Markdown to JSON Lines Converter

About Markdown to JSON Lines Converter

Convert Markdown tables to JSON Lines (JSONL) format, also known as newline-delimited JSON. Each row becomes a separate JSON object or array on its own line, perfect for streaming and big data applications.

Key Features

• Line-Delimited Format: One JSON object/array per line
• Object Mode: Convert rows to objects using headers as keys
• Array Mode: Convert rows to simple arrays
• Streaming Ready: Process large datasets line-by-line
• Big Data Compatible: Works with Hadoop, Spark, and other tools
• File Download: Save as .jsonl file
• Instant Preview: Real-time conversion as you type

How to Use

1. Input Markdown Table: Paste your Markdown table or upload a .md file
2. Configure Options: Choose whether to use headers for object keys
3. Review Output: The JSON Lines updates automatically
4. Copy or Download: Use the JSONL in your application

What is JSON Lines?

JSON Lines is a text format where:

• Each line is a valid JSON value (object or array)
• Lines are separated by newline characters (\n)
• Files can be processed line-by-line without loading entire dataset
• Ideal for streaming, logging, and big data processing

Example Conversion

Markdown Input:

| Name | Age | City | Department |
|------|-----|------|------------|
| John Doe | 28 | New York | Engineering |
| Jane Smith | 34 | London | Marketing |

JSON Lines Output (Object Mode):

{"Name":"John Doe","Age":"28","City":"New York","Department":"Engineering"}
{"Name":"Jane Smith","Age":"34","City":"London","Department":"Marketing"}

JSON Lines Output (Array Mode):

["Name","Age","City","Department"]
["John Doe","28","New York","Engineering"]
["Jane Smith","34","London","Marketing"]

Common Use Cases

• Data Streaming: Process large datasets without loading into memory
• Log Files: Structured logging in JSON Lines format
• Big Data: Import into Hadoop, Spark, or other big data tools
• ETL Pipelines: Extract, transform, and load data efficiently
• Database Import: Bulk import into MongoDB, Elasticsearch, etc.
• API Responses: Stream large result sets from APIs
• Machine Learning: Prepare training data for ML pipelines

Advantages of JSON Lines

• Streamable: Process one line at a time
• Appendable: Add new records without parsing entire file
• Simple: Easy to generate and parse
• Robust: Corrupted line doesn't affect others
• Universal: Supported by many data processing tools
• Memory Efficient: No need to load entire dataset

Processing JSON Lines

Python:

import json

with open('output.jsonl', 'r') as f:
    for line in f:
        data = json.loads(line)
        print(data['Name'], data['Age'])

JavaScript/Node.js:

const fs = require('fs');
const readline = require('readline');

const rl = readline.createInterface({
    input: fs.createReadStream('output.jsonl')
});

rl.on('line', (line) => {
    const data = JSON.parse(line);
    console.log(data.Name, data.Age);
});

Command Line (jq):

cat output.jsonl | jq '.Name'

Pandas (Python):

import pandas as pd

df = pd.read_json('output.jsonl', lines=True)
print(df.head())

Tools Supporting JSON Lines

• Apache Spark
• Hadoop
• MongoDB (mongoimport)
• Elasticsearch (bulk API)
• BigQuery
• jq (command-line JSON processor)
• Pandas (Python data analysis)
• Logstash

Markdown Table Support

Supports GitHub Flavored Markdown (GFM) table syntax:

• Pipe-delimited columns
• Header separator line with dashes
• Optional column alignment indicators
• Leading and trailing pipes

Tips for Best Results

• Use object mode for self-describing data
• Use array mode for minimal file size
• Process line-by-line for large datasets
• Validate with JSON Lines validators if needed
• Use .jsonl or .ndjson file extension
• Consider compression (gzip) for large files

Privacy & Security

All conversions happen locally in your browser. Your Markdown data is never uploaded to any server, ensuring complete privacy and security.