JSON parsing technology: principles, optimization and practical guide

This article systematically analyzes the core technical principles of JSON parsing, explores parsing strategies and performance optimization solutions in different scenarios, and provides developers with a complete practical reference.

Core Definition and Technical Challenges of JSON Parsing

JSON (JavaScript Object Notation) is a lightweight data exchange format. Its parsing process requires converting text data into structured objects in memory (such as dictionaries and lists). The core challenges of parsing include:

Syntax validation: detects illegal characters (such as unclosed quotes, incorrect comma separation).

Data type mapping: Accurately identify numeric values (Int/Float), Boolean values, null, and nested structures.

Performance bottleneck: memory usage and processing speed issues when parsing large files.

Core technology implementation of JSON parsing

Parser workflow

Lexical Analysis: Split the JSON string into a sequence of tokens (such as {, "key", :, etc.).

Syntax Analysis: Build an abstract syntax tree (AST) based on the state machine to verify the legality of the structure.

Semantic conversion: Convert AST into the data structure of the target language (such as Python's dict, Java's HashMap).

Example: Python parsing process

import json

data = json.loads('{"name": "Alice", "age": 30}')

# Step breakdown:

# 1. Lexical analysis generates tokens: ['{', 'name', ':', 'Alice', ',', 'age', ':', '30', '}']

# 2. Build AST: The root node is a dictionary containing two key-value pairs

# 3. Convert to Python dictionary object

Data type mapping rules

Numerical processing: distinguish between integers and floating-point numbers (e.g. 42 → int, 3.14 → float).

String escapes: handling Unicode (like \u4e2d→"中") and special characters (like \"→").

Nested structures: recursively parse arrays ([]) and objects ({}).

Exception handling mechanism

Error location: The location of syntax errors is indicated by line and column numbers.

Tolerant mode: Some parsers support ignoring redundant commas or comments (non-standard extension).

Performance optimization strategy

Streaming Parsing: Use libraries such as ijson to read files block by block to avoid memory overflow.

Precompiled Schema: Define data structure through Pydantic or JSON Schema to speed up verification and conversion.

C extension acceleration: Use orjson (Rust implementation) or ujson to replace the standard library to increase the parsing speed by 3-10 times.

Typical application scenarios of JSON parsing

Front-end and back-end API data interaction

Request/response parsing: Convert the JSON data in the HTTP Body into server-side objects.

Data validation: Verify the field type and format in combination with Schema (such as regular expression matching of email addresses and mobile phone numbers).

Configuration file parsing

Dynamic loading: Parse the parameters in config.json (such as database connection information, log level).

Hot update support: Real-time reloading is achieved through a file monitoring mechanism (such as Python's watchdog).

Log and data analysis

Log formatting: Parse semi-structured log text (such as { "timestamp": "2025-03-14", "event": "login" }) into a query-friendly format.

Big Data Processing: Use json_normalize in Spark or Pandas to flatten nested JSON into a two-dimensional table.

NoSQL Database Interaction

Document storage: MongoDB's BSON format is compatible with JSON, and the parser needs to handle extended types such as ObjectId.

Query syntax parsing: Convert query conditions in JSON format (such as {"price": {"$gt": 100}}) into database native instructions.

Technical Challenges and Solutions for JSON Parsing

Challenge 1: Memory consumption of large files

Solution:

Use the raw_decode() method of json.JSONDecoder to parse it piece by piece.

Use Dask, Modin and other libraries to implement distributed parsing.

Challenge 2: Non-standard format compatibility

Problem: Non-standard syntax like single quotes, trailing commas, comments, etc.

Solution: Enable a loose parser such as json5 or demjson, or preprocess the text (such as regular expression replacement).

Challenge 3: Cross-language data compatibility

Typical problem: JavaScript's BigInt vs Python's int range differences.

Solution: Use strings to store large integers during serialization (such as "id": "12345678901234567890").

Challenge 4: Security risks

Risk point: Parsing maliciously constructed JSON leads to memory exhaustion (such as deeply nested structures).

Defensive measures:

Set a recursion depth limit (e.g. json.loads(data, max_depth=100)).

Use a sandbox environment to parse untrusted data.

Conclusion

As a basic capability of modern software development, the efficiency and robustness of JSON parsing directly affect system performance and stability. Developers need to choose parsing strategies according to the scenario, and optimize the critical path by combining preprocessing, streaming processing and high-performance libraries. As a professional proxy IP service provider, abcproxy provides a variety of high-quality proxy IP products, including residential proxies, data center proxies, static ISP proxies, Socks5 proxies, and unlimited residential proxies, which are suitable for a variety of application scenarios. If you are building a data collection system that needs to frequently call third-party APIs, you can combine abcproxy's proxy IP service with high-performance JSON parsing technology to achieve an efficient and reliable data processing pipeline.