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Spark Execute SDK for Node.js and Browser","maintainers":[{"name":"mnaustria","email":"marcus.austria@coherent.global"},{"name":"vu.nguyen.coherent","email":"vu.nguyen@coherent.global"},{"name":"shinmarq","email":"shin.quipit@coherent.global"}],"readme":"# Spark Execute SDK\n\nExecute [Spark](https://coherent.global/spark/) models both online and offline with a unified API. This SDK seamlessly switches between local WebAssembly execution and remote API calls, giving you the flexibility to run models wherever makes sense for your application.\n\n## Features\n\n- Run models locally using WebAssembly (offline)\n- Call Spark API endpoints (online)\n- Automatic fallback from offline to online on errors\n- Cancel long-running executions mid-flight\n- Automatic memory management with configurable limits\n- Full support for cross-service calls (XCall)\n- Works in Node.js and browser environments\n\n## Quick Start\n\nInstall the package:\n\n```bash\nnpm install @coherentglobal/spark-execute-sdk\n```\n\nBasic usage:\n\n```js\n// CommonJS\nconst Spark = require('@coherentglobal/spark-execute-sdk');\n\n// ES Modules / TypeScript\nimport Spark from '@coherentglobal/spark-execute-sdk';\n\nconst spark = new Spark({\n sparkEndpoint: {\n url: \"https://excel.uat.us.coherent.global\",\n tenant: \"your-tenant\",\n authType: \"public\"\n }\n});\n\nconst result = await spark.execute({\n request_data: { inputs: { Input: 1 } },\n request_meta: { version_id: \"model-uuid\" }\n});\n\nawait spark.destroy();\n```\n\n# How-to Guides\n\n## Installation\n\n```\nnpm install @coherentglobal/spark-execute-sdk\n```\n\nor\n\n```\nyarn add @coherentglobal/spark-execute-sdk\n```\n\n## Usage\n\n### Configuration\n\n**Execution Modes:**\n- Include both `sparkEndpoint` and `nodegenModels` for **automatic fallback** (offline → online)\n- Include only `sparkEndpoint` for **online-only** execution\n- Include only `nodegenModels` for **offline-only** execution\n\n```js\nconst config = {\n // Online execution (optional - omit for offline-only)\n sparkEndpoint: {\n url: \"https://excel.uat.us.coherent.global\",\n tenant: \"tenant\",\n authType: \"public\" | \"syntheticKey\" | \"bearerToken\",\n\n // Provide value OR function (called per request - you handle caching)\n syntheticKey: \"apiKey\" || generateSyntheticKey(),\n bearerToken: \"token\" || generateToken(),\n },\n\n // Offline execution (optional - omit or use [] for online-only)\n nodegenModels: [\n {\n versionId: \"uuid\",\n type: \"base64\", // Always \"base64\"\n binary: string | Blob | Buffer | ArrayBuffer | Function, // See below\n preventCleanup: false, // Keep in memory even when limit reached\n replica: 1, // Number of concurrent worker instances\n metaData: {\n EngineInformation: {\n ProductName: \"folder-name\",\n ServiceName: \"service-name\",\n VersionId: \"uuid\"\n }\n }\n }\n ],\n\n // Advanced options\n offlineMaxService: 20, // Max models in memory before LRU cleanup\n timeout: 60000 // Request timeout (ms)\n};\n```\n\n### Understanding the `binary` Configuration\n\nThe `binary` field accepts different formats depending on your environment.\n\n| Environment | Accepted Formats |\n|-------------|------------------|\n| **Browser** | Base64 string, `Blob`, `ArrayBuffer`, Function |\n| **Node.js** | Base64 string, `Buffer`, `ArrayBuffer`, Function |\n\n> **Important:** The `type` field should always be `\"base64\"` regardless of actual binary format.\n\n#### Common Patterns\n\n**Static values:**\n```js\n// Base64 string (most common)\nconst fs = require('fs');\nbinary: fs.readFileSync('./model.zip', 'base64')\n\n// Buffer (Node.js)\nbinary: fs.readFileSync('./model.zip')\n\n// ArrayBuffer (fetch in browser/Node.js)\nconst response = await fetch('/models/model.zip');\nbinary: await response.arrayBuffer()\n\n// Blob (browser file upload)\nbinary: new Blob([zipData], { type: 'application/zip' })\n```\n\n**Dynamic loading (function):**\n```js\n// Lazy load from filesystem\nbinary: (versionId) => require('fs').readFileSync(`./models/${versionId}.zip`, 'base64')\n\n// Fetch from remote storage\nbinary: async (versionId) => {\n const response = await fetch(`https://storage.example.com/models/${versionId}.zip`);\n return await response.arrayBuffer();\n}\n\n// With caching\nconst modelCache = new Map();\nbinary: async (versionId) => {\n if (!modelCache.has(versionId)) {\n modelCache.set(versionId, await fetchModelFromS3(versionId));\n }\n return modelCache.get(versionId);\n}\n```\n\n## API Reference\n\n### Initialization\n\nCreate a Spark instance with your configuration:\n\n```js\nconst spark = new Spark(config);\n```\n\n### execute(input, version_id?)\n\nExecute a model using the provided input payload.\n\n```js\n// Basic usage\nconst response = await spark.execute(input);\n\n// With explicit version ID\nconst response = await spark.execute(input, 'model-uuid-here');\n```\n\n> **Tip:** The `version_id` parameter is optional if already in `request_meta`. See [Error Handling](#error-handling) below.\n\n### executeWithCancellationToken(input, version_id?)\n\nExecute a model with the ability to cancel the operation mid-execution. Useful for long-running calculations that users may want to cancel.\n\n```js\nconst execution = spark.executeWithCancellationToken(input);\n\n// Cancel anytime before completion\nexecution.cancellationToken.cancel();\n\nexecution.response\n .then((result) => {\n console.log('Completed:', result);\n })\n .catch((err) => {\n if (err instanceof Spark.WasmRunnerErrors.ExecuteCancelled) {\n console.log('User cancelled the execution');\n } else {\n console.error('Execution failed:', err);\n }\n });\n```\n\n> **Important:** Cancellation is best-effort. The underlying WebAssembly execution may continue running in its worker thread, but the result will be discarded and an `ExecuteCancelled` error will be thrown. The worker is cleaned up after completion.\n\n### destroy()\n\nClean up all resources, unload models, and terminate worker threads. Call this when you're done with a Spark instance to prevent memory leaks.\n\n```js\nawait spark.destroy();\n```\n\n> **Critical:** Always call `destroy()` when finished, especially in browsers or long-running Node.js apps. Forgetting this can lead to memory leaks as WebAssembly modules and workers won't be garbage collected.\n\n### Error Handling\n\nSpecific error types accessible via `Spark.WasmRunnerErrors`:\n\n```js\ntry {\n const response = await spark.execute(input);\n} catch (err) {\n if (err instanceof Spark.WasmRunnerErrors.MissingModelError) {\n console.error('Model not found:', err.message);\n } else if (err instanceof Spark.WasmRunnerErrors.ExecuteCancelled) {\n console.error('Execution was cancelled');\n } else if (err instanceof Spark.WasmRunnerErrors.UnauthorizedError) {\n console.error('Authentication failed');\n } else if (err instanceof Spark.WasmRunnerErrors.BadRequestError) {\n console.error('Bad request:', err.message);\n } else {\n console.error('Unknown error:', err);\n }\n}\n```\n\n**Available Error Types:**\n- `MissingModelError` - Model not found\n- `ExecuteCancelled` - Execution was cancelled via cancellation token\n- `UnauthorizedError` - Authentication failed (invalid token/key)\n- `BadRequestError` - Invalid request or SDK not initialized properly\n\n### Error Response Structure\n\nWhen an online API request fails, the Spark API returns structured error responses:\n\n```json\n{\n \"error\": {\n \"status\": 401,\n \"message\": \"Unauthorized: Invalid API key\",\n \"code\": \"UNAUTHORIZED\"\n }\n}\n```\n\nCommon HTTP status codes:\n- **400 Bad Request** - Invalid input data or malformed request\n- **401 Unauthorized** - Missing or invalid authentication credentials\n- **404 Not Found** - Model or endpoint not found\n- **500 Internal Server Error** - Server-side error during execution\n- **503 Service Unavailable** - Service temporarily unavailable\n\n### Input Structure\n\n```json\n{\n \"request_data\": { \"inputs\": { \"Input\": 1 } },\n \"request_meta\": {\n \"version_id\": \"\",\n \"transaction_date\": \"2022-09-19T04:17:17.142Z\",\n \"call_purpose\": \"Spark - API Tester\"\n // correlation_id, source_system, requested_output (optional)\n }\n}\n```\n\n## Platform-Specific Usage\n\n### Node.js\n\n```js\n// CommonJS\nconst Spark = require('@coherentglobal/spark-execute-sdk');\n\n// ES Modules / TypeScript\nimport Spark from '@coherentglobal/spark-execute-sdk';\n\nconst fs = require('fs');\nconst spark = new Spark({\n nodegenModels: [{\n versionId: \"model-uuid\",\n binary: fs.readFileSync('./model.zip', 'base64'),\n metaData: { /* ... */ }\n }]\n});\n\nconst response = await spark.execute(input);\nawait spark.destroy();\n```\n\n### Browser\n\n```html\n\n\n```\n\n## Troubleshooting\n\n### Common Issues\n\n#### \"Model not found\" Error\n\n**Problem:** SDK throws `MissingModelError` when trying to execute.\n\n**Solutions:**\n- Verify that `versionId` in your input matches a configured model in `nodegenModels`\n- Check that `request_meta.version_id` is correctly specified in your input\n- Check if the model binary is loaded (check browser console for loading errors)\n\n#### \"Binary corrupted\" or Initialization Errors\n\n**Problem:** Model fails to initialize or throws corruption errors.\n\n**Solutions:**\n- Verify the model binary is valid and not truncated during download\n- Check that the base64 encoding is correct (no line breaks or extra characters)\n- Try re-downloading the model from Spark\n- Ensure the binary isn't corrupted (use `type: \"base64\"` for base64-encoded zips)\n\n#### \"Worker terminated unexpectedly\"\n\n**Problem:** WebAssembly worker crashes during execution.\n\n**Solutions:**\n- Check browser console for memory errors\n- Reduce the number of concurrent replicas\n- Ensure the model isn't too large for the available memory\n- Try increasing timeout value if the model is computationally intensive\n\n#### ExecuteCancelled Thrown Instantly\n\n**Problem:** Cancellation error occurs immediately even without calling cancel.\n\n**Solutions:**\n- Check if cancellation token is being reused from a previous execution\n- Create a new execution instance for each call to `executeWithCancellationToken`\n- Verify that cancel() isn't being called earlier in your code flow\n\n#### Online Fallback Not Triggered\n\n**Problem:** SDK doesn't fall back to online execution when offline fails.\n\n**Solutions:**\n- Ensure both `sparkEndpoint` and `nodegenModels` are configured\n- Check that the offline error is not being caught before fallback occurs\n- Verify network connectivity for online execution\n- Check authentication credentials for the online endpoint\n\n#### Memory Leaks in Long-Running Applications\n\n**Problem:** Application memory usage keeps increasing over time.\n\n**Solutions:**\n- Always call `spark.destroy()` when finished with an instance\n- Don't create multiple Spark instances unnecessarily\n- Monitor the number of loaded models (check against `offlineMaxService` limit)\n- Use `preventCleanup: false` for models that don't need to persist\n\n## Memory Usage & Cleanup\n\n### How Model Cleanup Works\n\nThe SDK automatically manages memory using LRU (Least Recently Used) eviction:\n\n- **offlineMaxService** (default: 20): Maximum models kept in memory\n- **preventCleanup**: Set to `true` to prevent a model from being unloaded\n- When the limit is reached, the least recently used model (without `preventCleanup`) is unloaded\n\n### Worker Thread Management\n\nEach model uses dedicated worker threads for parallel execution.\n\n#### Concurrency Model\n\n- Each model creates a pool of isolated worker threads based on the `replica` count (default: 1)\n- Incoming execution requests are distributed across available workers in round-robin fashion\n- Each worker runs in its own thread with its own WebAssembly instance—no shared state\n- Model are lazy-loaded on first run\n- Higher `replica` counts increase initial execution time (more workers to load) but improve throughput for concurrent requests\n\n#### Configuration Guide\n\n| Use Case | `replica` | `preventCleanup` |\n|----------|-----------|------------------|\n| High-frequency model** | 4-8 | `true` |\n| Occasional use | 1 | `false` |\n| Critical path | 2-4 | `true` |\n| Background jobs | 1-2 | `false` |\n\n#### Memory Considerations\n\nEach worker instance loads the full WebAssembly module into memory:\n\n- **Memory per worker** = Model required memory (~150-300 MB)\n- **Total memory** = (Number of models) × (Replicas per model) × (Memory per worker)\n- **Example**: 10 models × 3 replicas × 150 MB = ~4500 MB minimum\n\n**NOTE**: We recommend keeping `replica` at 2 or fewer, unless you have a very specific performance requirement that justifies higher concurrency.\n\n### Cleanup Best Practices\n\n**Always call destroy():**\n```js\nconst spark = new Spark(config);\ntry {\n await spark.execute(input);\n} finally {\n await spark.destroy();\n}\n```\n\n**For long-running apps:**\n- Set `offlineMaxService` based on available memory\n- Use `preventCleanup: true` sparingly\n- Call `destroy()` before page unload (browsers)\n\n## Known Limitations\n\n### Browser Memory Constraints\n\nWebAssembly in browsers has strict memory limits:\n\n| Constraint | Limit | Impact |\n|------------|-------|--------|\n| **WebAssembly memory** | ~1-2 GB (browser dependent, varied between platform) | Large models may fail to load |\n| **Total tab memory** | ~2-4 GB (browser dependent) | Multiple models + high replicas can hit browser limits |\n\n**Recommendations for Browser:**\n- Limit `replica` count to 2-4 for large / highly complex models\n- Monitor memory usage with browser DevTools\n- Consider online-only mode for very large models\n\n### Node.js Worker Thread Cost\n\nEach worker thread in Node.js has overhead:\n\n- **Startup time**: 50-200ms per worker\n- **Memory overhead**: ~150-300 MB per thread (V8 isolate + runtime)\n- **Efficient thread limit**: Typically 50-200 threads (OS dependent)\n\n**For Node.js applications:**\n- Don't create hundreds of workers (replica count × model count should be reasonable)\n- Workers are lazy-loaded but all replicas for a model are initialized on first execution\n\n### Cross-Service Call (XCall) Depth\n\n**Recursion Limit:**\n- Maximum XCall depth: ~10-20 levels (implementation dependent)\n- Deeply nested XCalls increase memory usage and execution time\n- Stack overflow possible with excessive recursion\n\n**Best Practice:**\n- Design models to minimize XCall depth\n- Avoid circular dependencies between models\n- Consider flattening deeply nested call chains\n\n### Model Loading Failures\n\n**Common offline mode failures:**\n\n| Scenario | Browser | Node.js |\n|----------|---------|---------|\n| 10+ concurrent replicas | May hit memory limits | Works but slow startup |\n| Complex XCall graphs | Stack overflow possible | More headroom |\n\n**Mitigation:**\n- Use online mode for exceptionally large models\n- Reduce `replica` count if initialization fails\n- Monitor memory consumption during development\n\n## Live Demo\n\nSee the SDK in action with this interactive CodeSandbox example:\n\n**[View Demo →](https://codesandbox.io/s/autumn-hill-1342ln?file=/index.html)**\n","readmeFilename":"README.md"}