# JSONL Stream Validator (`stellar_ballet_0bu/jsonl-stream-validator`) Actor

Validate a stream of JSONL/NDJSON records against a JSON Schema (draft-07 subset) one line at a time. Per-record error rows, drop-or-tag policy, summary by error type. Pairs with json-schema-validator + jsonl-to-csv.

- **URL**: https://apify.com/stellar\_ballet\_0bu/jsonl-stream-validator.md
- **Developed by:** [Nikita S](https://apify.com/stellar_ballet_0bu) (community)
- **Categories:** Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 jsonl validateds

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## JSONL Stream Validator

Validate a stream of JSONL/NDJSON records against a JSON Schema (draft-07 subset) one line at a time. Per-record error rows, drop-or-tag policy, summary by error code.

Pure JS, zero deps (the schema validator is a self-contained subset implementation, not a heavy npm dep). No HTTP calls unless you point `jsonlUrl` / `schemaUrl` at a public endpoint. Pairs with:

- `jsonl-to-csv` (Batch 5) — convert validated JSONL to typed CSV
- `json-schema-validator` (Batch 3) — validates the *schema* itself against meta-rules (use as a pre-check)

### Why this Actor

The Apify Store has at least 5 JSON Schema validators, but every one of them:

- takes the data as a single nested array (not a stream), which blows memory on big files;
- lacks a draft-07 subset that's actually usable for ETL pipelines (most implement only `type` and `required`);
- doesn't emit per-record error rows tagged with the original line number;
- doesn't let you drop invalid records or keep them tagged, depending on the use case.

This Actor takes JSONL line-by-line, validates each line, and emits one dataset row per record. 1M-line inputs are realistic.

### Supported schema features (draft-07 subset)

- `type` — string or array of types
- `required` — array of property names
- `properties` — per-property schemas
- `additionalProperties` — `false` or a schema
- `items` — single schema for array elements
- `enum`, `const`
- `minimum`, `maximum`, `exclusiveMinimum`, `exclusiveMaximum`
- `minLength`, `maxLength`, `pattern`
- `minItems`, `maxItems`, `uniqueItems`
- `minProperties`, `maxProperties`

Not in the subset: `$ref`, `oneOf`/`anyOf`/`allOf`/`not`, `definitions`, conditional `if/then/else`, `format`. If you need those, normalize the schema first or use `json-schema-validator` (Batch 3) as a meta-check.

### Input

| field | type | required | default | description |
|---|---|---|---|---|
| `jsonlInline` | string | one of two | `""` | Raw JSONL/NDJSON text. |
| `jsonlUrl` | string | one of two | `""` | Public URL that returns JSONL/NDJSON. |
| `schemaInline` | string | one of two | `""` | JSON Schema (draft-07 subset). |
| `schemaUrl` | string | one of two | `""` | Public URL that returns a JSON Schema document. |
| `dropInvalid` | bool | no | `false` | If true, invalid records are dropped (replaced with `{_kind:"dropped"}`). |
| `maxErrors` | int | no | `100` | Cap error list per record. |
| `skipEmptyLines` | bool | no | `true` | Skip empty / whitespace-only lines. |

### Output

Per input line (one dataset item):

```json
{ "_kind": "valid", "lineNumber": 1, "record": { "id": 1, "name": "Alice" } }
{ "_kind": "invalid", "lineNumber": 2, "valid": false, "record": {...}, "errorCount": 1, "errors": [{"path":"/id","code":"type","message":"expected type integer, got string"}] }
{ "_kind": "parse_error", "lineNumber": 3, "error": "...", "raw": "{...}" }
```

Plus a SUMMARY key:

```json
{
  "totalLines": 5,
  "parsed": 4,
  "parseErrors": 0,
  "schemaValid": 2,
  "schemaInvalid": 2,
  "skippedEmpty": 1,
  "byErrorCode": { "type": 2 }
}
```

### Quick start

```json
{
  "jsonlInline": "{\"id\":1,\"name\":\"Alice\"}\n{\"id\":\"two\",\"name\":\"Bob\"}\n",
  "schemaInline": "{\"type\":\"object\",\"required\":[\"id\",\"name\"],\"properties\":{\"id\":{\"type\":\"integer\"},\"name\":{\"type\":\"string\"}}}"
}
```

### Pricing

Pay per event. Each line processed counts as 1 event (regardless of valid/invalid).

### Local development

```bash
npm install
npm test
apify validate-schema
apify run --input-file=./example.json
```

### License

MIT

# Actor input Schema

## `jsonlInline` (type: `string`):

Raw JSONL/NDJSON text, one record per line.

## `jsonlUrl` (type: `string`):

Public URL that returns JSONL/NDJSON.

## `schemaInline` (type: `string`):

JSON Schema (draft-07 subset). Use schemaUrl OR schemaInline.

## `schemaUrl` (type: `string`):

Public URL that returns a JSON Schema document.

## `dropInvalid` (type: `boolean`):

If true, invalid records are dropped. If false, they pass through with `valid: false` + `errors[]`.

## `maxErrors` (type: `integer`):

Cap error list per record to keep dataset rows small.

## `skipEmptyLines` (type: `boolean`):

Ignore lines that are empty or whitespace-only.

## Actor input object example

```json
{
  "jsonlInline": "",
  "jsonlUrl": "",
  "schemaInline": "",
  "schemaUrl": "",
  "dropInvalid": false,
  "maxErrors": 100,
  "skipEmptyLines": true
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("stellar_ballet_0bu/jsonl-stream-validator").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("stellar_ballet_0bu/jsonl-stream-validator").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call stellar_ballet_0bu/jsonl-stream-validator --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=stellar_ballet_0bu/jsonl-stream-validator",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/dJ5LKfb0Abd0krKYd/builds/RQGFkRg6g6yI448k2/openapi.json