README_INGESTION

ThemisDB Ingestion Optimization - Complete Documentation

Version: 1.0
Date: December 25, 2025
Status: Complete ✅

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📚 Documentation Overview

This folder contains comprehensive documentation on optimizing data ingestion into ThemisDB. The documentation is organized into multiple levels for different audiences:

1. Executive Summary (5-minute read)

📄 INGESTION_OPTIMIZATION_SUMMARY.md

Target Audience: CTOs, Engineering Managers, Team Leads

Contents:

• Top 5 optimization ideas with quick impact analysis
• Performance comparison tables
• Recommended action plan (Phase 1-3)
• Configuration templates
• Key metrics to track

Key Takeaways:

• Quick wins: +150-250% throughput in 1-2 weeks
• Medium-term: +200-500% for specific workloads
• Practical configuration templates provided

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2. Architecture & Optimization Layers (15-minute read)

📄 INGESTION_ARCHITECTURE.md

Target Audience: Solutions Architects, Senior Engineers

Contents:

• Complete ingestion stack visualization
• Layer-by-layer optimization opportunities
• Data flow examples (before/after)
• Priority matrix (impact vs. effort)
• Implementation checklist

Key Takeaways:

• 4-layer architecture: Client → Network → Server → Storage
• Visual diagrams for each layer
• Real-world data flow examples
• Clear implementation roadmap

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3. Detailed Technical Guide (60-minute read)

📄 INGESTION_OPTIMIZATION_IDEAS.md

Target Audience: Engineers, Database Administrators

Contents:

• 7 major optimization categories
• 40+ specific optimization techniques
• Code examples and configurations
• Performance benchmarks and impact analysis
• Trade-offs and risk assessment

Sections:

1. RocksDB Write Path Optimizations (40+ pages)

   • Adaptive write buffer sizing
   • Parallel memtable writes
   • Level0 compaction tuning
   • WAL optimization (async, group commit)

2. HTTP/gRPC Protocol Optimization (15 pages)

   • Binary vs. JSON comparison
   • HTTP/2 multiplexing
   • Payload compression (Zstd, Gzip, LZ4)

3. Batch & Buffer Strategies (20 pages)

   • Adaptive batch sizing
   • Multi-level buffering
   • Priority-based queues

4. Compression & Serialization (15 pages)

   • Product Quantization for embeddings (-90-97% storage!)
   • Time Series Gorilla compression
   • JSON payload pre-compression

5. Memory-Mapped I/O & Zero-Copy (10 pages)

   • Memory-mapped file import
   • Zero-copy network transfers
   • Direct I/O for bulk writes

6. Client-Side Optimizations (8 pages)

   • Connection pooling
   • Request pipelining
   • Client-side batching

7. Summary & Prioritization (5 pages)

   • Quick wins vs. long-term
   • Configuration recommendations
   • Action plan

Key Takeaways:

• Comprehensive technical details
• Production-ready code examples
• Real benchmark data
• Risk and trade-off analysis

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🎯 Quick Start Guide

For Managers/Decision Makers

1. Read Executive Summary (5 min)
2. Review recommended action plan
3. Approve Phase 1 implementation (1-2 weeks)

For Architects

1. Read Executive Summary (5 min)
2. Review Architecture Document (15 min)
3. Plan implementation strategy

For Engineers

1. Skim Executive Summary (5 min)
2. Study Detailed Guide (60 min)
3. Test optimizations in development
4. Use configuration template

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📈 Expected Impact

Phase 1: Quick Wins (Week 1-2)

Metric	Before	After	Improvement
Write Throughput	100k ops/s	250k ops/s	+150%
P99 Latency	50ms	15ms	-70%
Network Traffic	100%	30%	-70%
Storage (1M embeddings)	3 GB	3 GB	No change yet

Effort: 10 days
Cost: Near zero (configuration changes)
Risk: Very low (well-tested optimizations)

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Phase 2: Medium-term (Month 1-2)

Metric	Before	After	Improvement
Write Throughput	250k ops/s	500k ops/s	+100%
P99 Latency	15ms	5ms	-67%
Storage (1M embeddings)	3 GB	0.3 GB	-90%
Bulk Import (10GB)	30 min	5 min	+500%

Effort: 2 months
Cost: Medium (development time)
Risk: Low-Medium (requires testing)

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Phase 3: Long-term (Month 3-6)

Metric	Before	After	Improvement
Write Throughput (@64 threads)	500k ops/s	1.5M ops/s	+200%
P99 Latency	5ms	2ms	-60%

Effort: 6 months
Cost: High (significant development)
Risk: Medium (durability trade-offs)

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🛠️ Implementation Resources

Configuration Files

• 📄 ingestion-optimized.yaml - Production-ready configuration
• Use as template for your environment
• Includes comments explaining each setting

Code Examples

All optimization techniques include working code examples:

• C++ (RocksDB optimizations)
• Python (client-side optimizations)
• Configuration (YAML)

Benchmarking Scripts

# Write throughput test
./bench_write --config=ingestion-optimized.yaml

# Latency test
./bench_latency --percentiles=50,95,99

# Bulk import test
./bench_bulk_import --file=testdata.json --size=10GB

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⚠️ Important Considerations

Durability Trade-offs

Some optimizations reduce durability guarantees:

Optimization	Durability Impact	Recommended For
Async WAL	⚠️ ~100µs data loss risk	Read replicas, dev
Group Commit	⚠️ ~100µs data loss risk	High-throughput
Disable WAL	❌ Full data loss risk	Bulk import only

General Rules:

• Production Primary: Keep full durability (sync=true, enable_wal=true)
• Read Replicas: Can use async WAL for performance
• Bulk Import: Disable durability during import, re-enable after
• Development: Optimize for performance

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Memory Requirements

Configuration	Min RAM	Recommended RAM	Notes
Standard	8 GB	16 GB	Default settings
High-Throughput	32 GB	64 GB	4× larger buffers
Bulk-Import	64 GB	128 GB	8× larger buffers

Formula:

Required RAM = 
  (write_buffer_size × max_write_buffer_number) +
  block_cache_size +
  2 GB (OS/Application)

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CPU Requirements

Configuration	Min Cores	Recommended	Notes
Standard	4	8	Basic workload
High-Throughput	8	16	Heavy compaction
Bulk-Import	16	32+	Parallel compression

Note: More cores = more parallelism = higher throughput

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📊 Monitoring & Validation

Key Metrics to Track

Create a Grafana dashboard with these metrics:

1. Write Performance

   • Write throughput (ops/sec)
   • Write latency (P50, P95, P99)
   • Batch size distribution

2. Resource Usage

   • Memory (total, memtables, block cache)
   • CPU (total, compaction, compression)
   • Disk I/O (read/write MB/s)

3. RocksDB Health

   • Level0 file count (should stay low)
   • Write stalls (should be zero)
   • Compaction pending bytes

4. Network

   • Request rate
   • Payload size (compressed vs uncompressed)
   • Connection count

Alert Thresholds

alerts:
  - name: High Level0 Files
    threshold: level0_files > 10
    action: Increase compaction threads
    
  - name: Write Stalls
    threshold: write_stalls > 0
    action: Critical - tune Level0 config
    
  - name: High P99 Latency
    threshold: p99_latency > 100ms
    action: Investigate bottleneck
    
  - name: Memory Pressure
    threshold: memory_usage > 90%
    action: Reduce buffer sizes

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🔍 Troubleshooting

Problem: Write Stalls

Symptoms:

• P99 latency spikes to seconds
• rocksdb.stall.micros metric increases
• Level0 file count keeps growing

Solutions:

1. Increase max_background_compactions to 8-12
2. Lower level0_file_num_compaction_trigger to 2
3. Lower level0_stop_writes_trigger to 16
4. Add more CPU cores for compaction

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Problem: Out of Memory

Symptoms:

• System memory usage at 100%
• OOM killer terminates process
• Swap usage increases

Solutions:

1. Reduce write_buffer_size (e.g., 1024MB → 512MB)
2. Reduce max_write_buffer_number (e.g., 6 → 4)
3. Reduce block_cache_size
4. Enable db_write_buffer_size limit
5. Add more RAM

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Problem: Low Throughput

Symptoms:

• Write throughput < 50k ops/s
• CPU usage < 50%
• Disk I/O not saturated

Solutions:

1. Enable HTTP/2
2. Increase client batch size
3. Use binary protocol instead of JSON
4. Enable payload compression
5. Increase parallelism (more client threads)

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📚 Additional Resources

Internal Documentation

• BATCH_PROCESSING_OPPORTUNITIES.md - Detailed batch processing analysis
• PERFORMANCE_INDEX.md - Complete performance docs index
• THEMISDB_IMPACT_ANALYSE_OPTIMIERUNGEN.md - Full impact analysis

External Resources

• RocksDB Tuning Guide
• RocksDB Performance Benchmarks
• HTTP/2 Best Practices
• Product Quantization Paper
• Gorilla Time Series Compression
• Zstandard Compression

Video Tutorials (Future)

• Ingestion Optimization Walkthrough
• Configuration Best Practices
• Benchmarking Guide
• Troubleshooting Common Issues

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✅ Next Steps

1. Start with Phase 1 (Quick Wins)

   • Read Executive Summary
   • Apply configuration template
   • Run benchmarks to validate improvements

2. Plan Phase 2 (Medium-term)

   • Review Detailed Guide
   • Identify specific workloads to optimize
   • Allocate development resources

3. Monitor and Iterate

   • Set up Grafana dashboards
   • Track key metrics
   • Fine-tune based on real workload

4. Share Feedback

   • Report performance improvements
   • Suggest additional optimizations
   • Contribute benchmarks and use cases

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🤝 Contributing

Found an optimization not covered here? Have benchmark results to share?

1. Open an issue on GitHub
2. Submit a pull request with your findings
3. Share your success story

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📝 Change Log

Version 1.0 (December 25, 2025)

• Initial release
• 3 comprehensive documents
• 40+ optimization techniques
• Production-ready configuration template
• Complete architecture documentation

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📧 Contact

Questions? Contact the ThemisDB Performance Team

Status: Documentation Complete ✅
Ready for: Implementation Phase 1 🚀

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Happy Optimizing! 💡⚡🚀