NDJSON / JSON Lines Filter
Paste NDJSON (one JSON object per line) and keep just the rows and fields you want. Filter with a simple predicate, select or rename columns, and export as JSONL, a JSON array, or CSV. Runs entirely in your browser — no upload, no sign-up.
About this tool
NDJSON / JSON Lines Filter keeps just the rows and fields you want from newline-delimited JSON — one JSON object per line, the format that log pipelines, data exports, and streaming APIs emit. Write a small predicate to keep matching records, list the fields to keep or rename, and export the result as JSONL, a pretty JSON array, or CSV. Everything runs in your browser through WebAssembly — nothing is uploaded, so it is safe for private logs and exports.
A predicate is one expression made of path op value clauses. Operators are ==, !=, >, >=, <, <=, contains, startswith, endswith, and ~ (regex, also spelled matches). Join clauses with and, or, and not, and group them with parentheses. A bare path on its own (e.g. active) matches when that field exists and is truthy. Comparisons are numeric when both sides are numbers, otherwise string. Dotted paths (user.name) and array indexes (items.0.id) reach into nested data.
Worked example — filter rows, then keep two fields:
Input (NDJSON):
{"name":"Alice","age":30,"city":"NYC","active":true}
{"name":"Bob","age":25,"city":"LA","active":false}
{"name":"Carol","age":40,"city":"NYC","active":true}
Predicate: city == NYC and age > 28 — Fields: name, age — Format: ndjson
Output:
{"name":"Alice","age":30}
{"name":"Carol","age":40}
Worked example — rename nested fields and export CSV:
Input:
{"user":{"name":"Al","id":7},"city":"NYC"}
{"user":{"name":"Bo","id":9},"city":"LA"}
Fields: name=user.name, uid=user.id, city — Format: csv
Output:
name,uid,city
Al,7,NYC
Bo,9,LA
Limits and edge cases
- Input is pasted text held in memory, so it is bound by your browser's available RAM — it is not built for streaming multi-gigabyte files.
- Blank lines are always skipped. By default a line that is not valid JSON stops the run with a
line N: invalid JSON …error; turn on Skip invalid JSON lines to silently drop malformed lines instead. - A missing path is treated as absent:
==matches onlynull,!=matches any non-null, and the ordering/containsoperators are false. In Fields, a missing path yieldsnull. contains,startswith, andendswithare case-sensitive substring tests;~/matchesuses Rustregexsyntax (no lookbehind/backreferences) and reports invalid patterns.andbinds tighter thanor; use parentheses to force other grouping.!is not a negation operator — use the wordnot(or the Invert toggle for the whole predicate).- CSV columns are the first-seen union of the kept records' keys; cells containing a comma, quote, or newline are quoted RFC-4180 style, and
nullrenders as an empty cell. Records that are not objects contribute empty cells. - Max rows stops filtering once that many rows are kept (
0= unlimited); Invert keeps the rows that do NOT match the predicate.
FAQ
What is NDJSON / JSON Lines, and how is it different from a JSON array?
NDJSON (also called JSON Lines or JSONL) puts one complete JSON value — usually an object — on each line, with no enclosing brackets or commas between records. A JSON array wraps every element in [ … ] and separates them with commas. NDJSON streams well because each line is independent, which is why logs and exports use it. Set Output format to array to convert the kept rows back into a single pretty-printed JSON array, or ndjson to keep them line-delimited.
How do I filter on a nested or array field?
Use a dotted path. user.name == Al reads the name key inside a user object, and numeric segments index into arrays, so items.0.id > 100 looks at the first element of an items array. The same paths work in the Fields box, so you can flatten nested data — id=user.id, city pulls user.id up to a top-level id column. A path that does not exist in a record resolves to absent (missing) for the predicate and to null for field selection.
Can I use a regular expression?
Yes. Use the ~ operator (or the word matches), for example email ~ \.org$ to keep addresses ending in .org, or name ~ ^A for names starting with A. Patterns use the Rust regex engine, which supports the common syntax but not lookbehind or backreferences. An invalid pattern is reported as an invalid regex error rather than silently matching nothing.
How do I keep the rows that DON'T match?
Turn on Invert to keep the complement of your predicate — every record that the predicate rejects. For example, predicate active with Invert on keeps only the inactive rows. Within a predicate you can also negate a single clause with not, e.g. not (city == NYC); Invert flips the whole expression at once.
Is my data uploaded anywhere?
No. The filtering runs entirely in your browser through WebAssembly — the text you paste never leaves your machine, so it is safe for private logs, database exports, and API payloads. There is no server round-trip and no sign-up.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool ndjson-filter '{"name":"Alice","age":30,"city":"NYC","active":true}
{"name":"Bob","age":25,"city":"LA","active":false}
{"name":"Carol","age":40,"city":"NYC","active":true}'New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/ndjson-filter/?data=%7B%22name%22%3A%22Alice%22%2C%22age%22%3A30%2C%22city%22%3A%22NYC%22%2C%22active%22%3Atrue%7D%0A%7B%22name%22%3A%22Bob%22%2C%22age%22%3A25%2C%22city%22%3A%22LA%22%2C%22active%22%3Afalse%7D%0A%7B%22name%22%3A%22Carol%22%2C%22age%22%3A40%2C%22city%22%3A%22NYC%22%2C%22active%22%3Atrue%7D&predicate=age%20%3E%2028%20and%20city%20%3D%3D%20NYC&fields=name%2C%20city%2C%20years%3Dage&format=ndjson&invert=true&limit=0&skip_invalid=trueMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.