Guide to C++ Serialization

Serialization enables C++ objects to be persisted and reused across storage systems, networks, and disparate applications. This bridges C++ systems despite their preferred insularity – converting objects to interchangeable formats like JSON or XML.

Designed for efficiency nearer the metal, C++ handles use cases from high performance computing to real-time analytics. With serialization, these otherwise isolated systems can participate in broader data ecosystems.

This definitive guide covers considerations, formats, libraries, containers, versioning, security, and use cases surrounding C++ serialization.

Why Serialize C++ Objects

Let‘s review top reasons for integrating serialization:

• Persistence – Save/restore objects to disks, databases
• Networking – Transfer objects between systems
• Simplicity – Share objects vs rebuild structures
• Versioning – Enable backups/archives

Without serialization, C++ resides as an application island – unable to interoperate with outside systems, limiting usefulness to niche applications.

The capability to transform C++ object graphs into portable formats like JSON unlocks integration with:

• Cloud platforms and storage for horizontal scale
• Mobile and web apps interacting via APIs
• Service mesh architectures built on JSON/HTTP
• Event stream processors parsing real-time data
• Big data analytics pipelines crunching large datasets

This integration spans anything ingesting JSON over HTTP. Serialization thus bridges C++ performance into the wider JSON-powered ecosystem.

But why JSON specifically as the interchange format?

Binary vs Text Serialization

C++ objects serialize to either binary or text formats. Both approach have trade-offs:

Format	Benefits	Drawbacks
Binary	Compact, performant	Not portable across platforms
Text	Portable, self-describing	Larger payload size

Binary serialization uses native C++ byte order and datatypes. This offers efficiency starting from a known system. However, binary isn‘t portable across hardware nor backward-compatible as datatypes evolve.

Text serialization leverages interoperable standards like JSON or XML. Platform specifics get encoded into portable text representations. Though larger in size, text formats travel widely across programming languages and paradigms. Text additionally allows human inspection when debugging complex object structures.

This guide focuses on text serialization, specifically JSON.

Why JSON Has Dominated

JSON (JavaScript Object Notation) has emerged as the dominant data interchange format. But it wasn‘t always thus. Competing formats like XML, YAML, Protocol Buffers (Protobuf) once vied for relevance:

Format	Strengths	Weaknesses
XML	Verbose structure	Cumbersome parsing
YAML	Human readable	Ambiguous specifications
Protobuf	Compact and performant	Not human-readable

Each format took an extreme position favoring machine-friendliness or human-friendliness. JSON struck a pragmatic balance – concise, hierarchical structure with universal programmer familiarity. JSON resembles a simplified JavaScript object literal, ubiquity due to JavaScript‘s dominance on the web.

Platforms rapidly standardized on JSON APIs due to approachability. JSON became user-friendly enough while staying reasonably compact as data lingua franca. This drove network effects around tooling and community support.

Serializing C++ Objects to JSON

Having established JSON as the serialization format of choice, let‘s demonstrate basic usage:

// Example C++ class
class Person {
public:
  string name;
  int age;

  Person(string name, int age) :
    name(name), age(age) {} 
};

// Serialize instance to JSON
Person person("John", 30);
string json = serialize(person);  

// Output JSON text
{"name":"John","age":30}

This outputs a JSON representation of the C++ Person object. Our class instance becomes a JSON object with properties mirroring C++ member fields.

Key steps for custom serialization:

• Map C++ data members to JSON properties
• Primitive types convert directly (string, int)
• Customize handling for complex types like vectors
• Recursively serialize nested object composition
• Store polymorphic type information
• Encode C++ pointers as JSON object ids

Robust serialization manages issues like:

• Null values
• Circular references
• STL containers
• Inheritance hierarchies

For reusable libraries, customizing serialization handlers for each datatype and class offers flexibility at the expense of programmer effort.

JSON Serialization Libraries

Rather than hand-coding serialization logic, purpose-built C++ JSON libraries can simplify integration:

Library	Description
JSONCPP	Mature BSD-licensed library since 2007, good stability
RapidJSON	High-performance JSON generator/parser from Tencent
Nlohmann	Header-only library, integrate via operator overloading
Boost JSON	Boost object serialization framework + JSON support

The optimal library depends on performance requirements, integration constraints, and customization needs.

For example, RapidJSON focuses exclusively on maxing parsing and generation throughput by leveraging C++ capabilities like move semantics and SIMD instructions. This suits high-volume messaging or web services.

Whereas Nlohmann prioritizes developer ergonomics – its syntax overloading allows direct assignment between C++ objects and JSON constructs. This accelerates coding at potential cost of performance.

Serialization Library Benchmarks

Quantifying performance helps inform selection. Recent benchmarks tested leading C++ JSON libraries against 25 GB+ of Twitter JSON data.

We observe:

• RapidJSON fastest for parsing, but high memory usage
• Boost.JSON competes well, avoiding RapidJSON‘s memory spikes
• Well-implemented simdjson leverages vectorization
• Nlohmann JSON trails on performance metrics

So RapidJSON suits message brokers, while Boost provides balance. Nlohmann optimizes for developer simplicity.

Each library usage involves:

1. Register custom serialization handlers for bespoke C++ types
2. Invoke simplified serialize() and deserialize() functions
3. Pass string or stream arguments for parsing/generation

This hides intricacies of JSON encoding behind a familiar C++ interface.

Serializing STL Containers & Models

The C++ Standard Template Library (STL) offers foundational container templates – vector, list, map, unordered_set etc. STL container serialization introduces further complexity of preserving:

• Object relationships within graph structures
• Nested child object abstraction
• Comparison ordering and sorting invariance
• Unique key constraints across types

Consider serializing a map holding base/derived polymorphic objects:

std::map<int, Animal> zoo;

zoo[0] = Monkey("Bobo");
zoo[1] = Lion("Leo");  

// Serialize map container to JSON
string zooJson = serialize(zoo);   

// Output structure mirrors C++ types
{
  "0": {
    "_type": "Monkey",
    "name": "Bobo"   
  },
  "1": {
    "_type" : "Lion",
    "name": "Leo"
  } 
}

Here Animal is the base class, while Monkey and Lion are derived subclasses.

To reconstruct C++ hierarchy:

1. Deserialize JSON array into map container
2. Init pointers to derived types via _type field
3. dynamic_cast pointers back to derived classes
4. Access Monkey and Lion objects normally

Encapsulating these details behind serialize/deserialize simplifies usage while handling tricky object relationships under the hood.

Versioning Schema-Less JSON

Serialization schemes often fall short on handling evolution of formats over time. This causes brittle coupling between systems using distinct version releases.

Ideally, producers and consumers support:

• Backward compatibility – Consumers handle older formats
• Forward compatibility – Producers output newer formats

Schema-less JSON techniques emphasize flexibility for such changes:

• Dynamically introspect objects
• Duck-type pointers
• Handle absent optional members
• Default initialize new properties

This places fewer expectations on JSON structure in favor of adaptation. Versioning through extensibility preserves functionality across sequential upgrades.

Production systems apply techniques like:

• Publishing API changes for coordinated upgrades
• Supporting multiple schema versions during transition
• Deprecating older formats on a defined timeline

Thus serialization enables C++ version progress without disruption.

Security: Encrypting & Sanitizing Serialized Output

Unencrypted serialized output risks exposing private class members or sensitive data. Anyone with object dump access can inspect designs.

Mitigations include:

• Omit private variables from serialization scope
• Encrypt streams with TLS libraries like OpenSSL
• Obfuscate class and field names
• Code review serialization handlers
• Validate input streams before materializing objects

For example, OpenSSL helps encrypt JSON payloads:

#include <openssl/evp.h>

string encrypt(string json) {

  // Generate cipher and keys 
  EVP_CIPHER_CTX *ctx = EVP_CIPHER_CTX_new();
  unsigned char key[32], iv[32];

  // Encrypt
  int len, c_len;
  unsigned char *c_text =
    EVP_EncryptUpdate(ctx, json.c_str(), &len, json.size());

  return c_text; 
}

Cipher implementations secure JSON while preserving portability of the text serialization format.

High-Performance Computing Use Cases

High-performance and scientific computing frequently utilize C++ for optimal efficiency manipulating numeric datasets.

Traditionally these number-crunching systems operated in isolation – perhaps writing binary outputs to distributed filesystems. Serialization now bridges these clusters into broader data pipelines.

For example, physics simulations outputting telemetry metrics can integrate with real-time analytics by:

1. Serializing simulation event objects to JSON
2. Stream JSON physics events to Apache Kafka
3. Kafka streams events to a Spark cluster
4. Spark SQL analyzes data using JSON support

This demonstrates piping HPC outputs into cloud big data ecosystems over Kafka‘s JSON event bus.

Serialization Overhead Tradeoffs

The above does impose a 2-3x serialization tax for JSON‘s verbosity and FTS conversions. This hurts efficiency for two reasons:

1. Network i/o – More bytes sent over the wire
2. CPU overhead – JSON conversion computations

So performance-critical systems selectively output to JSON, or leverage JSON compression formats like Snappy.

In truth, JSON‘s universality outweighs moderate performance costs that Moore‘s Law rapidly diminishes. The format unlocks integration opportunities absent in isolated binary outputs.

Conclusion

JSON has standardized how systems speak, becoming the idiomatic data interchange format. Via JSON, C++‘s niche performance integrates with mainstream data ecosystems.

Libraries ease JSON serialization integration, efficiently converting rigid stack-based objects into fluid interchangeable payloads. Techniques handle versioning and security for enterprise reliability.

Code once serialized, C++ data structures flow anywhere – fueling event pipelines, microservices, and cloud analytics alike. Serialization transforms C++ from isolated islands into integral melds within the meshed sea.