JSON Lines – Working with Large Log Files

JSON Lines in Liquid Studio

Liquid Studio includes a JSON Lines Editor in a unique split view design.

The top view can instantly load huge (Terabyte+) JSON Lines files and provides schema aware validation for the whole JSON Lines document.

The bottom view consists of a fully functional JSON editor containing the top view’s selected JSON lines item. The JSON editor has the benefit of syntax highlighting, schema aware validation, auto complete/intellisense, and other tools such as a document outline view and spell checker.

Both views can be edited, validated and searched.

So what is JSON Lines?

JSON Lines, also known as JSONL or Newline Delimited JSON (NDJSON), is a simple, text-based data format where each line of a file is a single, valid JSON object. Unlike a traditional JSON file, which contains one large JSON object or an array of objects, JSON Lines files are structured to be read one line at a time. The format is a sequence of JSON values, each terminated by a newline character (\n).

For example, while a traditional JSON file would look like this:

    [
      {"id": 1, "name": "John"},
      {"id": 2, "name": "Jane"}
    ]

A JSON Lines file represents the same data as:

    {"id": 1, "name": "John"}
    {"id": 2, "name": "Jane"}

This structure is crucial for its primary use cases, as it avoids wrapping the data in a single array. Each line is an independent record, meaning that new records can be simply appended to the file without needing to parse and rewrite the entire document.

Note: Each line is terminated with a New Line, a comma on the end of the line should not be included.

What is the point of JSON Lines?

The key benefit of JSON Lines is its suitability for streaming data and large datasets. When dealing with massive files—such as application logs, data backups, or machine learning datasets—it’s often impractical to load the entire file into memory at once. JSON Lines solves this by allowing developers to process the data record by record.

This line-by-line processing model provides several advantages:

Memory Efficiency: Only one line (or record) needs to be in memory at any given time, making it ideal for large files that would otherwise cause memory issues.
Streamability: It is perfectly suited for continuous data streams, such as real-time log analysis or data pipelines, as new records can be processed as they are received.
Parallel Processing: Each line is an independent unit, which allows for easy parallelization. A large file can be split into smaller chunks, with each chunk processed simultaneously by a different worker.
Simple Tooling: It works seamlessly with standard Unix command-line tools like head, tail, grep, and awk which are designed to operate on a line-by-line basis.

This line-by-line structure allows for simple, robust appending of new data to the end of a file without the need to reformat the entire document, which would be required for a traditional JSON array.

Problems with JSON Lines

Despite its benefits, JSON Lines is not without its drawbacks:

Not a Valid JSON Document: The entire JSON Lines file is not a single, valid JSON document. This can cause issues with parsers that expect a single root element (e.g., an object or an array) and may not be able to process the file correctly.
Lack of Top-Level Metadata: Because each line is a standalone object, there is no place for file-level metadata that applies to the entire dataset (e.g., a schema version or a creation timestamp). This information would need to be stored separately or redundantly on each line, which is inefficient.
No Random Access: While you can easily append new lines to the end of the file, you cannot easily insert or update a record in the middle without rewriting the entire file from that point onwards.
No comments: Like standard JSON, the format does not allow for comments, which can make it difficult to add notes or documentation to the file.

The Power of JSON Lines: A Stream-Friendly Format

Working with JSON Lines can be very straightforward, especially in modern programming languages that have built-in support for line-by-line file processing. The primary logic involves reading a line, parsing it as a JSON object, and then repeating the process.

A number of tools are available for working with JSON Lines, from command-line utilities for quick transformations to libraries for developers and full-featured editors. These tools are often designed to handle the line-by-line nature of JSONL, making them particularly efficient for large datasets.

Command-Line Tools

These are a must-have for anyone working with data streams or large files from a terminal. They excel at filtering, transforming, and analyzing JSON Lines data.

jq: The most famous and versatile command-line JSON processor. While it can handle regular JSON, its streaming capabilities make it perfect for working with JSON Lines. It’s like sed or awk for JSON, allowing you to slice, filter, and map data with a powerful and expressive language.
gron: A unique utility that transforms JSON into a list of assignments that are easy to grep. For a JSON Lines file, it will produce a series of path = value assignments, which can then be easily filtered and converted back to JSON.
dsq: A tool that allows you to run SQL queries directly against a variety of data formats, including JSON Lines. This is incredibly useful for analysts or developers who are more comfortable with SQL syntax.
jless: A command-line JSON viewer that is specifically designed for exploring large JSON documents. It provides a vim-like interface for navigating, collapsing, and searching through structured data, which is especially helpful for debugging large JSONL files.