Top Ways to Serialize JSON in Popular Languages

What breaks your data flow across languages? JSON serialization quirks can quietly disrupt features and integrations. Learn how to keep it consistent, fast, and secure—no matter which language your stack relies on.

How does something as simple as JSON break your app in production?

As more systems rely on APIs and microservices built with different languages, one problem remains. Data serialized in one language doesn’t always work the same in another. Small differences in how each language formats dates, handles nulls, or structures types can lead to bugs that are hard to fix.

Why do these mismatches still happen?

This article summarizes the best ways to serialize JSON across Python, Java, JavaScript, and C#. You’ll find out how different serializers behave, which libraries to use, and how to avoid issues that slow you down across teams or platforms.

Let’s consider what it takes to keep your JSON workflows clean and consistent.

Key Takeaways

• Learn the best way to serialize JSON in 15+ major programming languages

• Understand common pitfalls like circular references and empty strings

• Explore top JSON serializer libraries like Jackson, Newtonsoft.JSON, and Serde

• Get tips on handling JSON string conversion, dates, and supported types

• Compare serialization with deserializing JSON for full data round-tripping

What does it mean to serialize JSON?

Serializing JSON means converting a data object into a JSON string—a text-based representation in JavaScript Object Notation format. The result can be easily written to a file, transferred over a network, or stored in a database.

For instance, in Python:

Copy
1import JSON
2data = {"name": "Alice", "age": 28}
3JSON_string = JSON.dumps(data)

This converts a Python object (a dictionary) into a JSON string. The return value is now suitable for API responses or configuration files.

Serialization behavior may vary depending on the language or serializer. Some tools offer default handling of null, boolean, or DateTime types, while others require explicit configuration.

What is the difference between serialization and deserialization?

The terms might sound similar, but they do the opposite:

Deserializing JSON is just as vital as serializing it. A mismatch in data types, null handling, or metadata expectations during deserialization can cause runtime errors.

JSON Serialization Methods by Programming Language

Here’s how different programming languages serialize JSON, using both default and third-party libraries:

Cross-Language Compatibility

1. Empty Strings and null Values

Different languages treat "" (empty strings) and null in unique ways during serialization:

• In Python, an empty string remains as "" in the JSON output, while None becomes null.

• In C#, an empty string is serialized literally, but null can be excluded based on serializer settings (IgnoreNullValues = true).

• In JavaScript, undefined fields are often omitted, while null is included explicitly.

Issue: If a field is serialized as null in one language and ignored in another, it can cause data loss or logic bugs when deserializing.

Solution: Always define a default policy for handling null and empty string values during JSON serialization and deserialization. Use serializer configuration to enforce consistent behavior across languages.

2. Circular References

Circular references occur when an object references itself, directly or indirectly. Example in Python:

Copy
1a = {}
2a["self"] = a
3json.dumps(a)  # Raises a recursion error

In JavaScript:

Copy
1const obj = {};
2obj.self = obj;
3JSON.stringify(obj);  // Throws TypeError: Converting circular structure to JSON

Issue: Most JSON serializers don’t support circular references by default, leading to errors or infinite loops.

Solution: Use libraries that detect and break circular references:

• JavaScript: Use custom replacers or packages like flatted.

• Python: Use libraries like jsonpickle or manually remove cycles.

• C#: Newtonsoft.JSON supports reference handling via PreserveReferencesHandling.

3. Metadata Loss (e.g., datetime, boolean Types)

By default, JSON has no native datetime type. It only supports:

• string

• number

• boolean

• null

• object

• array

If you serialize a datetime object, it becomes a string, often losing context or timezone information.

Example (Python):

Copy
1from datetime import datetime
2import json
3
4json.dumps({"time": datetime.now()})  # TypeError unless custom encoder is used

Issue: Upon deserializing, the receiving end gets a plain string, not a usable datetime object.

Solution:

• Use standardized ISO 8601 format ("2023-11-15T16:37:00Z") for all date/time values.

• Implement custom serializers and deserializers that convert these strings back into datetime objects.

Best Practices for JSON Interoperability

1. Use ISO 8601 Format for Dates

Since JSON lacks a native date type, using ISO 8601 strings ensures consistency:

Copy
1{
2  "created_at": "2025-06-27T14:30:00Z"
3}

This format is:

• Universally recognized across programming languages

• Easily parsed using standard method or function calls

• Avoids ambiguity from locale-dependent formats like 06/27/2025

Libraries with ISO 8601 support:

• Python datetime.isoformat()

• Java's DateTimeFormatter.ISO_INSTANT

• JavaScript new Date().toISOString()

2. Validate JSON Output

Always validate your JSON output before sending it across systems.

Malformed JSON can:

• Crash APIs

• Causes silent data corruption

• Fail silently on some deserializing tools

Use tools like:

• JSONLint

• IDE extensions

• JSON schema validation (e.g., ajv for JavaScript)

Also, validate against a schema if available to check:

• Required fields

• Data types

• Enum constraints

3. Avoid Deep Nesting

Deeply nested JSON objects or arrays make parsing more error-prone:

Copy
1{
2  "user": {
3    "profile": {
4      "settings": {
5        "preferences": {
6          "theme": "dark"
7        }
8      }
9    }
10  }
11}

Problems:

• Harder to navigate and debug

• Slower to parse, especially in typed languages

• Fragile during version changes

Solution: Flatten JSON structures where possible. Use indexed arrays or top-level keys for clarity and performance.

4. Compress Large Payloads

Large JSON text can lead to performance bottlenecks during transmission. Compression reduces size and bandwidth:

• Most web servers and clients support Gzip and Brotli.

• Reduces size by 60–80%, especially when JSON contains repeated keys.

How to enable:

• In Python, use gzip module before writing to a file or network

• In Node.js, use zlib.createGzip() for response compression

Note: Always decompress before deserializing JSON

5. Create DTOs (Data Transfer Objects)

Instead of serializing random object instances, define explicit class structures:

• Enforces structure and data types

• Helps maintain default values

• Improves code readability and debugging

Example in Java:

Copy
1public class UserDTO {
2    private String name;
3    private int age;
4    private LocalDate createdAt;
5}

Then serialize with Jackson:

Copy
1ObjectMapper mapper = new ObjectMapper();
2String jsonString = mapper.writeValueAsString(userDTO);

Using DTOs ensures that each language knows exactly what properties, values, and types to expect.

JSON Serialization Flow

This diagram shows how serialization and deserialization of JSON work together to transmit data safely between systems. Starting from an object, we serialize JSON into a string and then deserialize it back into a usable object on the other side.

Comparing JSON to Other Formats

JSON remains the best general-purpose format when human readability and language compatibility matter most.

Security Considerations

Deserializing JSON without validation can be dangerous:

• Never trust input without sanitization

• Always check null and empty string cases

• Use secure JSON serializer options that restrict executable metadata

For instance, Python’s pickle should never be used as a drop-in JSON serializer—it executes code on load.

Keep Your JSON Reliable Across Languages

Small serialization mismatches can lead to bugs and failed communication when working across different languages. Choosing the right approach helps maintain clean and predictable data. This blog walked through practical tools like Python’s JSON module, Jackson for Java, and Newtonsoft.Json for C#, all of which help handle quirks like nulls, types, and circular structures.

As systems scale and diversify, it's smart to serialize JSON with care to prevent future issues. Review your serializers, enable schema checks, and make consistency your goal. Start refining your approach today to support secure and reliable data flows.