How to read JSON files

This article deeply analyzes the core technical logic of JSON file reading, covering multi-language implementation solutions, performance optimization strategies and solutions to common problems. Combined with the application of IP2world proxy service in data collection, it provides developers with a complete JSON data processing guide.

1. Technical definition and core value of JSON file reading

JSON (JavaScript Object Notation) is a lightweight data exchange format, widely used in configuration files, API responses, and cross-platform data transmission. Its core value lies in:

Structured storage: supports nested objects and arrays, and can clearly express hierarchical relationships (such as user information containing address sub-objects)

Cross-language compatibility: Almost all programming languages provide native or third-party parsing libraries

Human-computer dual-reading feature: The text format is convenient for program analysis and also supports manual review and modification

IP2world proxy service is often combined with JSON file reading and writing in data collection, for example, persisting the JSON response obtained from the API and then parsing and analyzing it.

2. JSON reading implementation solution in a multi-language environment

1. Python Implementation

import json

# Read local files

with open('data.json', 'r', encoding='utf-8') as f:

data = json.load(f)

# Parsing API responses (combined with requests library)

import requests

response = requests.get('https://api.example.com/data', proxies=ip2world_proxy_config)

api_data = response.json()

characteristic:

Automatically convert JSON objects to dictionaries/lists

Support json.JSONDecodeError exception capture

2. JavaScript Implementation

// Node.js environment

const fs = require('fs');

let rawData = fs.readFileSync('data.json');

let jsonData = JSON.parse(rawData);

// Browser environment (asynchronous reading)

fetch('data.json')

.then(response => response.json())

.then(data => console.log(data));

Note:

The browser needs to handle cross-domain issues (CORS)

It is recommended to read large files in a streaming manner to avoid memory overflow.

3. Java Implementation

import com.fasterxml.jackson.databind.ObjectMapper;

// Read local file

ObjectMapper mapper = new ObjectMapper();

Map<String, Object> data = mapper.readValue(new File("data.json"), Map.class);

// Parse network data (combined with HttpClient)

CloseableHttpClient client = HttpClients.custom()

.setProxy(ip2worldProxy).build();

HttpResponse response = client.execute(new HttpGet("https://api.example.com/data"));

JsonNode rootNode = mapper.readTree(response.getEntity().getContent());

Advantages:

Jackson/Gson library supports high-performance streaming parsing (JsonParser)

Type binding can be directly mapped to POJO objects

3. 4 Technical Challenges and Solutions for JSON File Reading

1. Large file processing performance bottleneck

Problem: Loading 10GB JSON file causes memory exhaustion

Solution:

Use streaming parsing (such as Python's ijson, Java's JsonParser)

Chunked reading:

import ijson

with open('large_data.json', 'r') as f:

parser = ijson.parse(f)

for prefix, event, value in parser:

if prefix == 'item.key':

process(value)

2. Abnormal encoding and format

Typical errors:

BOM header interference (\ufeff)

Trailing comma ({"key": "value",})

Solution:

Force UTF-8 encoding and skip BOM:

with open('data.json', 'r', encoding='utf-8-sig') as f:

data = json.load(f)

Use a loose parser (such as Python's demjson, JS's JSON5)

3. Complex structure mapping

Nested object handling:

Path query: jq command line tool or jsonpath-ng library

from jsonpath_ng import parse

expr = parse('$.users[?(@.age > 30)].name')

matches = [match.value for match in expr.find(data)]

Type conversion exception:

Automatic conversion of numeric strings (such as "00123" converted to 123)

Use parse_float/parse_int callback functions to control type

4. Security risk control

JSON injection attack: maliciously constructed JSON string causes the parser to crash

Defensive measures:

Limit the maximum parsing depth (such as Python's json.loads(max_depth=10))

Use safer parsing libraries such as orjson instead of standard libraries

4. 3 Best Practices for Efficiently Reading JSON

1. Preprocessing optimization strategy

Compression and Indexing:

Use gzip compression for repeated fields (can save 70% of space)

Create an inverted index for frequently queried fields (such as Elasticsearch)

Format verification:

Deploy JSON Schema validation (Python example):

from jsonschema import validate

schema = {"type": "object", "properties": {"id": {"type": "number"}}}

validate(instance=data, schema=schema)

2. Memory management technology

Sharding: Split a large file into multiple small files based on key fields

jq -c '.users[]' large.json | split -l 1000 - users_

Lazy loading: parse specific fields only when needed (like Dask lazy calculation)

3. Abnormal monitoring system

Logging: Capture parsing error context information

try:

data = json.loads(raw_json)

except json.JSONDecodeError as e:

logging.error(f"Error at line {e.lineno}: {e.msg}")

Retry mechanism: When network source JSON reading fails, the IP2world proxy automatically switches IP and tries again

5. Collaborative Scenarios of JSON Reading and Proxy Services

Distributed data collection:

The multi-threaded crawler fetches API data through IP2world dynamic residential proxy and writes JSON responses to distributed file systems (such as HDFS)

Use S5 proxy API to implement independent IP for each request thread to avoid anti-crawling mechanism

Cross-region data aggregation:

Call IP2world specific region proxy (such as German residential IP) to obtain localized JSON data

Compare and analyze data characteristics of different regions (such as price and user behavior differences)

Real-time log analysis:

When streaming the server JSON log, use the proxy IP to protect the real address of the source station

Combining Kafka+Spark to build a real-time processing pipeline

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