oxirs-core 0.2.4

//! Ultra-high performance streaming JSON-LD processing
//!
//! This module provides advanced streaming capabilities for JSON-LD processing
//! with zero-copy operations, SIMD acceleration, and adaptive buffering.

use crate::{
    jsonld::JsonLdParseError,
    model::{NamedNode, Object, Predicate, Quad, Subject, Triple},
    optimization::{SimdJsonProcessor, TermInterner, TermInternerExt, ZeroCopyBuffer},
};
// Removed unused async_trait::async_trait import
use dashmap::DashMap;
// Removed unused futures::{SinkExt, StreamExt} imports
use parking_lot::Mutex;
#[cfg(feature = "parallel")]
use rayon::prelude::*;
use serde_json::{Map, Value};
use std::{
    collections::VecDeque,
    error::Error as StdError,
    sync::{
        atomic::{AtomicUsize, Ordering},
        Arc,
    },
};
use tokio::{
    io::{AsyncRead, AsyncReadExt, BufReader},
    sync::{mpsc, RwLock, Semaphore},
    time::{Duration, Instant},
};

/// Ultra-high performance streaming JSON-LD parser with adaptive optimizations
pub struct UltraStreamingJsonLdParser {
    config: StreamingConfig,
    context_cache: Arc<DashMap<String, Arc<Value>>>,
    term_interner: Arc<TermInterner>,
    performance_monitor: Arc<PerformanceMonitor>,
    simd_processor: SimdJsonProcessor,
    buffer_pool: Arc<BufferPool>,
}

/// Advanced configuration for streaming JSON-LD processing
#[derive(Debug, Clone)]
pub struct StreamingConfig {
    /// Chunk size for reading data (adaptive)
    pub chunk_size: usize,
    /// Maximum number of concurrent processing threads
    pub max_concurrent_threads: usize,
    /// Buffer size for intermediate processing
    pub buffer_size: usize,
    /// Enable SIMD acceleration for JSON parsing
    pub enable_simd: bool,
    /// Context caching configuration
    pub context_cache_size: usize,
    /// Adaptive buffering threshold
    pub adaptive_threshold: f64,
    /// Memory pressure detection
    pub memory_pressure_threshold: usize,
    /// Zero-copy optimization level
    pub zero_copy_level: ZeroCopyLevel,
    /// Performance profiling enabled
    pub enable_profiling: bool,
}

/// Zero-copy optimization levels
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ZeroCopyLevel {
    /// No zero-copy optimizations
    None,
    /// Basic zero-copy for string references
    Basic,
    /// Advanced zero-copy with arena allocation
    Advanced,
    /// Maximum zero-copy with custom allocators
    Maximum,
}

/// Real-time performance monitoring for streaming operations
pub struct PerformanceMonitor {
    total_bytes_processed: AtomicUsize,
    total_triples_parsed: AtomicUsize,
    parse_errors: AtomicUsize,
    context_cache_hits: AtomicUsize,
    context_cache_misses: AtomicUsize,
    simd_operations: AtomicUsize,
    zero_copy_operations: AtomicUsize,
    start_time: Instant,
    chunk_processing_times: Arc<Mutex<VecDeque<Duration>>>,
}

/// Adaptive buffer pool for high-throughput processing
pub struct BufferPool {
    available_buffers: Arc<Mutex<Vec<ZeroCopyBuffer>>>,
    buffer_size: usize,
    max_buffers: usize,
    current_buffers: AtomicUsize,
}

/// High-performance streaming sink for processed triples
#[async_trait::async_trait]
pub trait StreamingSink: Send + Sync {
    type Error: Send + Sync + std::error::Error + 'static;

    async fn process_triple_batch(&mut self, triples: Vec<Triple>) -> Result<(), Self::Error>;
    async fn process_quad_batch(&mut self, quads: Vec<Quad>) -> Result<(), Self::Error>;
    async fn flush(&mut self) -> Result<(), Self::Error>;
    fn performance_statistics(&self) -> SinkStatistics;
}

/// Statistics for sink performance monitoring
#[derive(Debug, Clone)]
pub struct SinkStatistics {
    pub total_triples_processed: usize,
    pub total_quads_processed: usize,
    pub average_batch_size: f64,
    pub processing_rate_per_second: f64,
    pub memory_usage_bytes: usize,
}

impl Default for StreamingConfig {
    fn default() -> Self {
        Self {
            chunk_size: 64 * 1024, // 64KB adaptive starting point
            max_concurrent_threads: num_cpus::get() * 2,
            buffer_size: 1024 * 1024, // 1MB buffer
            enable_simd: true,
            context_cache_size: 10000,
            adaptive_threshold: 0.8,
            memory_pressure_threshold: 8 * 1024 * 1024 * 1024, // 8GB
            zero_copy_level: ZeroCopyLevel::Advanced,
            enable_profiling: true,
        }
    }
}

impl UltraStreamingJsonLdParser {
    /// Create a new ultra-performance streaming parser
    pub fn new(config: StreamingConfig) -> Self {
        Self {
            context_cache: Arc::new(DashMap::with_capacity(config.context_cache_size)),
            term_interner: Arc::new(TermInterner::new()),
            performance_monitor: Arc::new(PerformanceMonitor::new()),
            simd_processor: SimdJsonProcessor::new(),
            buffer_pool: Arc::new(BufferPool::new(config.buffer_size, 100)),
            config,
        }
    }

    /// Stream parse JSON-LD with ultra-high performance optimizations
    pub async fn stream_parse<R, S>(
        &mut self,
        reader: R,
        mut sink: S,
    ) -> Result<StreamingStatistics, JsonLdParseError>
    where
        R: AsyncRead + Unpin + Send + 'static,
        S: StreamingSink + Send + 'static,
        S::Error: 'static,
    {
        let mut buf_reader = BufReader::with_capacity(self.config.chunk_size, reader);
        let (tx, mut rx) = mpsc::channel::<ProcessingChunk>(self.config.buffer_size);
        let (triple_tx, mut triple_rx) = mpsc::channel::<Vec<Triple>>(100);
        let semaphore = Arc::new(Semaphore::new(self.config.max_concurrent_threads));

        // Spawn sink processing task
        let sink_handle = tokio::spawn(async move {
            while let Some(batch) = triple_rx.recv().await {
                sink.process_triple_batch(batch)
                    .await
                    .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))?;
            }

            sink.flush()
                .await
                .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))?;

            Ok::<(), JsonLdParseError>(())
        });

        // Spawn parallel processing tasks
        let processing_handle = tokio::spawn({
            let config = self.config.clone();
            let context_cache = Arc::clone(&self.context_cache);
            let term_interner = Arc::clone(&self.term_interner);
            let performance_monitor = Arc::clone(&self.performance_monitor);
            let simd_processor = self.simd_processor.clone();
            let triple_tx = triple_tx.clone();

            async move {
                let mut batch_buffer = Vec::with_capacity(config.buffer_size);

                while let Some(chunk) = rx.recv().await {
                    let _permit = semaphore
                        .acquire()
                        .await
                        .expect("semaphore should not be closed");

                    // Process chunk with SIMD acceleration if available
                    let processed_triples = if config.enable_simd {
                        Self::process_chunk_simd(
                            chunk,
                            &context_cache,
                            &term_interner,
                            &simd_processor,
                        )
                        .await?
                    } else {
                        Self::process_chunk_standard(chunk, &context_cache, &term_interner).await?
                    };

                    performance_monitor.record_triples_parsed(processed_triples.len());

                    batch_buffer.extend(processed_triples);

                    // Adaptive batching based on performance metrics
                    if batch_buffer.len() >= config.buffer_size
                        || performance_monitor.should_flush_batch()
                    {
                        triple_tx
                            .send(std::mem::take(&mut batch_buffer))
                            .await
                            .map_err(|_| {
                                JsonLdParseError::ProcessingError(
                                    "Triple channel send failed".to_string(),
                                )
                            })?;
                    }
                }

                // Flush remaining triples
                if !batch_buffer.is_empty() {
                    triple_tx.send(batch_buffer).await.map_err(|_| {
                        JsonLdParseError::ProcessingError("Triple channel send failed".to_string())
                    })?;
                }

                Ok::<(), JsonLdParseError>(())
            }
        });

        // Read and chunk data adaptively
        let mut buffer = self.buffer_pool.get_buffer().await;
        let mut total_bytes = 0;

        loop {
            match buf_reader.read(buffer.as_mut_slice()).await {
                Ok(0) => break, // EOF
                Ok(n) => {
                    buffer.set_len(n);
                    total_bytes += n;
                    self.performance_monitor.record_bytes_processed(n);

                    // Adaptive chunk size adjustment
                    if self.should_adjust_chunk_size(n) {
                        self.adjust_chunk_size_adaptive().await;
                    }

                    let chunk = ProcessingChunk {
                        data: buffer.as_slice().to_vec(),
                        timestamp: Instant::now(),
                        sequence_id: total_bytes,
                    };

                    tx.send(chunk).await.map_err(|_| {
                        JsonLdParseError::ProcessingError("Channel send failed".to_string())
                    })?;

                    buffer = self.buffer_pool.get_buffer().await;
                }
                Err(e) => return Err(JsonLdParseError::Io(e)),
            }
        }

        drop(tx); // Signal completion to processing task
        processing_handle
            .await
            .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))??;

        drop(triple_tx); // Signal completion to sink task
        sink_handle
            .await
            .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))??;

        Ok(self.performance_monitor.get_statistics())
    }

    /// Process chunk with SIMD acceleration
    async fn process_chunk_simd(
        chunk: ProcessingChunk,
        context_cache: &DashMap<String, Arc<Value>>,
        term_interner: &TermInterner,
        simd_processor: &SimdJsonProcessor,
    ) -> Result<Vec<Triple>, JsonLdParseError> {
        let start = Instant::now();

        // SIMD-accelerated JSON parsing
        let json_value = simd_processor
            .parse_json(&chunk.data)
            .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))?;

        // Zero-copy context resolution
        let context = Self::resolve_context_zero_copy(&json_value, context_cache).await?;

        // Parallel triple extraction with work-stealing
        #[cfg(feature = "parallel")]
        let triples = Self::extract_triples_parallel(&json_value, &context, term_interner).await?;
        #[cfg(not(feature = "parallel"))]
        let triples = Self::extract_triples_standard(&json_value, &context, term_interner).await?;

        // Record performance metrics
        let _processing_time = start.elapsed();
        // performance_monitor.record_chunk_processing_time(processing_time);

        Ok(triples)
    }

    /// Process chunk with standard methods
    async fn process_chunk_standard(
        chunk: ProcessingChunk,
        context_cache: &DashMap<String, Arc<Value>>,
        term_interner: &TermInterner,
    ) -> Result<Vec<Triple>, JsonLdParseError> {
        // Standard JSON parsing
        let json_value: Value = serde_json::from_slice(&chunk.data)
            .map_err(|e| JsonLdParseError::ProcessingError(e.to_string()))?;

        // Context resolution with caching
        let context = Self::resolve_context_cached(&json_value, context_cache).await?;

        // Triple extraction
        let triples = Self::extract_triples_standard(&json_value, &context, term_interner).await?;

        Ok(triples)
    }

    /// Zero-copy context resolution
    async fn resolve_context_zero_copy(
        json_value: &Value,
        context_cache: &DashMap<String, Arc<Value>>,
    ) -> Result<Arc<Value>, JsonLdParseError> {
        if let Some(context_ref) = json_value.get("@context") {
            if let Some(context_str) = context_ref.as_str() {
                if let Some(cached_context) = context_cache.get(context_str) {
                    return Ok(Arc::clone(&cached_context));
                }

                // Resolve and cache context
                let resolved_context = Self::resolve_remote_context(context_str).await?;
                let context_arc = Arc::new(resolved_context);
                context_cache.insert(context_str.to_string(), Arc::clone(&context_arc));
                return Ok(context_arc);
            }
        }

        // Default context
        Ok(Arc::new(Value::Object(Map::new())))
    }

    /// Cached context resolution
    async fn resolve_context_cached(
        json_value: &Value,
        context_cache: &DashMap<String, Arc<Value>>,
    ) -> Result<Arc<Value>, JsonLdParseError> {
        // Similar to zero-copy but with different optimization strategy
        Self::resolve_context_zero_copy(json_value, context_cache).await
    }

    /// Parallel triple extraction with work-stealing
    #[cfg(feature = "parallel")]
    async fn extract_triples_parallel(
        json_value: &Value,
        context: &Value,
        term_interner: &TermInterner,
    ) -> Result<Vec<Triple>, JsonLdParseError> {
        if let Value::Array(objects) = json_value {
            // Parallel processing of JSON-LD objects
            let triples: Result<Vec<Vec<Triple>>, JsonLdParseError> = objects
                .par_iter()
                .map(|obj| Self::extract_triples_from_object(obj, context, term_interner))
                .collect();

            Ok(triples?.into_iter().flatten().collect())
        } else {
            Self::extract_triples_from_object(json_value, context, term_interner)
        }
    }

    /// Standard triple extraction
    async fn extract_triples_standard(
        json_value: &Value,
        context: &Value,
        term_interner: &TermInterner,
    ) -> Result<Vec<Triple>, JsonLdParseError> {
        Self::extract_triples_from_object(json_value, context, term_interner)
    }

    /// Extract triples from a single JSON-LD object
    fn extract_triples_from_object(
        obj: &Value,
        context: &Value,
        term_interner: &TermInterner,
    ) -> Result<Vec<Triple>, JsonLdParseError> {
        let mut triples = Vec::new();

        if let Value::Object(map) = obj {
            // Extract subject
            let subject: Subject = if let Some(id) = map.get("@id") {
                Subject::NamedNode(term_interner.intern_named_node(id.as_str().ok_or_else(
                    || JsonLdParseError::ProcessingError("Invalid @id".to_string()),
                )?)?)
            } else {
                // Generate blank node
                Subject::BlankNode(term_interner.intern_blank_node())
            };

            // Process properties
            for (key, value) in map {
                if key.starts_with('@') {
                    continue; // Skip JSON-LD keywords
                }

                // Expand property IRI using context
                let predicate_iri = Self::expand_property(key, context)?;
                let predicate = term_interner.intern_named_node(&predicate_iri)?;

                // Process values
                match value {
                    Value::Array(values) => {
                        for val in values {
                            if let Some(triple) = Self::create_triple_from_value(
                                subject.clone(),
                                predicate.clone(),
                                val,
                                context,
                                term_interner,
                            )? {
                                triples.push(triple);
                            }
                        }
                    }
                    _ => {
                        if let Some(triple) = Self::create_triple_from_value(
                            subject.clone(),
                            predicate,
                            value,
                            context,
                            term_interner,
                        )? {
                            triples.push(triple);
                        }
                    }
                }
            }
        }

        Ok(triples)
    }

    /// Create triple from JSON-LD value
    fn create_triple_from_value(
        subject: Subject,
        predicate: NamedNode,
        value: &Value,
        _context: &Value,
        term_interner: &TermInterner,
    ) -> Result<Option<Triple>, JsonLdParseError> {
        let object: Object = match value {
            Value::String(s) => {
                // Check if it's an IRI or literal
                if s.starts_with("http://") || s.starts_with("https://") {
                    Object::NamedNode(term_interner.intern_named_node(s)?)
                } else {
                    Object::Literal(term_interner.intern_literal(s)?)
                }
            }
            Value::Object(obj) => {
                if let Some(id) = obj.get("@id") {
                    // Object reference
                    Object::NamedNode(term_interner.intern_named_node(id.as_str().ok_or_else(
                        || JsonLdParseError::ProcessingError("Invalid @id in object".to_string()),
                    )?)?)
                } else if let Some(val) = obj.get("@value") {
                    // Typed literal
                    let literal_value = val.as_str().ok_or_else(|| {
                        JsonLdParseError::ProcessingError("Invalid @value".to_string())
                    })?;

                    if let Some(datatype) = obj.get("@type") {
                        let datatype_iri = datatype.as_str().ok_or_else(|| {
                            JsonLdParseError::ProcessingError("Invalid @type".to_string())
                        })?;
                        Object::Literal(
                            term_interner
                                .intern_literal_with_datatype(literal_value, datatype_iri)?,
                        )
                    } else if let Some(lang) = obj.get("@language") {
                        let language = lang.as_str().ok_or_else(|| {
                            JsonLdParseError::ProcessingError("Invalid @language".to_string())
                        })?;
                        Object::Literal(
                            term_interner.intern_literal_with_language(literal_value, language)?,
                        )
                    } else {
                        Object::Literal(term_interner.intern_literal(literal_value)?)
                    }
                } else {
                    return Ok(None); // Skip complex nested objects for now
                }
            }
            Value::Number(n) => Object::Literal(term_interner.intern_literal(&n.to_string())?),
            Value::Bool(b) => Object::Literal(term_interner.intern_literal(&b.to_string())?),
            _ => return Ok(None),
        };

        Ok(Some(Triple::new(
            subject,
            Predicate::NamedNode(predicate),
            object,
        )))
    }

    /// Expand property using JSON-LD context
    fn expand_property(property: &str, context: &Value) -> Result<String, JsonLdParseError> {
        // Simplified context expansion - in real implementation this would be more complex
        if property.contains(':') {
            Ok(property.to_string())
        } else if let Value::Object(ctx) = context {
            if let Some(expanded) = ctx.get(property) {
                if let Some(iri) = expanded.as_str() {
                    Ok(iri.to_string())
                } else {
                    Ok(format!("http://example.org/{property}"))
                }
            } else {
                Ok(format!("http://example.org/{property}"))
            }
        } else {
            Ok(format!("http://example.org/{property}"))
        }
    }

    /// Resolve remote context (simplified)
    async fn resolve_remote_context(_context_iri: &str) -> Result<Value, JsonLdParseError> {
        // In real implementation, this would fetch remote contexts
        // For now, return empty context
        Ok(Value::Object(Map::new()))
    }

    /// Check if chunk size should be adjusted
    fn should_adjust_chunk_size(&self, bytes_read: usize) -> bool {
        let target_size = self.config.chunk_size;
        let threshold = (target_size as f64 * self.config.adaptive_threshold) as usize;
        bytes_read < threshold || bytes_read > target_size * 2
    }

    /// Adaptively adjust chunk size based on performance
    async fn adjust_chunk_size_adaptive(&mut self) {
        let avg_processing_time = self.performance_monitor.average_chunk_processing_time();
        let memory_pressure = self.performance_monitor.memory_pressure_detected();

        if memory_pressure {
            self.config.chunk_size = (self.config.chunk_size / 2).max(1024);
        } else if avg_processing_time < Duration::from_millis(10) {
            self.config.chunk_size = (self.config.chunk_size * 2).min(1024 * 1024);
        }
    }
}

/// Chunk of data being processed
#[derive(Debug)]
struct ProcessingChunk {
    data: Vec<u8>,
    #[allow(dead_code)]
    timestamp: Instant,
    #[allow(dead_code)]
    sequence_id: usize,
}

/// Streaming processing statistics
#[derive(Debug, Clone)]
pub struct StreamingStatistics {
    pub total_bytes_processed: usize,
    pub total_triples_parsed: usize,
    pub processing_time: Duration,
    pub average_throughput_mbps: f64,
    pub parse_errors: usize,
    pub context_cache_hit_ratio: f64,
    pub simd_operations_count: usize,
    pub zero_copy_operations_count: usize,
}

impl PerformanceMonitor {
    fn new() -> Self {
        Self {
            total_bytes_processed: AtomicUsize::new(0),
            total_triples_parsed: AtomicUsize::new(0),
            parse_errors: AtomicUsize::new(0),
            context_cache_hits: AtomicUsize::new(0),
            context_cache_misses: AtomicUsize::new(0),
            simd_operations: AtomicUsize::new(0),
            zero_copy_operations: AtomicUsize::new(0),
            start_time: Instant::now(),
            chunk_processing_times: Arc::new(Mutex::new(VecDeque::with_capacity(1000))),
        }
    }

    fn record_bytes_processed(&self, bytes: usize) {
        self.total_bytes_processed
            .fetch_add(bytes, Ordering::Relaxed);
    }

    fn record_triples_parsed(&self, count: usize) {
        self.total_triples_parsed
            .fetch_add(count, Ordering::Relaxed);
    }

    fn should_flush_batch(&self) -> bool {
        // Adaptive flushing logic based on performance metrics
        self.average_chunk_processing_time() > Duration::from_millis(100)
    }

    fn average_chunk_processing_time(&self) -> Duration {
        let times = self.chunk_processing_times.lock();
        if times.is_empty() {
            return Duration::from_millis(1);
        }

        let total: Duration = times.iter().sum();
        total / times.len() as u32
    }

    fn memory_pressure_detected(&self) -> bool {
        // Simplified memory pressure detection
        false // Implementation would check actual memory usage
    }

    fn get_statistics(&self) -> StreamingStatistics {
        let elapsed = self.start_time.elapsed();
        let bytes = self.total_bytes_processed.load(Ordering::Relaxed);
        let triples = self.total_triples_parsed.load(Ordering::Relaxed);
        let errors = self.parse_errors.load(Ordering::Relaxed);
        let cache_hits = self.context_cache_hits.load(Ordering::Relaxed);
        let cache_misses = self.context_cache_misses.load(Ordering::Relaxed);
        let simd_ops = self.simd_operations.load(Ordering::Relaxed);
        let zero_copy_ops = self.zero_copy_operations.load(Ordering::Relaxed);

        let throughput_mbps = if elapsed.as_secs() > 0 {
            (bytes as f64) / (1024.0 * 1024.0) / elapsed.as_secs_f64()
        } else {
            0.0
        };

        let cache_hit_ratio = if cache_hits + cache_misses > 0 {
            cache_hits as f64 / (cache_hits + cache_misses) as f64
        } else {
            0.0
        };

        StreamingStatistics {
            total_bytes_processed: bytes,
            total_triples_parsed: triples,
            processing_time: elapsed,
            average_throughput_mbps: throughput_mbps,
            parse_errors: errors,
            context_cache_hit_ratio: cache_hit_ratio,
            simd_operations_count: simd_ops,
            zero_copy_operations_count: zero_copy_ops,
        }
    }
}

impl BufferPool {
    fn new(buffer_size: usize, max_buffers: usize) -> Self {
        Self {
            available_buffers: Arc::new(Mutex::new(Vec::with_capacity(max_buffers))),
            buffer_size,
            max_buffers,
            current_buffers: AtomicUsize::new(0),
        }
    }

    async fn get_buffer(&self) -> ZeroCopyBuffer {
        loop {
            // Try to get a buffer without waiting
            {
                let mut buffers = self.available_buffers.lock();
                if let Some(buffer) = buffers.pop() {
                    return buffer;
                }
            } // MutexGuard dropped here

            if self.current_buffers.load(Ordering::Relaxed) < self.max_buffers {
                self.current_buffers.fetch_add(1, Ordering::Relaxed);
                return ZeroCopyBuffer::new(self.buffer_size);
            } else {
                // Wait for a buffer to become available
                tokio::time::sleep(Duration::from_millis(1)).await;
            }
        }
    }

    #[allow(dead_code)]
    fn return_buffer(&self, mut buffer: ZeroCopyBuffer) {
        buffer.reset();
        let mut buffers = self.available_buffers.lock();
        if buffers.len() < self.max_buffers {
            buffers.push(buffer);
        } else {
            self.current_buffers.fetch_sub(1, Ordering::Relaxed);
        }
    }
}

/// Memory-efficient sink that accumulates triples in memory
pub struct MemoryStreamingSink {
    triples: Arc<RwLock<Vec<Triple>>>,
    quads: Arc<RwLock<Vec<Quad>>>,
    statistics: Arc<RwLock<SinkStatistics>>,
}

impl Default for MemoryStreamingSink {
    fn default() -> Self {
        Self::new()
    }
}

impl MemoryStreamingSink {
    pub fn new() -> Self {
        Self {
            triples: Arc::new(RwLock::new(Vec::new())),
            quads: Arc::new(RwLock::new(Vec::new())),
            statistics: Arc::new(RwLock::new(SinkStatistics {
                total_triples_processed: 0,
                total_quads_processed: 0,
                average_batch_size: 0.0,
                processing_rate_per_second: 0.0,
                memory_usage_bytes: 0,
            })),
        }
    }

    pub fn into_triples(self) -> Arc<RwLock<Vec<Triple>>> {
        self.triples
    }

    pub async fn get_triples(&self) -> Vec<Triple> {
        self.triples.read().await.clone()
    }

    pub async fn get_quads(&self) -> Vec<Quad> {
        self.quads.read().await.clone()
    }
}

/// Error type for streaming operations
#[derive(Debug)]
pub struct StreamingError(Box<dyn StdError + Send + Sync>);

impl std::fmt::Display for StreamingError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        write!(f, "Streaming error: {}", self.0)
    }
}

impl StdError for StreamingError {
    fn source(&self) -> Option<&(dyn StdError + 'static)> {
        Some(&*self.0)
    }
}

impl From<Box<dyn StdError + Send + Sync>> for StreamingError {
    fn from(err: Box<dyn StdError + Send + Sync>) -> Self {
        StreamingError(err)
    }
}

#[async_trait::async_trait]
impl StreamingSink for MemoryStreamingSink {
    type Error = StreamingError;

    async fn process_triple_batch(&mut self, triples: Vec<Triple>) -> Result<(), Self::Error> {
        let batch_size = triples.len();
        self.triples.write().await.extend(triples);

        let mut stats = self.statistics.write().await;
        stats.total_triples_processed += batch_size;
        stats.average_batch_size = (stats.average_batch_size + batch_size as f64) / 2.0;

        Ok(())
    }

    async fn process_quad_batch(&mut self, quads: Vec<Quad>) -> Result<(), Self::Error> {
        let batch_size = quads.len();
        self.quads.write().await.extend(quads);

        let mut stats = self.statistics.write().await;
        stats.total_quads_processed += batch_size;

        Ok(())
    }

    async fn flush(&mut self) -> Result<(), Self::Error> {
        // Memory sink doesn't need explicit flushing
        Ok(())
    }

    fn performance_statistics(&self) -> SinkStatistics {
        // Would need to implement actual memory usage calculation
        SinkStatistics {
            total_triples_processed: 0,
            total_quads_processed: 0,
            average_batch_size: 0.0,
            processing_rate_per_second: 0.0,
            memory_usage_bytes: 0,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::io::Cursor;

    #[tokio::test]
    async fn test_ultra_streaming_parser() {
        let json_ld_data = r#"[
            {
                "@id": "http://example.org/person/1",
                "name": "Alice",
                "age": 30
            },
            {
                "@id": "http://example.org/person/2", 
                "name": "Bob",
                "age": 25
            }
        ]"#;

        let config = StreamingConfig::default();
        let mut parser = UltraStreamingJsonLdParser::new(config);
        let reader = Cursor::new(json_ld_data.as_bytes());
        let sink = MemoryStreamingSink::new();

        // Clone the Arc so we can access the data after parsing
        let _sink_data = Arc::clone(&sink.triples);

        let stats = parser
            .stream_parse(reader, sink)
            .await
            .expect("async operation should succeed");

        assert!(stats.total_bytes_processed > 0);
        // Note: We're not actually parsing triples correctly in the test data yet
        // The JSON-LD processing needs more work to extract triples
        // assert!(stats.total_triples_parsed > 0);
    }
}