//! Trueno-DB Integration Tests (Issue #79)
//!
//! EXTREME TDD - RED Phase
//! These tests verify the P0 requirements from the Toyota Way review.
//!
//! References:
//! - Specification: docs/specifications/trueno-db-integration-v2.md
//! - Review: docs/specifications/trueno-db-integration-review-response.md

/// P0-1: Feature-Gated Architecture
///
/// Ensures wgpu is not included by default to prevent +3.8MB binary bloat.
/// Reference: Parnas (1972) - Information hiding via modular decomposition
#[cfg(test)]
mod p0_1_feature_gates {
    /// RED TEST: Verify default build excludes wgpu
    ///
    /// This test ensures the "analytics-simd" feature is default and
    /// that wgpu is NOT compiled in, preventing +3.8 MB binary bloat.
    #[test]
    fn test_feature_gate_simd_only() {
        // FAIL: This will fail until we implement feature gating
        #[cfg(not(feature = "analytics-gpu"))]
        {
            // If analytics-gpu is NOT enabled, wgpu should not be available
            // This is the default configuration
            assert!(true, "SIMD-only build (default): wgpu excluded");
        }

        #[cfg(feature = "analytics-gpu")]
        {
            panic!("FAIL: GPU backend should not be enabled by default");
        }
    }

    /// RED TEST: Verify GPU-enabled build includes wgpu
    ///
    /// When explicitly enabling analytics-gpu, wgpu should be available.
    #[test]
    #[cfg(feature = "analytics-gpu")]
    fn test_feature_gate_gpu_enabled() {
        // FAIL: This will fail until we add analytics-gpu feature
        // When built with --features analytics-gpu, this test should pass
        assert!(true, "GPU-enabled build: wgpu included");
    }

    /// RED TEST: Dependency count regression
    ///
    /// Ensures we don't accidentally add more transitive dependencies.
    /// Target: SIMD-only ≤ 30 deps, GPU-enabled ≤ 95 deps
    #[test]
    fn test_dependency_count_regression() {
        // FAIL: This will fail until we implement proper dep counting

        // This test will be implemented using cargo-tree analysis
        // For now, it's a placeholder that will fail

        #[cfg(not(feature = "analytics-gpu"))]
        {
            // SIMD-only: Target ≤30 transitive deps
            let max_deps = 30;

            // TODO: Implement actual dep counting
            // For now, assume failure
            let actual_deps = count_transitive_dependencies();

            assert!(
                actual_deps <= max_deps,
                "SIMD-only build has {} deps, expected ≤{}",
                actual_deps,
                max_deps
            );
        }

        #[cfg(feature = "analytics-gpu")]
        {
            // GPU-enabled: Target ≤95 transitive deps
            let max_deps = 95;

            let actual_deps = count_transitive_dependencies();

            assert!(
                actual_deps <= max_deps,
                "GPU-enabled build has {} deps, expected ≤{}",
                actual_deps,
                max_deps
            );
        }
    }

    /// Helper: Count transitive dependencies
    ///
    /// RED: This function doesn't exist yet, will cause compile error
    fn count_transitive_dependencies() -> usize {
        // FAIL: Not implemented
        // Will use cargo tree parsing in GREEN phase
        0 // Placeholder
    }
}

/// P0-2: Top-K Selection Algorithm
///
/// Implements O(N) selection vs O(N log N) sort (28.75x speedup).
/// Reference: Shanbhag et al. (2018) SIGMOD, Blum et al. (1973)
#[cfg(test)]
#[cfg(feature = "analytics-simd")]
mod p0_2_top_k_selection {
    use pmat::services::analytics_top_k::TopKSelector;

    /// GREEN TEST: Top-K returns exactly K largest elements
    #[test]
    fn test_top_k_correctness() {
        let data = vec![5, 2, 8, 1, 9, 3, 7, 4, 6];
        let k = 3;

        let selector = TopKSelector::new(k);
        let result = selector.select(&data);

        assert_eq!(result.len(), k);
        assert_eq!(result, vec![9, 8, 7]); // Top 3 in descending order
    }

    /// GREEN TEST: Top-K is faster than sort for large datasets
    ///
    /// Note: This test should be run in release mode for accurate performance measurement
    /// Debug mode lacks optimizations and will show misleading results
    #[test]
    #[ignore] // Performance test - run with `cargo test --release -- --ignored`
    fn test_top_k_performance() {
        let data: Vec<u32> = (0..1_000_000).collect();
        let k = 10;

        // Measure sort + truncate
        let start = std::time::Instant::now();
        let mut sorted = data.clone();
        sorted.sort_unstable();
        sorted.reverse(); // Descending order
        sorted.truncate(k);
        let sort_time = start.elapsed();

        // Measure Top-K selection
        let start = std::time::Instant::now();
        let selector = TopKSelector::new(k);
        let _result = selector.select(&data);
        let topk_time = start.elapsed();

        // Target: Top-K should be ≥2x faster than sort (conservative target)
        // In release mode with optimizations, this should pass easily
        // Note: Worst-case data (ascending sequence) is O(N log K) for Top-K
        assert!(
            topk_time < sort_time / 2,
            "Top-K ({:?}) should be ≥2x faster than sort ({:?})",
            topk_time,
            sort_time
        );
    }
}

/// P0-3: Statistical Floating-Point Equivalence
///
/// Prevents flaky CI tests due to GPU non-associativity.
/// Reference: Higham (1993) SIAM, Whitehead & Fit-Florea (2011) NVIDIA
#[cfg(test)]
#[cfg(feature = "analytics-simd")]
mod p0_3_statistical_equivalence {
    use pmat::services::analytics_backend::{
        stats::{compute_avg, generate_test_dataset, mean_and_std},
        Backend,
    };

    /// GREEN TEST: SIMD statistical properties
    ///
    /// Tests SIMD backend statistical properties. GPU testing requires hardware.
    #[test]
    fn test_simd_statistical_properties() {
        const RUNS: usize = 10; // Reduced for unit test

        let dataset = generate_test_dataset(10_000);

        let mut simd_results = Vec::with_capacity(RUNS);

        for _ in 0..RUNS {
            simd_results.push(compute_avg(&dataset, Backend::Simd).unwrap());
        }

        let (mean, std) = mean_and_std(&simd_results);

        // SIMD should be deterministic (std ~0)
        assert!(
            std < 1e-10,
            "SIMD results should be deterministic, got std={}",
            std
        );

        // Mean should be consistent
        assert!(mean.is_finite(), "Mean should be finite");
    }

    /// GREEN TEST: Scalar vs SIMD equivalence
    ///
    /// Validates that SIMD and Scalar backends produce equivalent results
    #[test]
    fn test_scalar_simd_equivalence() {
        const RUNS: usize = 10;

        let dataset = generate_test_dataset(10_000);

        let mut scalar_results = Vec::with_capacity(RUNS);
        let mut simd_results = Vec::with_capacity(RUNS);

        for _ in 0..RUNS {
            scalar_results.push(compute_avg(&dataset, Backend::Scalar).unwrap());
            simd_results.push(compute_avg(&dataset, Backend::Simd).unwrap());
        }

        let (scalar_mean, _scalar_std) = mean_and_std(&scalar_results);
        let (simd_mean, _simd_std) = mean_and_std(&simd_results);

        let diff = (scalar_mean - simd_mean).abs();

        // Both should be deterministic, so diff should be ~0
        assert!(
            diff < 1e-10,
            "Scalar and SIMD should produce identical results: scalar={}, simd={}, diff={}",
            scalar_mean,
            simd_mean,
            diff
        );
    }

    /// IGNORED TEST: GPU and SIMD means within 6-sigma
    ///
    /// This test requires GPU hardware and is ignored by default.
    /// Run with: cargo test --features analytics-gpu -- --ignored
    #[test]
    #[ignore] // Requires GPU hardware
    #[cfg(feature = "analytics-gpu")]
    fn test_gpu_simd_statistical_equivalence() {
        const RUNS: usize = 100;
        const SIGMA_THRESHOLD: f64 = 6.0;

        let dataset = generate_test_dataset(100_000);

        let mut gpu_results = Vec::with_capacity(RUNS);
        let mut simd_results = Vec::with_capacity(RUNS);

        for _ in 0..RUNS {
            gpu_results.push(compute_avg(&dataset, Backend::Gpu).unwrap());
            simd_results.push(compute_avg(&dataset, Backend::Simd).unwrap());
        }

        let (gpu_mean, gpu_std) = mean_and_std(&gpu_results);
        let (simd_mean, simd_std) = mean_and_std(&simd_results);

        let diff = (gpu_mean - simd_mean).abs();
        let combined_sigma = (gpu_std.powi(2) + simd_std.powi(2)).sqrt();

        // Special case: If both backends are deterministic (σ ≈ 0), they should be identical
        // This occurs when GPU backend falls back to CPU (no GPU compute shader implemented yet)
        if combined_sigma < 1e-10 {
            assert!(
                diff < 1e-10,
                "Deterministic backends should produce identical results: GPU mean={}, SIMD mean={}, diff={}",
                gpu_mean,
                simd_mean,
                diff
            );
        } else {
            // Statistical equivalence test (6-sigma threshold)
            assert!(
                diff < SIGMA_THRESHOLD * combined_sigma,
                "GPU mean={}, SIMD mean={}, diff={}, 6σ={}",
                gpu_mean,
                simd_mean,
                diff,
                SIGMA_THRESHOLD * combined_sigma
            );
        }
    }
}

/// P0-4: OLAP Write Pattern Validation
///
/// Ensures PMAT only uses append-only batches (no incremental updates).
/// Reference: Stonebraker et al. (2005) VLDB, Abadi et al. (2013)
#[cfg(test)]
mod p0_4_olap_validation {
    use std::path::Path;

    /// RED TEST: No code calls deprecated single-row updates
    #[test]
    fn test_no_deprecated_update_calls() {
        let storage_files = find_tdg_storage_files();

        for file in storage_files {
            let content = std::fs::read_to_string(&file).expect("Failed to read file");

            // FAIL: Will fail if any code calls deprecated update_single()
            assert!(
                !content.contains("update_single("),
                "File {} must not call deprecated update_single(). Use append_batch() instead.",
                file.display()
            );

            // Also check for direct SQL UPDATE statements
            assert!(
                !content.contains("UPDATE tdg_scores"),
                "File {} must not use UPDATE statements. OLAP storage is append-only.",
                file.display()
            );
        }
    }

    /// RED TEST: All TDG storage uses append-only pattern
    #[test]
    fn test_append_only_pattern() {
        let storage_files = find_tdg_storage_files();

        for file in storage_files {
            let content = std::fs::read_to_string(&file).expect("Failed to read file");

            // Verify append_batch or similar batch operations exist
            let has_batch_operation = content.contains("append_batch")
                || content.contains("store_tdg_batch")
                || content.contains("INSERT INTO"); // Batch inserts OK

            assert!(
                has_batch_operation,
                "File {} must use batch append operations (OLAP pattern)",
                file.display()
            );
        }
    }

    /// Helper: Find all TDG storage-related files
    fn find_tdg_storage_files() -> Vec<std::path::PathBuf> {
        let base_path = Path::new("server/src/services");
        let mut files = Vec::new();

        // FAIL: This is a simplified version, will enhance in GREEN
        if base_path.join("tdg_storage.rs").exists() {
            files.push(base_path.join("tdg_storage.rs"));
        }

        files
    }
}

/// P0-5: Runtime PCIe Bandwidth Calibration
///
/// Measures actual bandwidth instead of assuming 32 GB/s.
/// Reference: Gregg & Hazelwood (2011) ISPASS, Chaudhuri et al. (2004) VLDB
#[cfg(test)]
mod p0_5_pcie_calibration {
    use std::time::Duration;

    /// RED TEST: Calibration measures bandwidth within reasonable range
    #[test]
    #[ignore] // Requires GPU hardware
    fn test_pcie_calibration_accuracy() {
        // FAIL: GpuDevice doesn't exist yet
        let device = create_test_gpu_device();
        let bandwidth = calibrate_pcie_bandwidth(&device);

        // Bandwidth should be between 2.5 GB/s (Thunderbolt eGPU)
        // and 32 GB/s (PCIe Gen4 x16)
        assert!(
            bandwidth >= 2.0 && bandwidth <= 35.0,
            "Calibrated bandwidth {} GB/s out of realistic range [2, 35]",
            bandwidth
        );

        // Should be at least 50% of theoretical max
        // (accounting for driver overhead)
        let theoretical_max = 32.0; // Assume Gen4 x16
        assert!(
            bandwidth > theoretical_max * 0.5,
            "Bandwidth {} GB/s is <50% of theoretical {} GB/s",
            bandwidth,
            theoretical_max
        );
    }

    /// RED TEST: Calibration completes in reasonable time
    #[test]
    #[ignore] // Requires GPU hardware
    fn test_pcie_calibration_performance() {
        let device = create_test_gpu_device();

        let start = std::time::Instant::now();
        let _bandwidth = calibrate_pcie_bandwidth(&device);
        let elapsed = start.elapsed();

        // Calibration should complete in <100ms
        assert!(
            elapsed < Duration::from_millis(100),
            "Calibration took {:?}, expected <100ms",
            elapsed
        );
    }

    /// RED: GpuDevice struct doesn't exist yet
    struct GpuDevice;

    fn create_test_gpu_device() -> GpuDevice {
        panic!("create_test_gpu_device not implemented (RED phase)");
    }

    fn calibrate_pcie_bandwidth(_device: &GpuDevice) -> f64 {
        panic!("calibrate_pcie_bandwidth not implemented (RED phase)");
    }
}

/// RED Phase Summary Test
///
/// This test documents all RED tests that must fail before GREEN implementation.
#[test]
fn test_red_phase_summary() {
    println!("RED Phase Tests (All should FAIL):");
    println!("  P0-1: Feature-gated architecture");
    println!("    - test_feature_gate_simd_only");
    println!("    - test_feature_gate_gpu_enabled");
    println!("    - test_dependency_count_regression");
    println!("");
    println!("  P0-2: Top-K selection algorithm");
    println!("    - test_top_k_correctness");
    println!("    - test_top_k_performance");
    println!("");
    println!("  P0-3: Statistical equivalence");
    println!("    - test_statistical_equivalence");
    println!("");
    println!("  P0-4: OLAP validation");
    println!("    - test_no_deprecated_update_calls");
    println!("    - test_append_only_pattern");
    println!("");
    println!("  P0-5: PCIe calibration");
    println!("    - test_pcie_calibration_accuracy");
    println!("    - test_pcie_calibration_performance");
    println!("");
    println!("Total: 11 RED tests");
    println!("Status: ❌ All should FAIL (RED phase)");
}
