40 compelling advantages that make JSON Lines the format of choice for streaming data, big data processing, and modern applications
This is the main advantage. You can read and parse the file one line at a time.
Comparison:
A standard JSON file (as an array) must be read entirely into memory before you can parse it. This is impossible for a 50GB file on a machine with 16GB of RAM.
You can easily append new records. To add a new log, you just append a new line to the file.
Comparison:
To add an item to a standard JSON array, you must read the entire file, parse it, add the new item to the in-memory array, and then serialize and write the entire file back to disk.
Since you only process one line at a time, the memory required is minimal, regardless of whether the file is 10MB or 10TB.
Use Case:
A web server writing log entries. It just appends strings to a file, which is incredibly fast and light on resources.
Your program can begin processing the very first record as soon as it's read, without waiting for the entire file to download or be parsed.
Use Case:
A real-time dashboard reading from a live data feed. It can update statistics instantly as each new line (event) arrives.
A syntax error or data corruption on one line only affects that single record.
Comparison:
In a standard JSON array, one missing comma or extra bracket can make the entire file unparseable. In JSONL, you can simply skip the malformed line and continue.
You can easily split a 1-billion-line JSONL file for parallel processing.
Example:
Give lines 1-1,000,000 to Core 1, lines 1,000,001-2,000,000 to Core 2, etc. Each line is an independent JSON object, so no coordination is needed.
Comparison:
This is extremely difficult with standard JSON, as you can't just "split" the file in the middle of an object.
JSONL is a native format for many big data systems (like Apache Spark, Hadoop, and AWS Step Functions).
Reasoning:
The "Map" step in a MapReduce job can operate on each line independently, making it a perfect fit for this processing model.
You can easily split a large JSONL file into smaller chunks for network transfer and reassemble them by simple concatenation.
Comparison:
You cannot "split" a JSON file without breaking its syntax. You would have to parse it, split the array, and re-serialize each chunk into a new, valid JSON file.
Counting the number of records is as simple as counting the number of lines.
Example (PowerShell):
(Get-Content my_data.jsonl).CountComparison:
To count records in a standard JSON array, you must parse the entire file and get the length of the array.
Each line is a full JSON object, so you retain all the benefits of JSON over simpler formats.
Comparison (vs. CSV):
CSV is flat. It cannot natively represent nested objects ({"user": {"id": 123}}) or arrays ({"tags": ["a", "b"]}). JSONL handles these perfectly.
Formats like CSV break if your text contains a comma or a newline. This requires complex escaping rules ("Hello, world").
Reasoning:
In JSONL, a newline or comma inside a string value is just part of the JSON string ({"comment": "Hello,\nworld"}). It doesn't break the format's structure.
You can have different object structures on different lines. Line 1 could be {"event": "login", "user": 1} and Line 2 could be {"event": "error", "code": 500, "details": "..."}.
Use Case:
Perfect for logging, where different event types have different data payloads.
Comparison (vs. CSV):
A CSV file has a rigid column structure that all rows must follow.
JSONL is far more concise than its equivalent in XML.
Comparison (vs. XML):
JSONL: {"id": 1, "name": "Alice"}
XML: <record><id>1</id><name>Alice</name></record>
The XML version is much larger and harder to read.
You can use common text-processing tools directly on JSONL files.
grep / Select-String: Find lines (records) containing specific texthead / Get-Content -TotalCount: Get the first N recordstail / Get-Content -Tail: Get the last N records or follow a log file in real-timewc -l: Count the total number of recordsIt's incredibly easy to write a program that generates JSONL. You just loop, serialize your object to a JSON string, and print it with a newline.
You don't need a special "JSONL library." You just need a line reader and a standard JSON parser, which are built into every modern language.
It's easier for a human to open a 5GB JSONL file and inspect the first few lines than it is to open a 5GB standard JSON file, which will likely crash any text editor.
You can merge two JSONL files into one valid JSONL file by simply concatenating them.
Example (PowerShell):
Get-Content file1.jsonl, file2.jsonl | Set-Content combined.jsonlComparison:
You cannot do this with standard JSON files.
Many ML/AI platforms (like OpenAI and Google Vertex AI) use the JSONL format for uploading training and batch-prediction datasets.
Reasoning:
Each line represents one training example or one prediction request, which maps perfectly to their streaming data pipelines.
It's an excellent format for "dumping" data from a database (especially NoSQL databases like MongoDB) for backup or transfer, as each document maps directly to one line.
This is a major DX win. If you're looking for an object with "id": "xyz-123", you can grep for that string, and the entire line returned is the entire object.
Comparison:
In a standard, pretty-printed JSON file, grep would only return the single line with the ID, giving you no context about the rest of the object.
The parsing logic is often simpler. Instead of data = json.load(file_handle) (which reads all) followed by a loop, your code is just for line in file_handle: process(json.parse(line)). This is more linear and intuitive.
Need a random sample of 1,000 records from a 1-billion-record file?
Example (PowerShell):
Get-Content data.jsonl | Get-Random -Count 1000Comparison:
With a standard JSON array, you'd have to parse the entire file, load all 1 billion records into an array, and then sample it.
If you find one bad record (e.g., on line 50,342), you can use a tool like sed or a simple script to find and replace just that line.
Comparison:
To patch one record in a standard JSON array, you must parse the whole file, find the item, change it, and re-serialize the entire file.
Because it's line-based, it works with tools that know nothing about JSON.
Use Case:
The Linux split command can break a 10GB JSONL file into 10 1GB files (split -l 1000000 data.jsonl), and each one is still a valid JSONL file. This is impossible with a JSON array.
Many modern databases use this format for bulk import/export. MongoDB's mongoexport produces JSONL by default. Google BigQuery, AWS S3 Select, and Azure Data Lake all have first-class support for it.
It is the de facto standard for structured logging. Tools like Filebeat, Logstash (part of the ELK stack), Splunk, and Fluentd are built to read logs line-by-line and are optimized to parse JSONL.
A stream of messages in Kafka, RabbitMQ, or AWS SQS is conceptually identical to a JSONL file. Each message is a self-contained JSON object, and the "topic" is the (never-ending) file.
You never need to find a "JSONL library" for your language. You only need two things every language has: 1) a line reader, and 2) a JSON parser. This gives it universal, out-of-the-box compatibility.
A common anti-pattern is just mashing JSON objects together ({"id":1}{"id":2}). This is a valid JSONL file. JSONL is the formal specification for this intuitive (but previously non-standard) idea.
A JSONL file is a perfect way to represent a stream of database changes.
Example:
{"op": "INSERT", "data": {"id": 1, "name": "Alice"}}
{"op": "UPDATE", "id": 1, "change": {"name": "Alicia"}}
{"op": "DELETE", "id": 1}Solves a classic CSV problem. If you add a new field to your data, you just add the new key-value pair to new lines.
Example:
{"id": 1, "name": "A"}
{"id": 2, "name": "B", "new_field": "x"}Parsers not aware of new_field will simply ignore it, making the format forward-compatible. In CSV, this would break all column indexing.
For a large, read-only set of key-value data, you can use a JSONL file as a "grep-able" cache. Instead of loading a 2GB JSON into memory, you can grep the file for the key (e.g., "id": "user-456") to retrieve the record, which is slow but uses zero RAM.
If you have 10 servers all writing their own logs.jsonl, you can merge them into one master file by simple file concatenation. You can even do this while they are still being written to.
It provides a structured record format without the baggage of XML.
Comparison (vs. XML):
JSONL has no schemas (XSD), no namespaces (xmlns), no DTDs, and no complex attribute-vs-element debates. It's just data.
When streaming from an API, the server doesn't have to gather all 1,000 results into an array. It can serialize and send the first object immediately, lowering perceived latency for the client.
A standard JSON array parser must "look ahead" to find the closing ] to know it's done. A JSONL parser has a simpler job: read until you hit \n, parse, repeat. This is a simpler and faster state machine.
Operations teams can safely rotate JSONL log files. They can mv app.jsonl app.jsonl.1 and tell the app to re-open its file handle. The file is never "corrupted" by being incomplete.
Comparison:
Doing this to a standard JSON array file would leave a permanently broken, unclosed array in app.jsonl.1.
A program generating a JSONL stream doesn't need to know when the "end" is to add a special closing character. It just stops writing. The stream is valid whether it has 1 line or 1 billion.
Because the structure (the JSON keys) is repeated on every line, JSONL compresses very effectively with gzip. More importantly, you can pipe it through a compression stream (e.g., cat data.jsonl | gzip > data.jsonl.gz) without loading it all into memory.
Explore detailed comparisons, understand the disadvantages, and discover when JSONL is the right choice for your project.