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JSONL explained: what it is and when to use it

There's no need to introduce JSON (JavaScript Object Notation) anymore. It has become the de facto standard for exchanging structured data between applications. However, when working with large datasets, logs, machine learning pipelines, or data streams, another format is often preferred: JSON Lines (JSONL).

In this article, we'll look at what JSONL is, how it differs from standard JSON, and in which situations it becomes particularly useful.

What is JSONL?

JSONL is a text format in which each line contains exactly one valid JSON value. In practice, this is most often a JSON object, but it can also be an array, a string, a number, a boolean, or null. Unlike a standard JSON file, which can group multiple records inside an array, a JSONL file writes each JSON value independently on its own line.

Example:

{"id":1,"name":"Alice","country":"France"}
{"id":2,"name":"Bob","country":"Germany"}
{"id":3,"name":"Charlie","country":"Canada"}

Each line is a complete JSON document that can be parsed independently.

Unlike standard JSON, there is no enclosing array ([]) and no commas between the objects.

JSONL does not necessarily contain objects, although that is the most common use case. Here is a valid JSONL example:

{"id":1,"name":"Alice"}
2
"Cyril"

JSON vs JSONL

A standard JSON document containing multiple objects looks like this:

[
  {"id":1,"name":"Alice"},
  {"id":2,"name":"Bob"},
  {"id":3,"name":"Cyril"}
]

Although perfectly valid, a JSON document structured as an array requires loading the entire file before it can be parsed.

JSONL, on the other hand, allows incremental processing because each line is independent. Applications can read one record at a time without loading the entire file into memory.

This design makes JSONL particularly well suited for very large datasets.

Why one object per line?

The main idea behind JSONL is streamability.

Since each line is an independent JSON value, applications can:

  • append new records easily;
  • process files sequentially;
  • split files across multiple workers;
  • handle partial errors more effectively.

This approach also simplifies distributed processing systems such as Apache Spark or Apache Flink, where data is processed as streams. At work, whenever I need to export large amounts of data, JSONL is my go-to format.

Common use cases

JSONL is widely used in data infrastructure.

Application logs

Structured logging systems often use JSONL.

Each log entry becomes an independent JSON object, making indexing and analysis easier with tools such as Elasticsearch or Splunk.

Machine learning datasets

Many machine learning frameworks store datasets in JSONL format.

Example:

{"prompt":"What is JSON?","completion":"A lightweight data interchange format."}
{"prompt":"What is XML?","completion":"A markup language for structured documents."}

Each example is independent, making loading and preprocessing easier.

AI and LLM fine-tuning

Some AI platforms, including OpenAI, use JSONL for fine-tuning datasets.

Each line represents one training example, making validation and data preparation simpler.

Data pipelines

ETL pipelines often export data as JSONL because records can be generated continuously without rebuilding an entire JSON document.

This is particularly useful in event-driven and streaming architectures.

Advantages of JSONL

JSONL offers several advantages:

  • efficient memory usage;
  • easy streaming;
  • simple file concatenation;
  • fast appending of new records;
  • compatibility with distributed systems;
  • human-readable format;
  • based on the already widely adopted JSON format.

For datasets containing millions of records, these advantages become significant.

Limitations

JSONL is not always the right choice—nothing is perfect.

Since each record is independent, the format is not well suited for representing complex hierarchical structures.

In addition, validation is performed line by line: an invalid line does not invalidate the entire file, but it must be handled separately by the application.

Conclusion

JSONL is a simple but powerful extension of JSON designed for large-scale data processing. By storing one JSON value per line, it enables efficient parsing, smooth streaming, and scalable processing.

If your application handles logs, AI datasets, ETL pipelines, or large amounts of structured data, JSONL is often a better choice than standard JSON. Standard JSON remains ideal for APIs and configuration files, while JSONL has established itself as a key format for data engineering and machine learning.

cyrilou




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