How it Works

Job lifecycle: an overview of ByteNite's end-to-end workflow

At ByteNite, a typical job follows this lifecycle:

1. Launch Phase: The customer initiates a job via the ByteNite API, specifying the data source and configuration details. The system pulls data from various cloud storage services (AWS S3, GCP, Azure, etc.).

2. Create Phase: This encompasses three stages:

   • Partitioner: The partitioner ingests the raw data, pre-processes it if necessary, and fans it out into independent chunks for parallel execution.

   • App: Each chunk is processed independently by the user-defined App, running the core logic (e.g., AI inference, media transcoding, data transformation).

   • Assembler: The assembler collects the results from each parallel execution, performs optional post-processing, and generates the final output.

3. Launch Phase (continued): Once the job completes, the assembled output is written back to the designated data destination (cloud storage), and the job status is finalized.

This modular flow ensures scalability, fault tolerance, and flexibility, letting you focus on building impactful applications without worrying about the underlying infrastructure.

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📦 Data pre-processing and task fan-out

Many applications require a pre-processing step to clean, filter, or split data into manageable chunks before core processing. ByteNite’s Partitioning Engine handles this pre-processing and task fan-out, distributing your workload across multiple parallel workers.

Whether you’re working with structured tables, unstructured media files, or semi-structured logs, ByteNite’s partitioners support a variety of fan-out strategies.

Examples of task fan-out use cases

Data Type	Partitioning Engine Examples
Structured Data	- Sharding by row/item count - Sharding by date range or key
Semi-Structured Data	- Key extraction and object fan-out - Log file splitting by timestamp
Unstructured Data	Text/Code - Document splitting by section or size - Codebase sharding by file/module Image - Image tiling - Batch splitting for inference Audio - Time-based audio chunking - Silence detection-based chunking - Language segment splitting Video - Frame-based video chunking - Scene detection-based chunking - Resolution-specific splitting
Any	- Task replication for redundancy - Passthrough (no fan-out)

If your workflow doesn’t require splitting data into tasks, you can use a passthrough partitioner to skip the fan-out phase.

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🧠 Core Task Execution

The App represents the core logic of your distributed job—this is where the heavy lifting happens. Whether it’s AI inference, media rendering, data transformation, or scientific computation, Apps execute these workloads in parallel across the data chunks produced by the partitioner.

You bring your container image with the necessary code and dependencies; ByteNite handles the rest: container orchestration, retries, scaling, and resource management.

Examples of core processing use cases

Category	Example Workloads
AI/ML	- Model inference (e.g., object detection, language models) - Model training on distributed datasets - Feature extraction pipelines
Data Processing	- ETL (Extract, Transform, Load) operations - Batch processing of logs or events - Data anonymization or sanitization
Media Processing	- Audio transcription - Image classification or enhancement - Video transcoding or thumbnail generation
Scientific Computing	- Genomic sequence analysis - Simulation workloads - Complex mathematical computations
Other	- Web scraping at scale - Document parsing and conversion - File format conversions

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📥 Task Fan-In and Data Post-Processing

After core processing, results from each task may need to be collected and aggregated. The Assembling Engine performs this fan-in and post-processing, allowing you to organize or transform the results before outputting them to the final destination.

This stage can be as simple as zipping files together or as complex as reassembling a video stream.

Examples of task fan-in use cases

Data Type	Assembling Engine Examples
Structured Data	- Data merging based on keys - Sorted concatenation of CSV/JSON files
Semi-Structured Data	- Log file aggregation - Schema validation and merging
Unstructured Data	Text/Code - Document stitching (e.g., combining chapters) - Codebase reassembly Image - Batch packaging of images - Mosaic creation from tiles Audio - Concatenation of audio chunks - Index-based reassembly Video - Video stream stitching - Scene-ordered assembly of clips
Any	- File zipping - Passthrough (no fan-in)

If no post-processing is required, a passthrough assembler can output task results directly.