 0xFFFFFFFF = 10 bytes!

Solution: sint32 and sint64 types use ZigZag encoding:

 0 → 0
-1 → 1
 1 → 2
-2 → 3
 2 → 4
...

Formula: (n << 1) ^ (n >> 31)

Negative and positive numbers are mapped alternately, so smaller absolute values give shorter encodings.

Schema Evolution

Protobuf supports forward/backward compatibility:

Rules:

1. Never change tag numbers: the tag is the ID.
2. Renaming fields is OK: unrelated to wire format.
3. Adding new fields is OK: old clients ignore them.
4. Deleting fields is OK, but mark tag reserved: prevents reuse.
5. Don't use required: removed in proto3.

Example:

// v1
message Person {
  int32 id = 1;
  string name = 2;
}

// v2 (compatible)
message Person {
  int32 id = 1;
  string name = 2;
  string email = 3;  // new field
  reserved 4, 5;     // reserved for future
}

A v1 server receiving a message from a v2 client will simply ignore the email field. Vice versa works too. This is critical in distributed systems during deployments.

Default Values in Proto3

In proto3, all fields are optional. Absent fields get default values:

• Numeric: 0
• String: ""
• bool: false
• message: null

Pitfall: You cannot distinguish "is the value 0?" from "is the value missing?" To solve this, use the optional keyword (proto3 2.6+):

message Person {
  optional int32 age = 1;  // explicit presence tracking
}

Performance

Protobuf is very fast:

• 3~10x smaller than JSON.
• Parsing speed: 5~100x (depending on language implementation).
• Low memory allocation (reusable).

2. Apache Thrift: RPC Integration

Origin

• 2007: Facebook releases it.
• 2008: Apache Incubator.
• Today: Internal RPC standard at many large companies.

Emerged around the same time as Protobuf. Battle-tested at Facebook scale.

Differences from Protobuf

Thrift provides serialization + RPC + server framework all together:

service UserService {
  Person getUser(1: i32 id) throws (1: NotFoundException e),
  list<Person> listUsers(1: i32 limit, 2: i32 offset)
}

struct Person {
  1: i32 id,
  2: string name,
  3: optional string email
}

Data structures and service definitions live in one file. Protobuf was originally separate from gRPC, but Thrift was integrated.

Transport Layer

Thrift's unique aspect: separation of transport, protocol, and server.

Transport: how data moves.

• TSocket, TFramedTransport, TMemoryBuffer, ...

Protocol: how data is encoded.

• TBinaryProtocol: simple, fixed size.
• TCompactProtocol: variable length (varint-like).
• TJSONProtocol: JSON format.

Server: how requests are handled.

• TSimpleServer, TThreadPoolServer, TNonblockingServer, ...

This composability allows choosing the right combination per situation.

TCompactProtocol

Thrift's most efficient encoding. Similar ideas to Protobuf:

• Varint: integers.
• ZigZag: negatives.
• Field ID delta: stores only the difference from the previous field ID (smaller).
• Type + ID merge: wire type and field id in a single byte.

Result: Nearly identical size to Protobuf. In some cases slightly smaller.

Pros and Cons

Pros:

• Integrated RPC: self-contained without separate gRPC setup.
• Language support: 27+ languages.
• Mature: validated inside Facebook, Uber, Twitter.
• Flexible transport/protocol.

Cons:

• Fewer recent updates: Thrift 4 is in discussion but slow.
• Scattered docs: less organized than Protobuf.
• Pushed aside by gRPC: Google's marketing power.

Selection guide:

• New project: gRPC (Protobuf).
• Existing Thrift systems: keep as-is.

3. Apache Avro: Big Data's Friend

Background

A format born in the Hadoop ecosystem. Currently the de facto standard in Kafka, Spark, and Hive.

Core Feature: Schema Travels with Data

In Protobuf/Thrift, schemas are used at code generation time. Avro is different:

Avro's philosophy: Schema is delivered with the data or managed centrally.

Two modes:

1. Container File Mode: schema embedded in file header.
2. Schema Registry Mode: referenced by schema ID from a central registry.

Schema Example

Avro schemas are written in JSON:

{
  "type": "record",
  "name": "Person",
  "fields": [
    { "name": "id", "type": "int" },
    { "name": "name", "type": "string" },
    { "name": "email", "type": ["null", "string"], "default": null },
    { "name": "age", "type": "int", "default": 0 }
  ]
}

Wire Format

Avro's encoding is extremely simple:

• Fixed-size types: stored as-is (int, long, float, double).
• ZigZag varint: for int and long.
• Variable size: length + data (string, bytes).
• Record: fields listed in schema order.

Important: No tag numbers! Field order is the structure.

Result: Very compact binary. Impossible to interpret without the schema.

Example

Same Person:

id=150, name="Bob", email=null, age=0

Avro wire:

150 (zigzag varint) = 0xAC 0x02  (2 bytes)
length 3 + "Bob" = 0x06 0x42 0x6F 0x62  (4 bytes)
null union tag = 0x00  (1 byte)
0 (zigzag varint) = 0x00  (1 byte)

Total: 8 bytes

Schema Evolution: Writer vs Reader Schema

Avro's killer feature: Writer schema is not equal to Reader schema.

• Writer schema: the schema used when writing the data.
• Reader schema: the schema expected when reading the data.

Avro automatically resolves differences between the two schemas:

Writer: {id, name, age}
Reader: {id, name, email}

• age is absent in reader → ignored.
• email is absent in writer → reader's default is used.

This makes compatibility checks and migration smooth.

Schema Registry

Confluent Schema Registry centrally manages Avro schemas:

Producer:
  schema_id = registry.register(schema)
  message = [magic byte][schema_id (4B)][avro payload]

Consumer:
  schema_id = extract_from_message
  schema = registry.get(schema_id)
  data = decode(payload, schema)

Each message contains only a 4-byte schema ID. Payload is pure Avro. When accumulated across tens to hundreds of GB of Kafka topics, this savings becomes enormous.

Kafka + Avro + Schema Registry

These three form the golden combination for big data streaming:

1. Kafka: message broker.
2. Avro: efficient encoding + schema evolution.
3. Schema Registry: central schema management + compatibility validation.

The default architecture of Confluent Platform. Adopted by Netflix, LinkedIn, Uber, and more.

Protobuf vs Avro

Selection criteria:

• RPC: Protobuf (gRPC).
• Kafka / big data streaming: Avro.
• File storage: Avro (self-describing files).

4. MessagePack: A Direct JSON Replacement

Philosophy

MessagePack's slogan: "It's like JSON. but fast and small."

• 1:1 mapping with JSON.
• No schema required.
• Binary, so smaller and faster.

Example

JSON:

{"id": 150, "name": "Bob"}

MessagePack (hex):

82 A2 69 64 CC 96 A4 6E 61 6D 65 A3 42 6F 62

• 82: map with 2 items.
• A2: fixstr of length 2 ("id").
• CC 96: uint8 value 150.
• A4: fixstr of length 4 ("name").
• A3 42 6F 62: fixstr of length 3 "Bob".

Total 15 bytes. Down from JSON's 25 bytes — a 40% reduction.

Characteristics

• No schema: works immediately.
• Type preservation: int, string, array, map, binary, etc.
• Extension: supports user-defined types (timestamp, UUID, etc.).
• Language support: 50+ languages.

Use Cases

1. Redis: Lua script results, some internal communication.
2. Fluentd: log collection protocol.
3. ZeroMQ: message bindings.
4. Games: real-time network protocols.

Differences from Protobuf

MessagePack is the best fit as a JSON replacement when you want something faster and smaller than JSON without a schema system burden.

5. FlatBuffers: The Answer to Zero-Copy

Motivation

Developed by Google for games and mobile. Goal: zero deserialization time.

Problems with existing formats:

• Protobuf/Avro/MessagePack: reading requires full parsing. New object allocations.
• Small messages are fine, but large messages or frequently accessed data incurs overhead.

The Idea: Use Directly from Memory

FlatBuffers uses the memory layout itself as the serialization format:

Byte stream:
[root offset][vtable 1][table 1][vtable 2][table 2]...

When reading:

1. Get the byte buffer (memcpy or mmap).
2. Follow the root offset.
3. Access fields directly without object allocation.

auto person = GetPerson(buffer);
int id = person->id();  // read memory directly without actual parsing

Result: Deserialization time approaches zero. No memory allocation. Cache-friendly.

IDL

A schema similar to Protobuf:

namespace Game;

table Person {
  id: int;
  name: string;
  email: string;
}

root_type Person;

Performance

• Deserialization: 1000x faster than Protobuf (near zero).
• Memory: no allocation.
• Serialization: slightly slower than Protobuf (vtable construction).
• Size: 20~50% larger than Protobuf.

Use Cases

1. Game engines: Cocos2d, various game data.
2. Mobile apps: data file formats.
3. Apache Arrow: some format sharing.
4. Facebook: major user.
5. TensorFlow Lite: model files.

When to Use

• Accessed often but rarely written.
• Large data structures.
• Memory-constrained.
• Fast loading matters.

Downsides:

• Size: larger than Protobuf.
• Lower flexibility: writing is complex.
• Hard to debug: difficult to inspect the binary directly.

The Value of Random Access

A major strength of FlatBuffers: random access. If you want to read a specific field in a 1 GB file:

auto file = mmap(filename);  // actual disk loading is lazy
auto root = GetRoot(file);
auto item_100 = root->items()->Get(100);
int value = item_100->value();
// only the pages actually needed are loaded from disk

No full parsing required. Ideal for large config files, game assets, and ML models.

6. Cap'n Proto: FlatBuffers' Cousin

Origin

Kenton Varda (a main developer of Protobuf v2) left Google to build this next-generation format. Philosophy:

"Protocol Buffers, infinity times faster."

Zero-Copy Plus RPC

FlatBuffers' zero-copy advantage plus a built-in RPC system. Used in the Sandstorm project.

Schema

struct Person {
  id @0 :Int32;
  name @1 :Text;
  email @2 :Text;
}

Similar to Protobuf but with @N syntax for ordering.

Encoding Characteristics

• Aligned memory: 8-byte boundaries.
• Zero-copy capable: like FlatBuffers.
• Packed compression: optionally applied for size reduction.
• Random access: access a portion of a large message.

Canonical Form

Cap'n Proto tries to produce the same bytes for the same data (useful for hash-based comparison).

RPC Features

Time Traveling RPC: the server can forward results to another service before responding. Promise pipelining.

Client → A: getUser(1)
Client → A: (promise of user) → B.emailSummary(user)

Data flows directly between A and B. The client is not in the middle. Reduces latency.

Use Cases

Less commonly used than FlatBuffers, but used internally by large companies like Cloudflare.

FlatBuffers vs Cap'n Proto

Cap'n Proto has some technical advantages, but FlatBuffers is more widely used in practice.

7. Other Notable Formats

CBOR (Concise Binary Object Representation)

IETF standard (RFC 8949). The "real" binary version of JSON. Used in IoT and CoAP.

• No schema.
• 1:1 mapping with JSON.
• Similar to MessagePack but standardized.

BSON (Binary JSON)

MongoDB's storage format. JSON-style with additional types (ObjectId, Date, Binary).

• No schema.
• Slightly larger than JSON but faster to parse.
• Mainly internal use at MongoDB.

Apache Arrow

Columnar memory format. Described earlier. Can serialize, but its main purpose is zero-copy interchange between processes.

Parquet

Columnar disk format. Described earlier. Less a serialization format and more a storage format, but provides cross-language compatibility.

Bencode

BitTorrent's protocol. Simple but inefficient.

Smile

JSON-like binary. Part of the Jackson library.

8. Performance Comparison: By the Numbers

Benchmark Scenario

A typical message (Person object, 100 fields, with nesting):

Key observations:

1. Size: Avro is greater than Thrift is greater than Protobuf is greater than FlatBuffers is greater than MessagePack is greater than JSON.
2. Serialization speed: all are faster than JSON.
3. Deserialization: Cap'n Proto/FlatBuffers are overwhelming (zero-copy).

Real-World Throughput

Processing 1 GB of data (100 GB system memory):

FlatBuffers' advantage is stark for bulk data processing.

9. Schema Evolution Comparison

Schemas change over time. How does each format handle different versions?

Protobuf

• Tag-number based: never change.
• Adding fields: OK (new tag).
• Removing fields: OK (mark tag reserved).
• Type changes: only among compatible types (int32 is interchangeable with int64).
• Forward/backward compatibility: both possible.

Safe changes (v1 to v2):

• Adding new fields.
• Renaming fields.
• required to optional.

Dangerous changes:

• Changing tag numbers.
• Drastic type change (string to int).
• Reusing a tag after deleting the field.

Thrift

Similar to Protobuf. Tag-based and well-supports compatibility.

Avro

Handled via Writer plus Reader schema. Avro's schema resolution rules:

1. Field matching: by name (aliases supported).
2. Missing fields: default values used.
3. Type promotion: int to long, float to double.
4. Union evolution: can add null.

Avro has the most powerful evolution system. Complex changes are possible.

JSON

No schema, making evaluation difficult but effectively allowing anything.

• Adding new fields: easy (clients ignore).
• Removing fields: risky (clients may depend on them).
• Type changes: risky.

JSON Schema enables validation, but there's no code-level automatic compatibility.

FlatBuffers / Cap'n Proto

Tag-based. Similar rules to Protobuf. However, due to zero-copy, field rearrangement is forbidden.

Practical Rules

1. Tag/field numbers are forever: never reuse them.
2. Additions only: adding is safer than deleting.
3. Optional by default: new fields should be optional.
4. Default values: set them explicitly.
5. Mark deprecated: note deprecation in code comments.
6. Validate compatibility in CI: buf breaking (Protobuf), Schema Registry (Avro).

10. Selection Guide

Recommendations by Scenario

REST API (public API):

• JSON: still the default. Anyone can parse it.
• Internal optimization: MessagePack or Protobuf over HTTP.

gRPC services:

• Protobuf: the default. gRPC is designed around it.

Kafka / streaming:

• Avro plus Schema Registry: the standard combination.
• Alternative: Protobuf with Schema Registry.

Games / real-time networking:

• FlatBuffers: game data.
• Cap'n Proto: when RPC is also needed.
• MessagePack: when flexibility is needed.

Config files / CLIs:

• YAML/JSON/TOML: must be human-readable.

ML models:

• FlatBuffers: TensorFlow Lite style.
• Protobuf: ONNX style.
• SafeTensors: latest trend.

Logs / events:

• Avro: big data pipelines.
• Protobuf: gRPC logs.
• JSON: for human parsing.

IoT / embedded:

• CBOR: IoT standard.
• Protobuf: when performance matters.
• MessagePack: for flexibility.

MongoDB storage:

• BSON: fixed (MongoDB only).

Decision Tree

Do you want a schema?
|-- Yes
|   |-- Need RPC integration? -> gRPC + Protobuf or Thrift
|   |-- Big data evolution important? -> Avro
|   |-- Need zero-copy? -> FlatBuffers or Cap'n Proto
|   +-- General -> Protobuf
+-- No
    |-- JSON compatibility? -> MessagePack or CBOR
    |-- MongoDB? -> BSON
    +-- Human-readable? -> JSON/YAML

11. Pitfalls and Real-World Lessons

Pitfall 1: "Using Protobuf When JSON Is Enough"

Problem: A complex code-generation pipeline for a small-scale API.

Lesson: The tool must justify the problem. At low traffic, JSON's developer convenience outweighs Protobuf's performance gains.

Pitfall 2: "Deleting an Old Field in Protobuf and Reusing the Tag"

Problem: v2 code misinterprets v1 data.

Lesson: Tag numbers are forever. If deletion is needed, mark as reserved.

Pitfall 3: "Losing the Writer Schema in Avro"

Problem: Old messages in a Kafka topic become unreadable.

Lesson: Use Schema Registry. Always preserve the writer schema.

Pitfall 4: "File Size Increase with FlatBuffers"

Problem: After migrating from Protobuf to FlatBuffers, file sizes increase.

Lesson: FlatBuffers is speed-first. If size matters, Protobuf is better. Or add a compression layer on top of FlatBuffers.

Pitfall 5: "Maintaining Code Generation for Dozens of Target Languages"

Problem: protoc version management, per-language builds.

Lesson: Use tools like monorepo plus buf. Automate in CI.

Pitfall 6: "Violating Schema Evolution Rules"

Problem: Adding a field as required, or changing types.

Lesson: Automated validation. buf breaking, Schema Registry compatibility check.

12. Practical Tools

buf: Modern Protobuf Tooling

Created by Uber and the community, buf dramatically improves Protobuf development:

# Lint
buf lint

# Breaking change detection
buf breaking --against .git#branch=main

# Generate code
buf generate

# Remote plugins (no need to install protoc)
buf generate --template buf.gen.yaml

Recommended for every project.

Confluent Schema Registry

Centrally manages Avro/Protobuf/JSON Schema:

• REST API for registering/retrieving schemas.
• Compatibility validation.
• Kafka integration.

// Kafka producer
properties.put("value.serializer", "io.confluent.kafka.serializers.KafkaAvroSerializer");
properties.put("schema.registry.url", "http://localhost:8081");

Essential in production Kafka plus Avro environments.

protobuf-validator

Adds validation rules to Protobuf messages:

message Person {
  int32 age = 1 [(validate.rules).int32 = { gte: 0, lte: 150 }];
  string email = 2 [(validate.rules).string.email = true];
}

Generated code includes validate methods.

grpcurl / grpc_cli

Test gRPC services like curl:

grpcurl -d '{"id": 1}' localhost:50051 myservice.UserService/GetUser

Automatically obtains schemas via server reflection.

Quiz Review

// Protobuf style
Person person;
person.ParseFromArray(buffer, size);  // parse entire data
int id = person.id();                   // access already-parsed field

// FlatBuffers
auto person = GetPerson(buffer);  // just cast a pointer, no parsing
int id = person->id();             // internally compute offset, read memory directly

// v1
message Person {
  int32 id = 1;
  string name = 2;
  string deprecated_field = 3;  // delete this and...
}

// v2 (dangerous!)
message Person {
  int32 id = 1;
  string name = 2;
  int32 new_field = 3;  // reusing tag 3
}

message Person {
  int32 id = 1;
  string name = 2;
  reserved 3;
  reserved "deprecated_field";
}

# Old client code
age = person.age  # KeyError / AttributeError / 0 (default)
send_birthday_wishes(person.age)  # wrong results

Closing: The Choice of Bytes

Key Takeaways

1. Protobuf: the standard. RPC plus type safety.
2. Thrift: Protobuf's cousin. Integrated RPC.
3. Avro: the big data standard. Strongest schema evolution.
4. MessagePack: the gentle JSON replacement.
5. FlatBuffers: zero-copy. Games/ML.
6. Cap'n Proto: FlatBuffers plus RPC.
7. JSON: still the default for public APIs.

Core of the Choice

"Which format is best?" is the wrong question. "Which format is optimal for this situation?" is the right question.

• RPC: Protobuf
• Kafka: Avro
• Games: FlatBuffers
• Public API: JSON
• File storage: Avro (self-describing)
• IoT: CBOR/MessagePack

Final Lesson

A serialization format is the clothing of data. Change it to fit the situation. Poor choice means lifelong pain; good choice invisibly prevents countless problems.

Next time you design a new system, don't just say "I'll use JSON." Ask:

• How much data will flow?
• Will the schema change?
• Who are the consumers?
• Is performance the bottleneck?
• Is type safety important?

Choose the format based on those answers. That is the art of engineering.

References

• Protocol Buffers Language Guide (proto3)
• Protobuf Encoding Reference
• Apache Thrift Documentation
• Apache Avro Specification
• FlatBuffers Documentation
• Cap'n Proto
• MessagePack Specification
• buf: Modern Protobuf Tooling
• Confluent Schema Registry
• Designing Data-Intensive Applications, Ch.4 (Encoding)
• Martin Kleppmann: Thinking in Events
• Comparing Binary Formats: MessagePack, BSON, CBOR