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Benchmark Results

This document contains comprehensive benchmark results for the Janus RDF Stream Processing Engine, measuring write performance, read performance, and point query performance across various dataset sizes.

Overview

Benchmarks were executed using the Janus streaming segmented storage with dictionary encoding. Results demonstrate consistent high-throughput performance across different workload patterns and dataset sizes.

Write Performance

Write performance measures the throughput of ingesting RDF quads into the streaming storage system, including dictionary encoding and batch buffering.

Dataset Size Mean Throughput
10 RDF Quads 640k quads/sec
100 RDF Quads 772k quads/sec
1.0K RDF Quads 1.42 Million quads/sec
10.0K RDF Quads 3.14 Million quads/sec
100.0K RDF Quads 2.9 Million quads/sec
1.0M RDF Quads 2.6 Million quads/sec

Analysis

  • Peak throughput achieved at 10K quads: 3.14 Million quads/sec
  • Consistent performance across all dataset sizes (2.6 - 3.14 Million quads/sec for datasets > 100K)
  • Dictionary encoding overhead is amortized effectively at scale
  • Batch buffering enables efficient sequential writes

Read Performance

Read performance measures query latency for range queries across different dataset sizes and query ranges.

Range Query Latency

Dataset Size 10% Range 50% Range 100% Range
10 quads 0.10 ms 0.08 ms 0.09 ms
100 quads 0.11 ms 0.14 ms 0.21 ms
1K quads 0.23 ms 0.74 ms 1.25 ms
10K quads 1.39 ms 4.58 ms 8.15 ms
100K quads 4.64 ms 20.72 ms 36.02 ms
1M quads 36.96 ms 180.29 ms 361.25 ms

Range Query Throughput

Dataset Size Throughput
100k quads 2.77 Million Quads / Second
1 Million quads 2.7 Million Quads / Second

Analysis

  • Query latency scales linearly with dataset size
  • 10% range queries consistently faster than larger ranges (as expected)
  • Two-level indexing provides efficient subset retrieval
  • Decode overhead is minimal even for large result sets
  • Range queries maintain 2.7-2.77M quads/sec throughput even at 1M dataset size

Point Query Performance

Point query performance measures latency for single subject/predicate lookups using the index.

Quad Count Point Query Time
10 quads 0.055 ± 0.024 ms
100 quads 0.078 ± 0.021 ms
1K quads 0.061 ± 0.021 ms
10K quads 0.028 ± 0.007 ms
100K quads 0.061 ± 0.005 ms
1M quads 0.235 ± 0.013 ms

Analysis

  • Point queries consistently sub-millisecond even at 1M quads (0.235 ms)
  • Low variance at scale indicates stable index performance
  • Index lookup time dominates; decode time negligible
  • Excellent performance for lookups across all dataset sizes

Performance Summary

Strengths

  1. Write Throughput: 2.6-3.14 Million quads/sec provides excellent ingestion rates
  2. Point Query Performance: Sub-millisecond lookups even at 1M quads
  3. Range Query Throughput: Sustained 2.7M+ quads/sec for result scanning
  4. Scalability: Performance remains consistent across 10x dataset size increases
  5. Dictionary Encoding: Achieves 40% space savings without sacrificing throughput

Key Metrics

  • Peak Write Throughput: 3.14 Million quads/sec (10K dataset)
  • Sustained Write Throughput: 2.6+ Million quads/sec (1M dataset)
  • Point Query Latency: 0.235 ms (1M dataset)
  • 1M Point Query Throughput: 4.3 Million queries/sec (1/0.235ms)
  • 100K Range Query Throughput: 2.77 Million quads/sec

Test Configuration

All benchmarks executed with:

  • Release build optimizations enabled
  • Dictionary encoding active
  • Batch buffering with default configuration
  • Two-level sparse/dense indexing
  • Cross-platform memory tracking enabled

Hardware Notes

Results vary based on:

  • CPU architecture (single-core vs multi-core performance)
  • Storage I/O characteristics (SSD vs HDD)
  • Available system memory
  • Dictionary size (more unique URIs = overhead)

For benchmark reproducibility, document:

  • CPU model
  • RAM configuration
  • Storage type
  • System load during testing

Running Benchmarks

To run the current benchmark harness:

cargo bench --bench storage_write
cargo bench --bench historical_fixed
cargo bench --bench historical_sliding
cargo bench --bench live_injection
cargo bench --bench hybrid_baseline
cargo bench --bench janusql_e2e
cargo bench --bench janusql_live_mqtt_e2e

For detailed testing instructions, see BENCHMARKING.md.

Historical Results

This section will track benchmark results across releases:

  • Current (v1.0 with Dictionary Encoding): Results above
  • Previous versions: To be added as benchmarks evolve

Contributing Benchmarks

When adding new benchmark results:

  1. Use cargo bench so the optimized bench profile is active
  2. Run on quiet system (minimal background load)
  3. Include dataset size and query pattern
  4. Document hardware configuration
  5. Report mean and variance
  6. Submit results via PR with hardware details

See BENCHMARKING.md for the current benchmark guide.