# 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 Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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 " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "JSONL Stream Validator", "description": "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.", "version": "0.1", "x-build-id": "RQGFkRg6g6yI448k2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stellar_ballet_0bu~jsonl-stream-validator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stellar_ballet_0bu-jsonl-stream-validator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stellar_ballet_0bu~jsonl-stream-validator/runs": { "post": { "operationId": "runs-sync-stellar_ballet_0bu-jsonl-stream-validator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stellar_ballet_0bu~jsonl-stream-validator/run-sync": { "post": { "operationId": "run-sync-stellar_ballet_0bu-jsonl-stream-validator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "jsonlInline": { "title": "JSONL (inline)", "type": "string", "description": "Raw JSONL/NDJSON text, one record per line.", "default": "" }, "jsonlUrl": { "title": "JSONL (public URL)", "type": "string", "description": "Public URL that returns JSONL/NDJSON.", "default": "" }, "schemaInline": { "title": "JSON Schema (inline)", "type": "string", "description": "JSON Schema (draft-07 subset). Use schemaUrl OR schemaInline.", "default": "" }, "schemaUrl": { "title": "JSON Schema (public URL)", "type": "string", "description": "Public URL that returns a JSON Schema document.", "default": "" }, "dropInvalid": { "title": "Drop invalid records", "type": "boolean", "description": "If true, invalid records are dropped. If false, they pass through with `valid: false` + `errors[]`.", "default": false }, "maxErrors": { "title": "Max errors per record", "type": "integer", "description": "Cap error list per record to keep dataset rows small.", "default": 100 }, "skipEmptyLines": { "title": "Skip empty / whitespace-only lines", "type": "boolean", "description": "Ignore lines that are empty or whitespace-only.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```