JSON Lines is useful for streaming data or handling very large log files. Because each line can be parsed independently, you can process huge datasets without loading the entire file into memory.
Why Use JSON Lines?
Unlike traditional JSON arrays, JSON Lines allows each entry to be processed independently. This makes it ideal for working with huge datasets without loading the entire file into memory.
- Stream-friendly: Supports real-time data pipelines.
- Memory-efficient: Process one line at a time.
- Log-friendly: Perfect for server logs and event data.
- Append-only format: New records can be added easily.
Example of JSON Lines
{
"name": "Alice", "age": 30
}
{
"name": "Bob", "age": 25
}
{
"name": "Charlie", "age": 28
}Each line is a complete JSON object, independent of the others.
Common Use Cases
- Large-scale logging systems
- Big data pipelines (Kafka, Spark, Hadoop)
- APIs that return continuous data streams
- Machine learning datasets
- Distributed systems tracing and monitoring
Benefits for Developers
JSON Lines simplifies working with constantly growing or streaming data. Developers can parse, filter, or transform each JSON object incrementally, enabling more efficient processing and lower resource usage.