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.

Frequently Asked Questions

How is JSON Lines different from normal JSON?

JSON Lines stores one JSON value per line, separated by newlines, instead of wrapping everything in a single top-level array. This makes it easier to stream, append, and process line-by-line without loading the entire file into memory.

Can I mix objects and arrays in the same JSONL file?

Technically yes—each line just needs to be valid JSON. However, most tools assume a consistent structure per line, so it's best to keep either all objects or all arrays for compatibility.

What file extension should I use?

Use .jsonl or .ndjson. Both are widely recognized for line-delimited JSON formats, and many tools explicitly look for these extensions.

Is this format supported by big data tools?

Yes. JSON Lines is supported or easily consumable by tools like Hadoop, Spark, Elasticsearch, BigQuery, and many streaming pipelines. You can usually treat each line as a separate record.

What happens if a single line is malformed?

One of the benefits of JSON Lines is robustness: a malformed line typically only breaks that record. Many parsers can skip bad lines while successfully processing the rest of the file.