🟢 Medium: Implement parallel data processing pipelines #16
Description
🟢 Medium: Implement parallel data processing pipelines
Issue Summary
RDMP processes data sequentially through pipeline stages, missing opportunities for parallel processing of independent operations and underutilizing multi-core CPU resources.
🚨 Medium Impact
- CPU Underutilization: Sequential processing on multi-core systems
- Poor Performance: Independent operations processed one at a time
- Scalability Limits: Cannot leverage modern multi-core hardware efficiently
- Extended Processing Time: Large datasets take longer than necessary
🔍 Current Problems
1. Sequential Pipeline Processing
// DataFlowPipelineEngine.cs - Sequential processing
public void ExecutePipeline(GracefulCancellationToken cancellationToken)
{
foreach (var component in Components)
{
currentChunk = component.ProcessPipelineData(currentChunk, _listener, cancellationToken);
// Each component waits for previous to complete
}
}Problem: No parallelization of independent components
2. Sequential File Processing
// Multiple files processed sequentially
foreach (var file in filesToProcess)
{
var result = ProcessFile(file); // Wait for each file
results.Add(result);
}Problem: Independent files processed one at a time
3. Sequential Data Transformations
// Transformations applied sequentially
var data1 = Transform1(inputData);
var data2 = Transform2(data1);
var data3 = Transform3(data2);Problem: Independent transformations not parallelized
4. Single-threaded Validation
// Validation performed sequentially
foreach (var item in itemsToValidate)
{
var result = Validate(item); // Sequential validation
}Problem: Independent validation not parallelized
📊 Performance Impact
| CPU Cores | Sequential Processing | Parallel Processing | Improvement |
|---|---|---|---|
| 2 cores | 100% time | ~60% time | 1.7x |
| 4 cores | 100% time | ~35% time | 2.9x |
| 8 cores | 100% time | ~20% time | 5x |
| 16 cores | 100% time | ~12% time | 8x |
🛠 Recommended Solution
1. Parallel Pipeline Processing
public class ParallelDataFlowPipelineEngine
{
public async Task ExecutePipelineAsync(GracefulCancellationToken cancellationToken)
{
var currentChunk = await Source.GetChunkAsync(_listener, cancellationToken);
// Identify independent components that can run in parallel
var independentGroups = GroupIndependentComponents(Components);
foreach (var group in independentGroups)
{
if (group.Count == 1)
{
// Sequential processing for dependent components
currentChunk = await group[0].ProcessPipelineDataAsync(currentChunk, _listener, cancellationToken);
}
else
{
// Parallel processing for independent components
currentChunk = await ProcessComponentsInParallelAsync(group, currentChunk, _listener, cancellationToken);
}
}
await Destination.ProcessPipelineDataAsync(currentChunk, _listener, cancellationToken);
}
private async Task<DataTable> ProcessComponentsInParallelAsync(
List<IDataFlowComponent> components,
DataTable currentChunk,
IDataLoadEventListener listener,
GracefulCancellationToken cancellationToken)
{
var parallelOptions = new ParallelOptions
{
CancellationToken = cancellationToken.AbortToken,
MaxDegreeOfParallelism = Environment.ProcessorCount
};
// Create independent copies for parallel processing
var tasks = components.Select(async component =>
{
var chunkCopy = currentChunk.Copy(); // Create independent copy
return await component.ProcessPipelineDataAsync(chunkCopy, listener, cancellationToken);
}).ToArray();
var results = await Task.WhenAll(tasks);
// Merge results (implementation depends on component types)
return MergeParallelResults(results, currentChunk);
}
}2. Parallel File Processing
public class ParallelFileProcessor
{
public async Task<List<T>> ProcessFilesAsync<T>(IEnumerable<string> filePaths, Func<string, Task<T>> processor, CancellationToken cancellationToken = default)
{
var parallelOptions = new ParallelOptions
{
CancellationToken = cancellationToken,
MaxDegreeOfParallelism = Math.Min(Environment.ProcessorCount, 8) // Limit concurrent file I/O
};
var results = new ConcurrentBag<T>();
await Task.Run(async () =>
{
await Parallel.ForEachAsync(filePaths, parallelOptions, async (filePath, ct) =>
{
var result = await processor(filePath);
results.Add(result);
});
}, cancellationToken);
return results.ToList();
}
}3. Parallel Data Validation
public class ParallelDataValidator
{
public async Task<ValidationResult[]> ValidateAsync<T>(IEnumerable<T> items, Func<T, Task<ValidationResult>> validator, CancellationToken cancellationToken = default)
{
var parallelOptions = new ParallelOptions
{
CancellationToken = cancellationToken,
MaxDegreeOfParallelism = Environment.ProcessorCount
};
var results = new ConcurrentBag<ValidationResult>();
var itemsArray = items.ToArray();
await Parallel.ForAsync(0, itemsArray.Length, parallelOptions, async (i, ct) =>
{
var item = itemsArray[i];
var result = await validator(item);
results.Add(result);
});
return results.ToArray();
}
}4. Parallel Data Transformation
public class ParallelDataTransformer
{
public async Task<DataTable> TransformAsync(DataTable inputTable, params Func<DataTable, Task<DataTable>>[] transformations)
{
if (transformations.Length == 1)
return await transformations[0](inputTable);
// Run transformations in parallel with independent copies
var tasks = transformations.Select(async transform =>
{
var copy = inputTable.Copy();
return await transform(copy);
}).ToArray();
var results = await Task.WhenAll(tasks);
// Merge results (implementation depends on transformation types)
return MergeTransformationResults(results, inputTable);
}
}5. Smart Component Dependency Analysis
public class ComponentDependencyAnalyzer
{
public List<List<IDataFlowComponent>> GroupIndependentComponents(List<IDataFlowComponent> components)
{
var dependencyGraph = BuildDependencyGraph(components);
var independentGroups = new List<List<IDataFlowComponent>>();
var processed = new HashSet<IDataFlowComponent>();
while (processed.Count < components.Count)
{
var currentLevel = new List<IDataFlowComponent>();
foreach (var component in components)
{
if (processed.Contains(component)) continue;
// Check if all dependencies are processed
var dependencies = GetDependencies(component, dependencyGraph);
if (dependencies.All(dep => processed.Contains(dep)))
{
currentLevel.Add(component);
}
}
if (currentLevel.Count == 0)
throw new InvalidOperationException("Circular dependency detected in pipeline components");
independentGroups.Add(currentLevel);
foreach (var component in currentLevel)
processed.Add(component);
}
return independentGroups;
}
}6. Resource-Aware Parallel Processing
public class ResourceAwareParallelProcessor
{
private readonly SemaphoreSlim _cpuSemaphore;
private readonly SemaphoreSlim _ioSemaphore;
public ResourceAwareParallelProcessor()
{
_cpuSemaphore = new SemaphoreSlim(Environment.ProcessorCount);
_ioSemaphore = new SemaphoreSlim(10); // Limit concurrent I/O operations
}
public async Task<T[]> ProcessInParallelAsync<T>(IEnumerable<Func<Task<T>>> operations, OperationType operationType)
{
var semaphore = operationType == OperationType.CPU ? _cpuSemaphore : _ioSemaphore;
var tasks = operations.Select(async operation =>
{
await semaphore.WaitAsync();
try
{
return await operation();
}
finally
{
semaphore.Release();
}
}).ToArray();
return await Task.WhenAll(tasks);
}
}
public enum OperationType
{
CPU,
I/O
}🎯 Implementation Plan
Phase 1 (Week 1): Core Parallel Infrastructure
- Implement ParallelDataFlowPipelineEngine
- Create component dependency analysis
- Add resource-aware parallel processing
Phase 2 (Week 2): Parallel File and Data Processing
- Implement ParallelFileProcessor
- Create ParallelDataValidator
- Add parallel data transformation support
Phase 3 (Week 3): Integration and Optimization
- Integrate parallel processing into existing pipelines
- Add performance monitoring and metrics
- Optimize parallelization strategies
Phase 4 (Week 4): Testing and Validation
- Create comprehensive performance benchmarks
- Add unit tests for parallel processing
- Validate thread safety and correctness
✅ Acceptance Criteria
- Independent pipeline components processed in parallel
- CPU utilization scales with core count
- Performance tests show 2-8x improvement on multi-core systems
- Thread safety maintained across all parallel operations
- Resource limits prevent system overload
- Configuration options for parallelization degree
🔍 Areas Requiring Updates
High Priority:
- DataFlowPipelineEngine - Core pipeline parallelization
- File Processing Components - Parallel file operations
- Data Validation Components - Parallel validation
- Data Transformation Components - Parallel transformations
Medium Priority:
- Export Operations - Parallel data export
- Import Operations - Parallel data import
- Reporting Components - Parallel report generation
- Background Processing - Parallel background tasks
Low Priority:
- Logging Operations - Parallel logging (if needed)
- Cache Operations - Parallel cache warming
- Plugin Loading - Parallel plugin discovery
- Configuration Loading - Parallel config loading
📈 Expected Impact
- Performance: 2-8x improvement on multi-core systems
- CPU Utilization: 80-95% CPU usage during processing
- Scalability: Linear performance scaling with core count
- Resource Efficiency: Better utilization of available hardware
- Processing Time: 50-90% reduction for large datasets
🔗 Related Issues
- Async database operations conversion
- Performance bottleneck analysis
- Thread safety improvements
- Resource management optimization