MediaWiki to JSON Lines Converter

About MediaWiki to JSON Lines Converter

Convert MediaWiki tables to JSON Lines (JSONL) format, also known as newline-delimited JSON (NDJSON). Each row becomes a separate JSON object or array on its own line, making it perfect for streaming, big data processing, and log files.

Key Features

• Line-Delimited: One JSON object/array per line for easy streaming
• Type Detection: Automatically converts numbers, booleans, and null values
• Header Support: Use first row as object keys
• Streamable: Process large datasets line-by-line without loading entire file
• Appendable: Easy to append new records to existing files
• Memory Efficient: Each line is independent and can be processed separately
• File Upload: Upload .wiki files directly
• Instant Preview: Real-time conversion as you type
• Copy & Download: Easy export options

How to Use

1. Input MediaWiki Table: Paste your MediaWiki table or upload a .wiki file
2. Configure Format: Choose between object or array format
3. Review Output: The JSON Lines updates automatically
4. Copy or Download: Use the Copy button or download as .jsonl file

Output Formats

• Object Format: Each row becomes a JSON object with header names as keys (best for databases)
• Array Format: Each row becomes a JSON array (best for simple data processing)
• One Per Line: Each JSON object/array is on its own line
• No Commas: Lines are not separated by commas, making it easy to append

Example Conversion

MediaWiki Input:

{| class="wikitable" border="1"
! Name !! Age !! City !! Department
|-
| John Doe || 28 || New York || Engineering
|-
| Jane Smith || 34 || London || Marketing
|}

JSON Lines Output (Object Format):

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

JSON Lines Output (Array Format):

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

Common Use Cases

• Big Data Processing: Process large datasets with Hadoop, Spark, or MapReduce
• Log Files: Store structured log data in JSONL format
• Streaming: Stream data line-by-line without loading entire file into memory
• Database Import: Import into MongoDB, Elasticsearch, or other NoSQL databases
• ETL Pipelines: Use in data transformation and loading workflows
• Machine Learning: Prepare training data for ML models
• API Responses: Stream large API responses efficiently

Advantages of JSON Lines

• Streamable: Process one line at a time without loading entire file
• Appendable: Add new records by appending lines to the file
• Recoverable: Corrupted lines don't affect other lines
• Simple: Easy to parse and generate with any programming language
• Memory Efficient: No need to load entire dataset into memory
• Parallel Processing: Different lines can be processed in parallel

Big Data Compatibility

JSON Lines format is widely supported by big data tools:

• Apache Hadoop: Process JSONL files with MapReduce
• Apache Spark: Read/write JSONL with spark.read.json()
• Elasticsearch: Bulk import with JSONL format
• MongoDB: Import with mongoimport --jsonArray
• Google BigQuery: Load JSONL files directly
• AWS Athena: Query JSONL files in S3

Type Detection

The converter automatically detects and converts data types:

• Numbers: "42" → 42, "3.14" → 3.14
• Booleans: "true" → true, "false" → false
• Null: "null" or empty cells → null
• Strings: Everything else remains as strings

MediaWiki Table Support

Supports standard MediaWiki table syntax:

• Table opening with {|
• Header rows with ! delimiter
• Row separators with |-
• Data rows with | delimiter
• Cell separators with ||
• Table closing with |}

File Format Specifications

• Extension: .jsonl, .ndjson, or .json
• MIME Type: application/x-ndjson
• Line Separator: LF (\\n) or CRLF (\\r\\n)
• Encoding: UTF-8
• Structure: One valid JSON value per line

Tips for Best Results

• Use object format for database imports and analytics
• Use array format for simple data processing pipelines
• Ensure header row contains unique column names
• Use consistent data types within each column
• Test with a small sample before processing large datasets
• Consider compression (gzip) for large JSONL files

Privacy & Security

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

FAQ  MediaWiki to JSON Lines Converter

What is the difference between JSON and JSON Lines?

Regular JSON typically represents an entire dataset as a single array or object, while JSON Lines stores one JSON value per line. JSONL is easier to stream, append, and process line-by-line for big data and logging workflows.

Can I open JSON Lines files in standard JSON viewers?

Some viewers support JSONL directly, but many expect a single JSON value. In those cases, you can wrap the lines in square brackets and commas, or use tools that understand .jsonl / .ndjson natively.

How do I import JSONL into tools like Elasticsearch or BigQuery?

Most big data and search platforms provide specific import options for line-delimited JSON. For example, Elasticsearch bulk APIs and BigQuery load jobs both support JSONL inputs where each line is a separate record.

Why does the tool not add commas between lines?

JSON Lines intentionally omits commas between records so that each line is an independent JSON document. This makes it trivial to append new lines or process the file as a stream.

Can I mix object and array formats in the same file?

Technically yes, because each line is self-contained JSON. However, for consistency and easier downstream processing, it is usually best to stick to either all objects or all arrays within a given file.