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Video Ingestion Flow: From Upload to Vector Database Storage

Overview

This document provides a comprehensive visual flow of the video ingestion process in the VDMS DataPrep microservice, from initial upload through frame extraction, embedding generation, and final storage in the vector database. The process has been optimized for performance with parallel processing, batch operations, and support for both API and SDK embedding modes.

High-Level Architecture

graph TB
    subgraph "Entry Points"
        A1[POST /videos/upload<br/>Direct File Upload]
        A2[POST /videos/minio<br/>Process from MinIO]
    end
    
    subgraph "Mode Selection"
        B[Processing Mode Selection<br/>SDK vs API Mode]
    end
    
    subgraph "Core Processing Pipeline"
        C[Frame Extraction]
        D[Object Detection]
        E[Batch Creation]
        F[Parallel Processing]
        G[Embedding Generation]
        H[Vector DB Storage]
    end
    
    subgraph "Storage Layer"
        I[(VDMS Vector Database)]
        J[(MinIO Object Storage)]
    end
    
    A1 --> J
    A1 --> B
    A2 --> J
    A2 --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I
    
    style B fill:#e1f5ff
    style F fill:#fff9e6
    style I fill:#e8f5e9
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Detailed Video Ingestion Flow

Stage 1: Video Upload & Initial Processing

flowchart TD
    START([Video Upload Request]) --> ENTRY{Entry Point?}
    
    ENTRY -->|Direct Upload| UPLOAD[POST /videos/upload<br/>File: video.mp4<br/>Params: frame_interval, enable_detection]
    ENTRY -->|MinIO Reference| MINIO[POST /videos/minio<br/>Params: bucket_name, video_id]
    
    UPLOAD --> VALIDATE1[Validate File<br/>- Check MP4 format<br/>- Check size limit 500MB<br/>- Validate parameters]
    MINIO --> VALIDATE2[Validate MinIO Path<br/>- Check bucket exists<br/>- Verify video_id directory<br/>- Validate video_name]
    
    VALIDATE1 --> STORE[Store Video to MinIO<br/>Path: bucket/video_id/filename.mp4]
    VALIDATE2 --> DOWNLOAD[Download from MinIO<br/>Get video content]
    
    STORE --> CONFIG[Load Configuration<br/>• frame_interval default: 15<br/>• enable_object_detection: true<br/>• detection_confidence: 0.85]
    DOWNLOAD --> CONFIG
    
    CONFIG --> MODE_SELECT{Processing Mode?<br/>EMBEDDING_PROCESSING_MODE}
    
    MODE_SELECT -->|SDK Mode| SDK[SDK Mode Selected<br/>✓ Direct function calls<br/>✓ Memory-based processing<br/>✓ OpenVINO optimized]
    MODE_SELECT -->|API Mode| API[API Mode Selected<br/>✓ HTTP API calls<br/>✓ File-based processing<br/>✓ Traditional approach]
    
    SDK --> FRAME_EXTRACT
    API --> FRAME_EXTRACT
    
    FRAME_EXTRACT[Stage 2: Frame Extraction]
    
    style START fill:#e3f2fd
    style SDK fill:#c8e6c9
    style API fill:#fff9c4
    style FRAME_EXTRACT fill:#ffe0b2
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Key Decisions:

  • Entry Point Selection: Direct upload saves to MinIO first; MinIO processing retrieves from existing storage
  • Mode Selection: SDK mode (optimized) vs API mode (traditional HTTP calls)
  • Configuration Priority: Request params → Config file defaults → Service defaults

Performance Factors:

  • File validation is minimal overhead (~ms)
  • MinIO upload/download depends on video size and network
  • SDK mode eliminates network latency for embeddings

Stage 2: Frame Extraction & Metadata Creation

flowchart TD
    START[Frame Extraction Stage] --> VIDEO_INFO[Read Video Information<br/>Using Decord VideoReader]
    
    VIDEO_INFO --> CALC[Calculate Video Metrics<br/>• Total Frames: len vr<br/>• FPS: vr.get_avg_fps<br/>• Duration: frames / fps]
    
    CALC --> INTERVAL[Calculate Frame Indices<br/>frame_indices = range 0, total_frames, frame_interval<br/><br/>Example: 900 frames, interval=15<br/>→ Extract 60 frames]
    
    INTERVAL --> EXTRACT_LOOP{For each<br/>frame_index}
    
    EXTRACT_LOOP --> SEEK[Seek to Frame<br/>vr at frame_idx]
    
    SEEK --> CONVERT[Convert Tensor to NumPy<br/>• Handle decord NDArray<br/>• Ensure uint8 format<br/>• Verify shape H,W,C]
    
    CONVERT --> META[Create Frame Metadata<br/>frame_metadata = <br/>• frame_id: video_id_framenum<br/>• frame_number<br/>• timestamp: frame_idx/fps<br/>• frame_type: 'full_frame'<br/>• video_id, filename, bucket<br/>• video_url, video_rel_url<br/>• tags<br/>• fps, total_frames, duration]
    
    META --> STORE_FRAME[Store Frame Array<br/>frames.append frame_numpy<br/>frames_metadata.append metadata]
    
    STORE_FRAME --> MORE{More frames?}
    
    MORE -->|Yes| EXTRACT_LOOP
    MORE -->|No| COMPLETE[Extraction Complete<br/>Result: List of numpy arrays + metadata]
    
    COMPLETE --> DETECT_STAGE[Stage 3: Object Detection]
    
    style START fill:#ffe0b2
    style CALC fill:#e1f5fe
    style EXTRACT_LOOP fill:#fff9c4
    style COMPLETE fill:#c8e6c9
    style DETECT_STAGE fill:#f8bbd0
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Optimization Highlights:

  1. Decord Library: GPU-capable video reading (though currently using CPU context for reliability)
  2. Memory Efficiency: Frames stored as numpy arrays, not written to disk in SDK mode
  3. Metadata Richness: Comprehensive frame metadata for search/retrieval

Performance Metrics:

  • Frame Extraction Time: Typically 0.5-2s for 60 frames from 30s video
  • Memory Usage: ~4MB per 1080p frame (uncompressed)
  • Extraction Rate: ~30-100 frames/second depending on video resolution

Stage 3: Object Detection (Optional)

flowchart TD
    START[Object Detection Stage] --> CHECK{Object Detection<br/>Enabled?}
    
    CHECK -->|Disabled| SKIP[Skip Detection<br/>Use full frames only<br/>frames remain unchanged]
    CHECK -->|Enabled| INIT[Initialize YOLOX Detector<br/>• Model: yolox_s<br/>• Device: CPU/GPU<br/>• Confidence: 0.85<br/>• Input Size: 640x640<br/>• NMS Threshold: 0.45]
    
    INIT --> BATCH_DETECT[Create Detection Batches<br/>detection_batch_size = 32<br/><br/>Example: 60 frames → 2 batches]
    
    BATCH_DETECT --> PARALLEL_DETECT{Process Batches<br/>in Parallel}
    
    PARALLEL_DETECT --> BATCH_PROC[For Each Batch]
    
    BATCH_PROC --> FRAME_LOOP{For each frame<br/>in batch}
    
    FRAME_LOOP --> CONVERT_PIL[Convert Frame to PIL<br/>Image.fromarray frame_numpy]
    
    CONVERT_PIL --> DETECT[Run YOLOX Detection<br/>detector.detect frame_pil]
    
    DETECT --> ADD_FULL[Add Full Frame<br/>frame_type: full_frame<br/>Keep original frame metadata]
    
    ADD_FULL --> CROPS{Detections<br/>Found?}
    
    CROPS -->|Yes| CROP_LOOP{For each<br/>detection}
    
    CROP_LOOP --> EXTRACT_CROP[Extract Crop<br/>• Validate bbox coordinates<br/>• Crop: frame y1:y2, x1:x2<br/>• Convert to PIL Image<br/>• Min size: 10x10 pixels]
    
    EXTRACT_CROP --> CROP_META[Create Crop Metadata<br/>crop_metadata = frame_metadata + <br/>• frame_type: detected_crop<br/>• is_detected_crop: true<br/>• crop_index<br/>• detection_confidence<br/>• crop_bbox x1 y1 x2 y2<br/>• detected_class_id<br/>• detected_label]
    
    CROP_META --> ADD_CROP[Add Crop to Results<br/>all_images.append crop_pil<br/>all_metadata.append crop_meta]
    
    ADD_CROP --> MORE_CROPS{More crops?}
    MORE_CROPS -->|Yes| CROP_LOOP
    MORE_CROPS -->|No| MORE_FRAMES
    
    CROPS -->|No| MORE_FRAMES{More frames?}
    
    MORE_FRAMES -->|Yes| FRAME_LOOP
    MORE_FRAMES -->|No| BATCH_DONE[Batch Complete]
    
    BATCH_DONE --> MORE_BATCHES{More batches?}
    MORE_BATCHES -->|Yes| PARALLEL_DETECT
    MORE_BATCHES -->|No| DETECTION_DONE[Detection Complete<br/><br/>Example Expansion:<br/>60 frames → 240 items<br/>60 full frames + 180 crops<br/>avg 3 objects/frame]
    
    SKIP --> BATCH_CREATE
    DETECTION_DONE --> BATCH_CREATE[Stage 4: Batch Creation]
    
    style START fill:#f8bbd0
    style INIT fill:#e1f5fe
    style PARALLEL_DETECT fill:#fff9c4
    style DETECTION_DONE fill:#c8e6c9
    style BATCH_CREATE fill:#d1c4e9
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Object Detection Details:

Model Specifications:

  • Architecture: YOLOX-S (small variant)
  • Framework: OpenVINO IR format
  • Input Resolution: 640x640 (preprocessed)
  • Classes: 80 COCO categories (person, car, bicycle, etc.)
  • Download: Auto-downloaded from GitHub on first use

Detection Process:

  1. Preprocessing: Image resized to 640x640, normalized
  2. Inference: OpenVINO execution on CPU/GPU
  3. Postprocessing: NMS filtering with threshold 0.45
  4. Crop Extraction: Bounding boxes validated and extracted

Performance Characteristics:

  • Detection Speed: ~50-100ms per frame (CPU), ~10-20ms (GPU)
  • Parallel Batches: 2-4 detection workers typical
  • Expansion Factor: 1.5x to 5x (avg 3 objects/frame)
  • Memory Impact: +20-50% for crop storage

Optimization Strategy:

  • Batched detection reduces overhead
  • Parallel processing utilizes multi-core CPUs
  • Global detector instance reused (no reload per request)
  • Crops validated (min 10x10px, valid coordinates)

Stage 4: Batch Creation for Parallel Processing

flowchart TD
    START[Batch Creation Stage] --> INPUT[Input: List of Images + Metadata<br/><br/>After Detection:<br/>• Full frames: 60<br/>• Detected crops: 180<br/>• Total items: 240]
    
    INPUT --> CONFIG[Get Pipeline Configuration<br/>Based on CPU cores and mode]
    
    CONFIG --> CALC_WORKERS[Calculate Worker Count<br/><br/>OpenVINO Mode:<br/>workers = max 1, cpu_cores // 4<br/>Limited by OV_NUM_STREAMS<br/><br/>PyTorch Mode:<br/>workers = cpu_cores // 16<br/>max 8 workers]
    
    CALC_WORKERS --> CALC_BATCH[Determine Batch Size<br/>batch_size = 32<br/>Optimal for embedding generation]
    
    CALC_BATCH --> CREATE_BATCHES[Create Processing Batches<br/>Split items into batches<br/><br/>Example:<br/>240 items ÷ 32 = 7.5<br/>→ 8 batches<br/>7 batches of 32 items<br/>1 batch of 16 items]
    
    CREATE_BATCHES --> BATCH_STRUCT[Batch Structure<br/>Each batch contains:<br/>• List of PIL Images<br/>• List of metadata dicts<br/>• Batch index<br/>• Total batch count]
    
    BATCH_STRUCT --> SUBMIT[Submit to Thread Pool<br/>ThreadPoolExecutor<br/>max_workers = pipeline_count<br/><br/>Example: 8 batches → 4 workers<br/>2 batches processed simultaneously]
    
    SUBMIT --> PARALLEL[Stage 5: Parallel Processing]
    
    style START fill:#d1c4e9
    style CONFIG fill:#e1f5fe
    style CREATE_BATCHES fill:#fff9c4
    style SUBMIT fill:#c8e6c9
    style PARALLEL fill:#ffccbc
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Pipeline Configuration Logic:

# Pseudo-code for worker calculation
cpu_cores = multiprocessing.cpu_count()

if use_openvino:
    base_workers = max(1, cpu_cores // 4)
    
    # Check OpenVINO environment variables
    ov_limit = check_env_vars([
        'OV_PERFORMANCE_HINT_NUM_REQUESTS',
        'PERFORMANCE_HINT_NUM_REQUESTS', 
        'OV_NUM_STREAMS'
    ])
    
    if ov_limit:
        workers = min(base_workers, ov_limit)
    else:
        workers = base_workers
else:
    # PyTorch is more CPU-intensive
    workers = min(max(1, cpu_cores // 16), 8)

batch_size = 32  # Fixed optimal size

Configuration Examples:

CPU Cores OpenVINO Mode PyTorch Mode
8 cores 2 workers 1 worker
16 cores 4 workers 1 worker
32 cores 8 workers 2 workers
64 cores 16 workers 4 workers
96 cores 24 workers 6 workers

Batch Size Considerations:

  • 32 items per batch: Optimal balance for embedding models
  • Smaller batches: Lower memory, higher overhead
  • Larger batches: Higher memory, better throughput
  • Dynamic adjustment: Future enhancement based on available memory

Stage 5: Parallel Batch Processing Pipeline

flowchart TD
    START[Parallel Processing Stage] --> POOL[Thread Pool Executor<br/>max_workers = pipeline_count<br/><br/>Example: 4 workers<br/>Processing 8 batches]
    
    POOL --> SUBMIT_ALL[Submit All Batches<br/>All batches queued<br/>Workers pick up jobs dynamically]
    
    SUBMIT_ALL --> WORKER1[Worker 1<br/>Process Batch]
    SUBMIT_ALL --> WORKER2[Worker 2<br/>Process Batch]
    SUBMIT_ALL --> WORKER3[Worker 3<br/>Process Batch]
    SUBMIT_ALL --> WORKER4[Worker 4<br/>Process Batch]
    
    WORKER1 --> BATCH_PROC1[Single Batch Processing]
    WORKER2 --> BATCH_PROC1
    WORKER3 --> BATCH_PROC1
    WORKER4 --> BATCH_PROC1
    
    subgraph "Single Batch Processing Pipeline"
        BATCH_PROC1[Start Batch Processing<br/>Input: 32 images + metadata]
        
        BATCH_PROC1 --> STEP1[Step 1: Validation<br/>Check model supports images<br/>Skip if text-only model]
        
        STEP1 --> STEP2[Step 2: Generate Embeddings<br/>SDK Mode: Direct function call<br/>API Mode: HTTP POST request]
        
        STEP2 --> EMB_DETAIL[Embedding Generation Details]
        
        subgraph "Embedding Generation"
            EMB_DETAIL --> EMB_SDK{Mode?}
            
            EMB_SDK -->|SDK| EMB_SDK_PROC[SDK Embedding<br/>• Use global SDK client<br/>• Thread-safe infer_new_request<br/>• No HTTP overhead<br/>• OpenVINO optimized<br/>• Batch processing inside model]
            
            EMB_SDK -->|API| EMB_API_PROC[API Embedding<br/>• HTTP POST to embedding service<br/>• Serialize images to base64<br/>• Network latency<br/>• JSON response parsing<br/>• Model runs remotely]
            
            EMB_SDK_PROC --> EMB_RESULT[Embedding Results<br/>Vector dimensions: 512/768/1024<br/>Format: List of float arrays]
            EMB_API_PROC --> EMB_RESULT
        end
        
        EMB_RESULT --> STEP3[Step 3: Validate Embeddings<br/>Filter out null/failed embeddings<br/>Match embeddings to metadata]
        
        STEP3 --> STEP4[Step 4: Store to Vector DB<br/>Immediate batch storage<br/>Prevents OutOfJournalSpace]
        
        STEP4 --> STORAGE_DETAIL[VDMS Storage Details]
    end
    
    subgraph "VDMS Vector DB Storage"
        STORAGE_DETAIL --> VDB_PREP[Prepare Storage Request<br/>• Embeddings: List of vectors<br/>• Metadata: List of dicts<br/>• Collection: DB_COLLECTION]
        
        VDB_PREP --> VDB_BULK[Bulk Insert Operation<br/>AddEntity with AddDescriptor<br/>Batch operation more efficient]
        
        VDB_BULK --> VDB_INDEX[Vector Index Update<br/>VDMS updates HNSW index<br/>Enables similarity search]
        
        VDB_INDEX --> VDB_RETURN[Return Stored IDs<br/>List of entity IDs<br/>Used for verification]
    end
    
    VDB_RETURN --> BATCH_DONE[Batch Complete<br/>Return results:<br/>• embeddings_count<br/>• stored_ids<br/>• processing_time<br/>• detection_time<br/>• embedding_time<br/>• storage_time]
    
    BATCH_DONE --> COLLECTOR[Results Collector<br/>as_completed future]
    
    COLLECTOR --> MORE{More batches<br/>pending?}
    
    MORE -->|Yes| COLLECTOR
    MORE -->|No| AGGREGATE[Aggregate All Results<br/>Sum embeddings<br/>Combine stored_ids<br/>Calculate statistics]
    
    AGGREGATE --> FINAL[Stage 6: Final Results]
    
    style START fill:#ffccbc
    style EMB_SDK_PROC fill:#c8e6c9
    style EMB_API_PROC fill:#fff9c4
    style VDB_BULK fill:#e8eaf6
    style BATCH_DONE fill:#c8e6c9
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Parallel Processing Characteristics:

Thread Pool Execution:

  • Dynamic Work Distribution: Batches processed as workers become available
  • True Parallelism: Multiple batches processed simultaneously
  • Completion Order: Batches complete in any order (not sequential)
  • Timeout Protection: 300s per batch maximum

Embedding Generation Comparison:

Aspect SDK Mode API Mode
Method Direct function call HTTP POST request
Network None (in-process) HTTP overhead
Serialization None (PIL objects) Base64 encoding
Latency ~50-200ms/batch ~200-800ms/batch
Throughput Higher Lower
Memory Shared with service Separate processes
Optimization OpenVINO compiled Model-dependent

Vector DB Storage Strategy:

Why Immediate Batch Storage:

  • Prevents Memory Overflow: Storing after each batch prevents accumulation
  • Protects Against Failures: Partial results saved even if pipeline fails
  • Avoids VDMS Issues: Prevents OutOfJournalSpace errors
  • Progress Tracking: Stored IDs returned incrementally

Bulk Insert Benefits:

  • Reduced Overhead: Single VDMS transaction per batch
  • Index Efficiency: VDMS can optimize index updates
  • Faster Than Individual: ~10x faster than per-item inserts

Stage 6: Results Aggregation & Performance Metrics

flowchart TD
    START[Results Aggregation] --> COLLECT[Collect All Batch Results<br/>From ThreadPoolExecutor.as_completed]
    
    COLLECT --> AGGREGATE[Aggregate Statistics]
    
    subgraph "Statistics Calculation"
        AGGREGATE --> COUNT[Total Embeddings<br/>Sum all batch counts<br/><br/>Example: 8 batches<br/>7×32 + 1×16 = 240 embeddings]
        
        COUNT --> IDS[Stored IDs<br/>Concatenate all ID lists<br/>Verify no duplicates]
        
        IDS --> TIMES[Processing Times<br/>Calculate per-stage statistics]
        
        TIMES --> TIME_DETAIL[Time Breakdown]
        
        subgraph "Time Statistics"
            TIME_DETAIL --> EXTRACT_TIME[Frame Extraction<br/>Total time: e.g., 1.2s<br/>Frames extracted: 60]
            
            EXTRACT_TIME --> DETECT_TIME[Object Detection<br/>Avg per batch: e.g., 0.8s<br/>Max per batch: e.g., 1.2s<br/>% of batch time: e.g., 25%]
            
            DETECT_TIME --> EMBED_TIME[Embedding Generation<br/>Avg per batch: e.g., 2.1s<br/>Max per batch: e.g., 3.5s<br/>% of batch time: e.g., 65%]
            
            EMBED_TIME --> STORE_TIME[Vector DB Storage<br/>Avg per batch: e.g., 0.3s<br/>Max per batch: e.g., 0.5s<br/>% of batch time: e.g., 10%]
            
            STORE_TIME --> TOTAL_TIME[Total Pipeline Time<br/>Wall clock time: e.g., 8.5s<br/>Sequential would be: ~28s<br/>Speedup: 3.3x]
        end
    end
    
    TIME_DETAIL --> FRAME_COUNTS[Frame Count Summary]
    
    subgraph "Frame Flow Tracking"
        FRAME_COUNTS --> INPUT_FRAMES[Input Frames<br/>Extracted from video: 60]
        
        INPUT_FRAMES --> POST_DETECT[Post-Detection Items<br/>After object detection: 240<br/>Expansion: 4x]
        
        POST_DETECT --> STORED_EMBS[Stored Embeddings<br/>Successfully stored: 240<br/>Success rate: 100%]
    end
    
    STORED_EMBS --> BATCH_STATS[Batch Statistics]
    
    subgraph "Batch Performance"
        BATCH_STATS --> BATCH_COUNT[Batches Processed<br/>Total: 8 batches]
        
        BATCH_COUNT --> AVG_BATCH[Average Batch Time<br/>e.g., 3.5s per batch]
        
        AVG_BATCH --> MAX_BATCH[Max Batch Time<br/>e.g., 4.2s slowest batch]
        
        MAX_BATCH --> EFFICIENCY[Processing Efficiency<br/>Parallel overhead: ~15%<br/>Resource utilization: 85%]
    end
    
    EFFICIENCY --> RETURN_RESULT[Return Final Result]
    
    subgraph "API Response"
        RETURN_RESULT --> RESPONSE[HTTP Response<br/>Status: 201 CREATED<br/>Message: Embeddings created successfully]
        
        RESPONSE --> RESPONSE_BODY[Response Body Example]
        
        RESPONSE_BODY --> JSON[JSON Response:<br/>status: success<br/>message: Embeddings created<br/>total_embeddings: 240<br/>total_frames_processed: 60<br/>frame_interval: 15<br/>processing_mode: sdk_pipeline<br/>timing: extraction, parallel, storage<br/>frame_counts: extracted, detected, stored]
    end
    
    JSON --> COMPLETE([Processing Complete])
    
    style START fill:#e1bee7
    style COLLECT fill:#e1f5fe
    style TIME_DETAIL fill:#fff9c4
    style FRAME_COUNTS fill:#c8e6c9
    style BATCH_STATS fill:#ffccbc
    style COMPLETE fill:#a5d6a7
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Performance Metrics Explained:

Time Statistics:

  1. Frame Extraction Time: Time to read video and extract frames

    • Depends on: Video size, resolution, codec, storage speed
    • Typical: 0.5-3s for 60 frames

  2. Detection Time per Batch: Time for object detection per batch

    • Depends on: Frame resolution, object count, CPU/GPU speed
    • Typical: 0.5-2s per batch of 32 items

  3. Embedding Time per Batch: Time to generate embeddings

    • Depends on: Model size, device, batch size
    • Typical SDK: 1-3s per batch, API: 2-5s per batch

  4. Storage Time per Batch: Time to store in VDMS

    • Depends on: Batch size, VDMS load, network latency
    • Typical: 0.2-0.8s per batch

  5. Pipeline Wall Time: Total end-to-end time

    • Benefits from parallelization
    • Typical speedup: 2-4x vs sequential

Frame Flow Tracking:

The system tracks three critical counts:

  1. Extracted Frames: Original frames from video (e.g., 60)
  2. Post-Detection Items: After adding crops (e.g., 240 = 60 + 180 crops)
  3. Stored Embeddings: Successfully stored in vector DB (e.g., 240)

Efficiency Calculations:

Expansion Factor = Post-Detection Items / Extracted Frames
                 = 240 / 60 = 4x

Success Rate = Stored Embeddings / Post-Detection Items
             = 240 / 240 = 100%

Theoretical Sequential Time = Batches × Max Batch Time
                            = 8 × 4.2s = 33.6s

Actual Parallel Time = 8.5s

Speedup = 33.6s / 8.5s = 3.95x

Efficiency = (Theoretical Sequential / Workers) / Actual
           = (33.6s / 4) / 8.5s = 8.4s / 8.5s = 98.8%

Complete End-to-End Flow Visualization

graph TB
    subgraph "Stage 1: Video Upload & Mode Selection"
        A[Video Upload<br/>POST /videos/upload or /videos/minio] --> B{Processing<br/>Mode?}
        B -->|SDK| C[SDK Mode<br/>Memory Processing]
        B -->|API| D[API Mode<br/>File Processing]
    end
    
    subgraph "Stage 2: Frame Extraction"
        C --> E[Extract Frames<br/>Decord VideoReader]
        D --> E
        E --> F[Frame List<br/>60 frames @ interval=15<br/>Time: 1.2s]
    end
    
    subgraph "Stage 3: Object Detection Optional"
        F --> G{Object Detection<br/>Enabled?}
        G -->|Yes| H[YOLOX Detection<br/>Parallel batches<br/>Time: 2.4s total]
        G -->|No| I[Skip Detection]
        H --> J[Expanded List<br/>240 items<br/>60 frames + 180 crops]
        I --> K[Original List<br/>60 items]
    end
    
    subgraph "Stage 4: Batch Creation"
        J --> L[Create Batches<br/>8 batches of 32 items<br/>batch_size=32]
        K --> L
        L --> M[Calculate Workers<br/>4 parallel workers<br/>OpenVINO optimized]
    end
    
    subgraph "Stage 5: Parallel Processing"
        M --> N[Submit to ThreadPool<br/>Process batches in parallel]
        
        N --> O1[Worker 1: Batch 1<br/>32 items → embeddings → store]
        N --> O2[Worker 2: Batch 2<br/>32 items → embeddings → store]
        N --> O3[Worker 3: Batch 3<br/>32 items → embeddings → store]
        N --> O4[Worker 4: Batch 4<br/>32 items → embeddings → store]
        
        O1 --> P[Collect Results<br/>as batches complete]
        O2 --> P
        O3 --> P
        O4 --> P
    end
    
    subgraph "Stage 6: Storage & Results"
        P --> Q[Aggregate Statistics<br/>240 embeddings stored<br/>Pipeline time: 8.5s]
        Q --> R[(VDMS Vector DB<br/>240 vectors indexed)]
        Q --> S[Return Response<br/>Status: 201 CREATED<br/>Details: timing + counts]
    end
    
    style C fill:#c8e6c9
    style D fill:#fff9c4
    style H fill:#e1f5fe
    style N fill:#ffccbc
    style R fill:#e8f5e9
    style S fill:#a5d6a7
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Performance Optimization Summary

Critical Optimizations

  1. Parallel Processing

    • Implementation: ThreadPoolExecutor with dynamic worker count
    • Impact: 3-4x speedup vs sequential processing
    • Configuration: Auto-calculated based on CPU cores and mode

  2. Batch Storage

    • Implementation: Store after each batch instead of all at end
    • Impact: Prevents memory overflow and VDMS journal errors
    • Benefit: Fault tolerance - partial results saved

  3. SDK Mode

    • Implementation: Direct function calls to embedding service
    • Impact: Eliminates HTTP/network overhead
    • Performance: 2-3x faster than API mode

  4. Object Detection Optimization

    • Implementation: Global detector instance, parallel detection batches
    • Impact: Avoids model reload, utilizes multi-core CPUs
    • Caching: Detector initialized once, reused across requests

  5. Memory-Based Processing (SDK)

    • Implementation: Process video directly from bytes in memory
    • Impact: Eliminates disk I/O overhead
    • Benefit: Lower latency, reduced disk wear

Configuration Parameters

Parameter Default Impact Tuning Guide
frame_interval 15 Frame extraction density Lower = more frames (slower, more detail)
batch_size 32 Items per embedding batch Fixed optimal value
pipeline_count auto Parallel workers CPU cores ÷ 4 (OpenVINO) or ÷ 16 (PyTorch)
detection_confidence 0.85 Object detection threshold Higher = fewer detections (faster)
enable_object_detection true Crop extraction Disable for 4x fewer embeddings (faster)

Performance Expectations

Example Video: 30 seconds, 30fps (900 frames), 1920x1080

Configuration Frames Extracted Items After Detection Total Time Speedup
interval=15, detection=ON, SDK 60 240 (avg 3 crops/frame) 8.5s 3.95x
interval=15, detection=OFF, SDK 60 60 4.2s 4.2x
interval=30, detection=ON, SDK 30 120 4.8s 3.8x
interval=15, detection=ON, API 60 240 18.5s 1.8x

Timing Breakdown (SDK Mode, Detection ON):

  • Frame Extraction: 1.2s (14%)
  • Object Detection: 2.4s (28%)
  • Embedding Generation: 4.2s (49%)
  • Vector DB Storage: 0.7s (8%)
  • Total Pipeline: 8.5s (100%)

System Architecture Context

Component Interaction

graph LR
    subgraph "VDMS DataPrep Microservice"
        A[FastAPI Endpoints] --> B[Video Processing]
        B --> C[Frame Extraction]
        C --> D[Object Detection<br/>YOLOX]
        D --> E[Embedding Helper]
    end
    
    subgraph "Embedding Service"
        F[API Mode<br/>HTTP Endpoint]
        G[SDK Mode<br/>Direct Functions]
    end
    
    subgraph "Storage Layer"
        H[(VDMS Vector DB)]
        I[(MinIO Object Storage)]
    end
    
    E -->|API Mode| F
    E -->|SDK Mode| G
    F --> H
    G --> H
    A --> I
    
    style G fill:#c8e6c9
    style H fill:#e8f5e9
    style I fill:#fff3e0
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Service Dependencies

  1. VDMS Vector Database

    • Stores embeddings with metadata
    • Provides similarity search
    • HNSW index for fast retrieval

  2. MinIO Object Storage

    • Stores original videos
    • Bucket-based organization
    • Provides video download URLs

  3. Multimodal Embedding Service

    • Generates embeddings from images
    • Supports API and SDK modes
    • OpenVINO or PyTorch backend

  4. YOLOX Object Detection

    • OpenVINO IR format
    • CPU/GPU inference
    • Auto-downloads model files

Troubleshooting & Monitoring

Key Metrics to Monitor

  1. Frame Extraction Rate: Frames/second during extraction

    • Target: >30 frames/second
    • Alert: <10 frames/second indicates I/O bottleneck

  2. Object Detection Time: Average seconds per batch

    • Target: <2s per batch of 32 items
    • Alert: >5s indicates CPU/GPU overload

  3. Embedding Generation Time: Average seconds per batch

    • Target SDK: <3s per batch, API: <6s per batch
    • Alert: >10s indicates model/network issues

  4. Storage Time: Average seconds per batch

    • Target: <1s per batch
    • Alert: >3s indicates VDMS performance issues

  5. Success Rate: Stored embeddings / Expected embeddings

    • Target: >95%
    • Alert: <90% indicates embedding failures

Common Performance Issues

Issue Symptom Cause Solution
Slow extraction High frame_extraction_time Large video, slow storage Use faster storage, reduce resolution
Detection bottleneck High detection_time CPU overload Enable GPU, reduce confidence threshold
Embedding slowdown High embedding_time Model overload Increase workers, enable OpenVINO
Storage delays High storage_time VDMS overload Check VDMS resources, reduce batch size
Memory errors Process killed Too many workers Reduce pipeline_count

Logs to Check

# Frame extraction progress
"Extracting X frames with interval Y"
"Frame extraction completed in Xs: Y frames"

# Object detection status
"Processing object detection in batches of X"
"Detection batch processed: Y frames → Z items"

# Parallel processing progress
"Processing X frames with Y maximum parallel workers"
"Batch N/M completed: X embeddings stored"

# Final results
"Embedding generation pipeline completed in Xs: Y embeddings across Z batches"
"Frame flow summary: extracted=X -> after_detection=Y -> stored=Z"

Conclusion

The VDMS DataPrep video ingestion pipeline is a highly optimized system that efficiently processes videos for semantic search. Key achievements:

✅ Parallel Processing: 3-4x speedup through multi-threaded execution
✅ Batch Storage: Prevents memory overflow with incremental saves
✅ SDK Optimization: 2-3x faster than traditional API calls
✅ Object Detection: Expands search coverage with detected crops
✅ Memory Efficiency: Direct memory processing in SDK mode
✅ Comprehensive Metrics: Detailed timing and statistics for optimization

Total Processing Time: 8-10 seconds for a 30-second video (SDK mode, detection ON)

Scalability: Handles videos up to 500MB, auto-configures workers based on available resources

References

  • Source Code: /home/sdp/workbench/integration/edge-ai-libraries-mme-v2/microservices/visual-data-preparation-for-retrieval/vdms/
  • Configuration: src/config.yaml
  • API Endpoints: src/endpoints/video_processing/
  • Core Processing: src/core/embedding/sdk_embedding_helper.py
  • Object Detection: src/core/object_detection/detector.py
  • Video Utils: src/core/utils/video_utils.py