XML to JSON Lines Converter

About XML to JSON Lines Converter

Convert XML data to JSON Lines (JSONL) format, also known as newline-delimited JSON. Perfect for streaming data, big data processing, and log files. Each line is a valid JSON object or array.

Key Features

• Line-Delimited: One JSON object/array per line for easy streaming
• Multiple Formats: Objects per line or headers + arrays format
• Type Detection: Automatically converts numbers, booleans, and null values
• Streamable: Process large datasets line-by-line without loading entire file
• Appendable: Easy to append new records to existing files
• Big Data Compatible: Works with Hadoop, Spark, Elasticsearch, MongoDB

How to Use

1. Input XML Data: Paste your XML data or upload an .xml file
2. Choose Format: Select objects per line or headers + arrays
3. Copy or Download: Copy to clipboard or download as .jsonl file
4. Process: Use with streaming tools or big data frameworks

Output Formats

• One JSON object per line: Each line is a complete JSON object - ideal for streaming and log processing
• Headers + arrays: First line contains headers, subsequent lines are data arrays - compact format for data transfer

Example Conversion

XML Input:

<?xml version="1.0"?>
<employees>
  <employee>
    <id>1</id>
    <name>John Doe</name>
    <age>28</age>
    <active>true</active>
  </employee>
  <employee>
    <id>2</id>
    <name>Jane Smith</name>
    <age>34</age>
    <active>false</active>
  </employee>
</employees>

Objects Per Line Output:

{"id":1,"name":"John Doe","age":28,"active":true}
{"id":2,"name":"Jane Smith","age":34,"active":false}

Headers + Arrays Output:

["id","name","age","active"]
[1,"John Doe",28,true]
[2,"Jane Smith",34,false]

JSON Lines Advantages

• Streamable: Process one line at a time without loading entire file into memory
• Appendable: Add new records by simply appending lines to the file
• Recoverable: Corrupted lines don't affect other records
• Simple: Easy to parse with any JSON parser
• Universal: Supported by most big data and streaming tools

Type Conversion

• Numbers: Numeric strings are converted to JSON numbers (e.g., "42" → 42)
• Booleans: "true" and "false" strings are converted to JSON booleans
• Null: Empty strings and "null" are converted to JSON null
• Strings: All other values remain as JSON strings

Supported XML Structures

• Repeating Elements: Automatically detects common record names (row, record, item, entry, employee, product, user)
• Nested Elements: Extracts child element values as fields
• Attributes: Includes XML attributes as fields (prefixed with @)
• Mixed Content: Handles various XML structures intelligently

Common Use Cases

• Big Data Processing: Import into Hadoop, Spark, or Hive
• Log Files: Convert XML logs to JSONL for easier processing
• Streaming: Process large XML datasets line-by-line
• Elasticsearch: Bulk import data into Elasticsearch
• MongoDB: Import data with mongoimport
• Data Pipelines: Use in ETL and data processing pipelines

Compatible Tools & Platforms

• Apache Hadoop: Process with MapReduce jobs
• Apache Spark: Read with spark.read.json()
• Elasticsearch: Bulk API accepts JSONL format
• MongoDB: mongoimport supports JSONL
• Command Line: Process with jq, grep, awk
• Python: Read line-by-line with json.loads()

File Format Specifications

JSON Lines format follows these rules:

• Each line is a valid JSON value (object or array)
• Lines are separated by newline characters (\n)
• UTF-8 encoding is recommended
• File extension is typically .jsonl or .ndjson

Privacy & Security

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

FAQ

When should I use JSON Lines instead of regular JSON?

Use JSON Lines when you are working with large datasets, streaming pipelines, or log files. Processing one JSON object per line makes it easy to handle data incrementally without loading the entire file into memory.

How are errors handled in a JSON Lines file?

Because each line is independent, a single malformed line usually does not prevent tools from processing the rest of the file. You can skip or log problematic lines without losing the entire dataset.

Can I convert JSON Lines back to standard JSON?

Yes. You can read each line, parse it as JSON, and push it into an array in your own scripts or tools. Many languages and command-line utilities provide straightforward ways to do this.

Which tools support JSONL generated here?

The output works with common big data and search tools such as Hadoop, Spark, Elasticsearch bulk APIs, MongoDB's mongoimport (JSONL mode), and many stream processing frameworks.