A high-performance framework built on "Everything is Flow" design philosophy, leveraging Kotlin's modern features for clean, solid, and scalable data processing.
Every operation in ApexFlow is represented as a Flow<T>, enabling seamless composition and reactive stream processing.
- Single Responsibility: Each component has one clear purpose
- Open/Closed: Extensible through plugins and composition
- Liskov Substitution: Interchangeable components
- Interface Segregation: Focused, minimal interfaces
- Dependency Inversion: Flow-based dependency management
Traditional Sequential Approach
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Read All │ │ Wait │ │ Write All │
│ Pages │────│ (Idle) │────│ Pages │
│ (T1) │ │ │ │ (T2) │
└─────────────┘ └─────────────┘ └─────────────┘
Total Time: T1 + T2
ApexFlow Streaming Approach
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Read │ │ Process │ │ Write │
│ Page 1 │────│ Page 1 │────│ Page 1 │
└─────────────┘ └─────────────┘ └─────────────┘
│ │ │
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Read │ │ Process │ │ Write │
│ Page 2 │────│ Page 2 │────│ Page 2 │
└─────────────┘ └─────────────┘ └─────────────┘
│ │ │
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Read │ │ Process │ │ Write │
│ Page 3 │────│ Page 3 │────│ Page 3 │
└─────────────┘ └─────────────┘ └─────────────┘
Total Time: max(T1, T2)
Theoretical Performance Gain
📊 Example: 406-page PDF conversion
- Traditional: 90s (50s reading + 40s writing)
- ApexFlow: 50s (reading and writing overlap)
- Theoretical Gain: 1.8x faster
Scaling with Complexity
As workflows become more complex, ApexFlow's advantage grows exponentially:
| Workflow Steps | Traditional | ApexFlow | Theoretical Gain |
|---|---|---|---|
| 2 steps | T1 + T2 | max(T1,T2) | (T1+T2)/max(T1,T2) |
| 5 steps | Σ(T1..T5) | max(T1..T5) | Σ(T1..T5)/max(T1..T5) |
| N steps | Σ(T1..TN) | max(T1..TN) | Σ(T1..TN)/max(T1..TN) |
Why This Works
- No Idle Time: Reading and writing happen simultaneously
- Reduced Memory Footprint: Process pages one at a time
- Backpressure Handling: Automatic flow control
- Parallel Execution: Leverage modern hardware efficiently
// Simple flow composition with + operator
val pipeline = pdfReader + imageProcessor + tiffWriter
// Enhance with plugins
val monitoredPipeline = pipeline
.withPluginTiming()
.withPluginPerformanceMonitoring()- Timing Plugin: Measure execution time per component
- Logging Plugin: Structured logging throughout the pipeline
- Performance Monitoring: Real-time metrics collection
- Custom Plugins: Extensible plugin architecture
- Backpressure-Aware: Automatic flow control
- Resource Management: Automatic cleanup with
use() - Error Handling: Resilient stream processing
- Concurrent Execution: Parallel processing with coroutines
Basic Conversion
// PDF to TIFF
file.toTiff(outputFile)
// TIFF to PDF
file.toPdf(outputFile)
// With custom configuration
file.toTiff(
outputFile,
pdfConfig = { dpi = 300f, skipBlankPages = true },
tiffConfig = { compressionType = "JPEG", compressionQuality = 90f }
)
file.toPdf(
outputFile,
tiffConfig = { bufferSize = 10 },
pdfConfig = { jpegQuality = 0.95f }
)// Using ApexFlow DSL for more control
val converter = apexPdfToTiff(
pdfConfig = { dpi = 300f },
tiffConfig = { compressionType = "JPEG" }
)
converter.convert(inputFile, outputFile)
// Component composition with + operator
val pdfReader = ApexPdfReader.fromFile(inputFile)
val tiffWriter = ApexTiffWriter.toFile(outputFile)
val pipeline = pdfReader + tiffWriter
pipeline.execute()- Reduced Latency: Stream processing eliminates wait times
- Memory Efficiency: Process pages one at a time, no full dataset loading
- Scalability: Performance scales with workflow complexity
- Clean Code: Minimal boilerplate, maximum clarity
- Composability: Reusable components with
+operator - Testability: Isolated, testable flow components
- Maintainability: Clear separation of concerns
- Monitoring: Built-in performance metrics
- Debugging: Flow visualization and tracing
- Extensibility: Plugin-based architecture
- Document Conversion: PDF ↔ TIFF with streaming processing
- Data Pipelines: ETL workflows with multiple processing steps
- Real-time Processing: Continuous data stream handling
- Batch Processing: Large dataset processing with memory efficiency
ApexFlow: Where complexity meets simplicity through the power of flow composition.