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Newline-delimited JSON — one JSON object per line. Used for log streams, data pipelines, and bulk data processing.
JSON Lines (also known as Newline Delimited JSON or NDJSON) emerged as a de facto standard in the 2010s for representing structured data as a sequence of JSON values separated by newline characters. Unlike standard JSON, which wraps all data in a single root array or object, JSONL stores one complete JSON value per line — typically objects — making it ideal for streaming, append-only logging, and processing large datasets line by line without loading the entire file into memory. The format is the standard for machine learning training data (used by OpenAI, Hugging Face, and other ML platforms), bulk API operations (Elasticsearch's bulk API, BigQuery data loading), log aggregation systems (structured logging with tools like Bunyan and Pino), and ETL pipeline intermediate formats.
JSONL's line-oriented structure maps naturally to Unix text processing tools — grep, head, tail, wc, and split all work meaningfully on JSONL files, combining the structure of JSON with the streamability of plain text. Each line is independently parseable, meaning corrupted records don't invalidate the entire file, and new records can be appended without re-serializing existing data. The format integrates with Apache Kafka message streams, AWS Kinesis data firehoses, and Google Cloud Dataflow pipelines.
Data processing frameworks like Apache Spark, pandas (read_json with lines=True), and DuckDB provide native JSONL support. The tabular nature of JSONL — where each line typically represents a record with the same schema — means that comparison benefits from both line-level diffing to identify changed records and field-level analysis within each JSON object to pinpoint exactly which values changed.
JSONL files represent datasets where individual record changes carry different significance depending on the field — a changed ID versus a changed description requires different review attention. Comparing JSONL files catches added or removed records in training datasets that affect model behavior, modified field values in bulk API payloads that alter data imports, changed record schemas that break downstream parsers, and reordered lines that may affect processing order in systems that are not idempotent.
Data engineers need record-level diff to verify ETL pipeline output across runs.
UtraDiff parses JSONL files line by line, building a semantic tree for each record and matching rows across files by content similarity. Inserted or deleted records are isolated without cascading as false positives through subsequent lines.
Each row's internal key structure is diffed semantically, detecting reordered keys as identical. The text diff with JSON syntax highlighting runs alongside the structured view, and cross-format comparison supports diffing JSONL against JSON arrays or YAML sequences.
Supported extensions: .jsonl .ndjson
JSONL can be compared with: JSON, JSON5, YAML, TOML, INI, Environment, Properties