背景介绍

在区块链技术蓬勃发展的浪潮中，海量的链上数据如同潮水般涌来，对区块链日志进行高效分析成为行业的关键挑战。

传统的数据处理工具在面对区块链日志高并发、大容量、实时性强等特点时，常常显得力不从心，性能瓶颈日益凸显。

作为一名深耕数据处理领域的开发者，我在区块链日志分析的实践中不断探索与尝试，历经无数个日夜的代码调试与性能优化，逐步积累了丰富的经验。

在一次次解决实际问题的过程中，我发现许多技术思路具有通用性，于是经过反复打磨与提炼，最终将这些宝贵的性能优化经验沉淀为一个通用的流式引擎框架 —— Fluxus。

它以卓越的性能、灵活的功能和友好的使用体验，为实时数据处理领域带来全新解决方案。

在数据洪流席卷各行各业的今天，实时数据处理能力成为企业保持竞争力的关键。

近日，这款基于 Rust 语言开发的轻量级流处理引擎正式亮相，凭借我在区块链日志分析性能优化中积累的深厚技术底蕴，重新定义了实时数据处理的标准。

高性能引擎内核，重新定义实时处理速度

Fluxus 深度挖掘 Rust 语言在性能上的巨大优势，精心优化实时数据处理流程，能够高效应对大规模数据流。

在实际测试中，面对每秒数十万条数据的冲击，Fluxus 仍能保持极低的延迟和极高的吞吐量，确保数据处理的及时性和准确性，为金融交易监控、网络流量分析等对实时性要求极高的场景提供了坚实保障。

灵活多样的窗口操作，满足复杂业务需求

Fluxus 提供了丰富的窗口操作模式。

翻滚窗口（Tumbling Windows） 以固定大小且不重叠的窗口对数据进行切割处理，适用于对数据进行周期性统计分析，如每小时的销售数据汇总；

滑动窗口（Sliding Windows 则以固定大小但重叠的窗口形式，能够捕捉数据的动态变化趋势，在股票价格波动分析、温度传感器数据监测等场景中发挥重要作用；

会话窗口（Session Windows） 基于非活动间隙动态调整窗口大小，特别适合处理用户行为数据，如分析用户在电商平台上的购物会话时长和行为模式。

并行处理与丰富操作集，释放数据价值

借助对数据流并行处理的支持，Fluxus 充分发挥多核处理器的性能优势，大幅提升数据处理效率。

同时，它还提供了一套丰富的流操作，涵盖map、filter、aggregate等常见操作，用户能够轻松实现复杂的数据转换和分析逻辑。

无论是对文本流进行词频统计，还是对网络日志进行异常检测，Fluxus 都能快速精准地完成任务，助力企业从海量数据中提取有价值的信息。

类型安全 API，打造高效开发体验

Fluxus 的 API 采用类型安全设计，从根源上减少了运行时错误的发生概率，为开发者营造了更加稳定、高效的开发环境。

清晰的接口定义和良好的项目结构，使得开发者能够快速上手并对引擎进行扩展。

无论是经验丰富的开发老手，还是初入实时数据处理领域的新手，都能在 Fluxus 的帮助下，高效地完成项目开发。

模块化架构设计，保障系统稳定运行

Fluxus 的架构由多个核心模块协同工作。

fluxus-api定义了引擎的核心 API 和接口，为引擎各组件与用户应用之间搭建起稳定的沟通桥梁；

fluxus-core包含核心实现和数据结构，负责处理流处理的内部逻辑，如窗口计算和操作执行；

fluxus-runtime则提供运行时引擎和执行环境，合理分配资源并高效调度任务，确保整个系统的稳定运行和高效执行。

丰富示例场景，解锁多元应用可能

为了让开发者更直观地感受 Fluxus 流处理引擎的强大功能与便捷使用，提供了一系列示例应用，覆盖多种常见数据处理场景。

文本处理

在文本处理领域，Word Count 示例利用翻滚窗口，将文本流拆分成单词，并统计每个时间窗口内的词频，同时展现了 Fluxus 的并行处理能力。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::collections::HashMap;
use std::time::Duration;

pub type WordCount = HashMap<String, usize>;

#[tokio::main]
async fn main() -> Result<()> {
    // Sample input text
    let text = vec![
        "hello world",
        "hello stream processing",
        "world of streaming",
        "hello streaming world",
    ];

    // Create a source from the text collection
    let source = CollectionSource::new(text);
    let sink: CollectionSink<WordCount> = CollectionSink::new();

    // Build and execute the streaming pipeline
    DataStream::new(source)
        // Split text into words
        .map(|line| {
            line.split_whitespace()
                .map(|s| s.to_lowercase())
                .collect::<Vec<_>>()
        })
        // Parallelize the processing
        .parallel(2)
        // Create tumbling windows of 1 second
        .window(WindowConfig::tumbling(Duration::from_millis(1000)))
        // Count words in each window
        .aggregate(HashMap::new(), |mut counts, words| {
            for word in words {
                *counts.entry(word).or_insert(0) += 1;
            }
            counts
        })
        // Write results to sink
        .sink(sink.clone())
        .await?;

    // Print the results
    println!("\nWord count results:");
    for result in sink.get_data() {
        println!("\nWindow results:");
        let mut words: Vec<_> = result.iter().collect();
        words.sort_by(|a, b| b.1.cmp(a.1).then(a.0.cmp(b.0)));
        for (word, count) in words {
            println!("  {}: {}", word, count);
        }
    }

    Ok(())
}

运行cargo run --bin word-count，开发者能快速理解基本流处理逻辑与窗口操作的基础应用。

传感器数据处理

在物联网数据处理方面，Temperature Sensor Analysis 示例用于处理多个传感器的读数，通过滑动窗口持续监控数据，计算温度的最小值、最大值和平均值。这不仅体现了 Fluxus 对 IoT 数据的高效处理能力，也展示了其在时间序列数据处理上的优势。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::collections::HashMap;
use std::time::{Duration, SystemTime};

#[derive(Clone)]
pub struct SensorReading {
    sensor_id: String,
    temperature: f64,
    humidity: f64,
    timestamp: SystemTime,
}

#[derive(Clone)]
pub struct SensorStats {
    sensor_id: String,
    avg_temperature: f64,
    avg_humidity: f64,
    min_temperature: f64,
    max_temperature: f64,
    reading_count: usize,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample temperature readings
    let readings = generate_sample_readings();
    let source = CollectionSource::new(readings);
    let sink = CollectionSink::new();

    // Build and execute the streaming pipeline
    DataStream::new(source)
        // Group by sensor_id
        .map(|reading| {
            (
                reading.sensor_id.clone(),
                (reading.temperature, reading.humidity, reading.timestamp),
            )
        })
        // Create 10-second tumbling windows
        .window(WindowConfig::tumbling(Duration::from_millis(10000)))
        // Aggregate temperatures in each window
        .aggregate(
            HashMap::new(),
            |mut stats, (sensor_id, (temp, humidity, _))| {
                let entry = stats
                    .entry(sensor_id.clone())
                    .or_insert_with(|| SensorStats {
                        sensor_id: String::new(),
                        avg_temperature: 0.0,
                        avg_humidity: 0.0,
                        min_temperature: f64::MAX,
                        max_temperature: f64::MIN,
                        reading_count: 0,
                    });

                entry.sensor_id = sensor_id;
                entry.min_temperature = entry.min_temperature.min(temp);
                entry.max_temperature = entry.max_temperature.max(temp);
                entry.avg_temperature = (entry.avg_temperature * entry.reading_count as f64 + temp)
                    / (entry.reading_count + 1) as f64;
                entry.avg_humidity = (entry.avg_humidity * entry.reading_count as f64 + humidity)
                    / (entry.reading_count + 1) as f64;
                entry.reading_count += 1;

                stats
            },
        )
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nTemperature analysis results:");
    for window_stats in sink.get_data() {
        println!("\nWindow results:");
        for (_, stats) in window_stats {
            println!(
                "Sensor {}: {} readings, Avg: {:.1}°C, Min: {:.1}°C, Max: {:.1}°C, Avg Humidity: {:.1}%",
                stats.sensor_id,
                stats.reading_count,
                stats.avg_temperature,
                stats.min_temperature,
                stats.max_temperature,
                stats.avg_humidity,
            );
        }
    }

    Ok(())
}

// Helper function to generate sample data
fn generate_sample_readings() -> Vec<SensorReading> {
    let start_time = SystemTime::now();
    let mut readings = Vec::new();

    for i in 0..100 {
        let timestamp = start_time + Duration::from_secs(i as u64 / 10);

        // Sensor 1: Normal temperature variations
        readings.push(SensorReading {
            sensor_id: "sensor1".to_string(),
            temperature: 20.0 + (i as f64 / 10.0).sin() * 2.0,
            humidity: 50.0 + (i as f64 / 10.0).cos() * 5.0,
            timestamp,
        });

        // Sensor 2: Gradually increasing temperature
        readings.push(SensorReading {
            sensor_id: "sensor2".to_string(),
            temperature: 22.0 + i as f64 * 0.1,
            humidity: 55.0 + i as f64 * 0.2,
            timestamp,
        });

        // Sensor 3: Random fluctuations
        readings.push(SensorReading {
            sensor_id: "sensor3".to_string(),
            temperature: 25.0 + (i as f64 * 0.7).cos() * 3.0,
            humidity: 60.0 + (i as f64 * 0.5).sin() * 4.0,
            timestamp,
        });
    }

    readings
}

用户行为分析

对于用户行为分析，Click Stream Analysis 示例借助会话窗口，跟踪用户的导航模式，将事件分组为会话，并分析用户参与度指标，助力企业深入了解用户行为，优化产品设计和营销策略。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::collections::HashMap;
use std::time::{Duration, SystemTime};

#[derive(Clone)]
pub struct ClickEvent {
    user_id: String,
    page_id: String,
    event_type: String,
    timestamp: SystemTime,
}

#[derive(Clone)]
pub struct UserSession {
    user_id: String,
    page_views: Vec<String>,
    start_time: SystemTime,
    duration_secs: u64,
    total_events: usize,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample click events
    let events = generate_sample_clicks();
    let source = CollectionSource::new(events);
    let sink = CollectionSink::new();

    // Build and execute the streaming pipeline
    DataStream::new(source)
        // Filter only page view events
        .filter(|event| event.event_type == "page_view")
        // Group by user_id
        .map(|event| {
            (
                event.user_id.clone(),
                (event.page_id.clone(), event.timestamp),
            )
        })
        // Create session windows with 30-second timeout
        .window(WindowConfig::session(Duration::from_millis(30000)))
        // Aggregate user sessions
        .aggregate(
            HashMap::new(),
            |mut sessions, (user_id, (page_id, timestamp))| {
                let session = sessions
                    .entry(user_id.clone())
                    .or_insert_with(|| UserSession {
                        user_id,
                        page_views: Vec::new(),
                        start_time: timestamp,
                        duration_secs: 0,
                        total_events: 0,
                    });

                session.page_views.push(page_id);
                session.duration_secs = timestamp
                    .duration_since(session.start_time)
                    .unwrap_or(Duration::from_secs(0))
                    .as_secs();
                session.total_events += 1;

                sessions
            },
        )
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nClick stream analysis results:");
    for session_data in sink.get_data() {
        println!("\nSession window results:");
        for (_, session) in session_data {
            println!(
                "User {}: {} events over {}s, Pages: {}",
                session.user_id,
                session.total_events,
                session.duration_secs,
                session.page_views.join(" -> ")
            );
        }
    }

    Ok(())
}

// Helper function to generate sample data
fn generate_sample_clicks() -> Vec<ClickEvent> {
    let start_time = SystemTime::now();
    let mut events = Vec::new();
    let pages = ["home", "products", "cart", "checkout"];
    let users = ["user1", "user2", "user3"];

    for (user_idx, user_id) in users.iter().enumerate() {
        let user_start = start_time + Duration::from_secs(user_idx as u64 * 5);

        // Simulate a user session with page views and some other events
        for (i, &page) in pages.iter().enumerate() {
            // Add page view
            events.push(ClickEvent {
                user_id: user_id.to_string(),
                page_id: page.to_string(),
                event_type: "page_view".to_string(),
                timestamp: user_start + Duration::from_secs(i as u64 * 10),
            });

            // Add some click events
            events.push(ClickEvent {
                user_id: user_id.to_string(),
                page_id: page.to_string(),
                event_type: "click".to_string(),
                timestamp: user_start + Duration::from_secs(i as u64 * 10 + 2),
            });
        }
    }

    events
}

网络日志分析

Network Log Analysis 示例则展示了 Fluxus 的高级流处理特性。它对 HTTP 访问日志进行处理，计算请求统计信息，并使用带有自定义聚合的滑动窗口，满足复杂网络数据的分析需求。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::collections::HashMap;
use std::time::{Duration, SystemTime};

#[derive(Clone)]
#[allow(dead_code)]
pub struct LogEntry {
    ip: String,
    method: String,
    path: String,
    status: u16,
    bytes: u64,
    timestamp: SystemTime,
}

#[derive(Clone)]
pub struct PathStats {
    path: String,
    total_requests: usize,
    error_count: usize,
    total_bytes: u64,
    avg_response_size: f64,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample log entries
    let logs = generate_sample_logs();
    let source = CollectionSource::new(logs);
    let sink = CollectionSink::new();

    // Build and execute the streaming pipeline
    DataStream::new(source)
        // Group by path
        .map(|log| (log.path.clone(), log))
        // Create 60-second sliding windows with 10-second slide
        .window(WindowConfig::sliding(
            Duration::from_millis(60000),
            Duration::from_millis(10000),
        ))
        // Aggregate path statistics
        .aggregate(HashMap::new(), |mut stats, (path, log)| {
            let entry = stats.entry(path).or_insert_with(|| PathStats {
                path: String::new(),
                total_requests: 0,
                error_count: 0,
                total_bytes: 0,
                avg_response_size: 0.0,
            });

            entry.path = log.path;
            entry.total_requests += 1;
            if log.status >= 400 {
                entry.error_count += 1;
            }
            entry.total_bytes += log.bytes;
            entry.avg_response_size = entry.total_bytes as f64 / entry.total_requests as f64;

            stats
        })
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nNetwork log analysis results:");
    for window_stats in sink.get_data() {
        println!("\nWindow results:");
        for (_, stats) in window_stats {
            println!(
                "Path: {}\n  Requests: {}\n  Errors: {}\n  Avg Size: {:.2} bytes\n  Error Rate: {:.1}%",
                stats.path,
                stats.total_requests,
                stats.error_count,
                stats.avg_response_size,
                (stats.error_count as f64 / stats.total_requests as f64) * 100.0
            );
        }
    }

    Ok(())
}

// Helper function to generate sample data
fn generate_sample_logs() -> Vec<LogEntry> {
    let start_time = SystemTime::now();
    let mut logs = Vec::new();
    let paths = ["/api/users", "/api/products", "/api/orders", "/health"];
    let methods = ["GET", "POST", "PUT", "DELETE"];

    for i in 0..200 {
        let timestamp = start_time + Duration::from_secs(i as u64 / 4);
        let path = paths[i % paths.len()];
        let method = methods[i % methods.len()];

        // Generate a mix of successful and error responses
        let status = if i % 10 == 0 {
            500 // Occasional server errors
        } else if i % 7 == 0 {
            404 // Some not found errors
        } else {
            200 // Mostly successful
        };

        // Simulate variable response sizes
        let bytes = if status == 200 {
            1000 + (i % 5) * 500 // Successful responses have larger sizes
        } else {
            100 + (i % 3) * 50 // Error responses are smaller
        } as u64;

        logs.push(LogEntry {
            ip: format!("192.168.1.{}", i % 256),
            method: method.to_string(),
            path: path.to_string(),
            status,
            bytes,
            timestamp,
        });
    }

    logs
}

IoT 设备数据分析

在物联网设备数据综合处理场景中，IoT Device Analysis 示例可同时处理来自不同设备的传感器数据，利用翻滚窗口实时监控并计算设备状态统计信息，为企业实现设备的高效管理和维护提供支持。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::collections::HashMap;
use std::time::{Duration, SystemTime};

#[derive(Clone)]
pub struct IoTData {
    device_id: String,
    device_type: String,
    value: f64,
    battery_level: u8,
    signal_strength: i32,
    timestamp: SystemTime,
}

#[derive(Clone)]
pub struct DeviceStats {
    device_id: String,
    device_type: String,
    avg_value: f64,
    min_battery: u8,
    avg_signal: i32,
    alert_count: u32,
    last_update: SystemTime,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample IoT device data
    let iot_data = generate_sample_data();
    let source = CollectionSource::new(iot_data);
    let sink = CollectionSink::new();

    // Build and execute stream processing pipeline
    DataStream::new(source)
        // Group by device ID
        .map(|data| (data.device_id.clone(), data))
        // Create 2-minute sliding window with 30-second slide
        .window(WindowConfig::sliding(
            Duration::from_secs(120), // 2 minutes
            Duration::from_secs(30),  // 30 seconds
        ))
        // Aggregate device statistics
        .aggregate(HashMap::new(), |mut stats, (device_id, data)| {
            let entry = stats
                .entry(device_id.clone())
                .or_insert_with(|| DeviceStats {
                    device_id,
                    device_type: data.device_type.clone(),
                    avg_value: 0.0,
                    min_battery: data.battery_level,
                    avg_signal: 0,
                    alert_count: 0,
                    last_update: data.timestamp,
                });

            // Update statistics
            entry.avg_value = (entry.avg_value + data.value) / 2.0;
            entry.min_battery = entry.min_battery.min(data.battery_level);
            entry.avg_signal = (entry.avg_signal + data.signal_strength) / 2;
            entry.last_update = data.timestamp;

            // Check alert conditions
            if data.battery_level < 20 || data.signal_strength < -90 {
                entry.alert_count += 1;
            }

            stats
        })
        // Output results to sink
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nIoT Device Statistics:");
    for result in sink.get_data() {
        for (_, stats) in result {
            println!(
                "Device ID: {}, Type: {}, Average Value: {:.2}, Min Battery: {}%, Average Signal: {}dBm, Alert Count: {}",
                stats.device_id,
                stats.device_type,
                stats.avg_value,
                stats.min_battery,
                stats.avg_signal,
                stats.alert_count
            );
        }
    }

    Ok(())
}

// Generate sample IoT device data
fn generate_sample_data() -> Vec<IoTData> {
    let device_types = [
        "Temperature Sensor",
        "Humidity Sensor",
        "Pressure Sensor",
        "Light Sensor",
    ];
    let mut data = Vec::new();
    let start_time = SystemTime::now();

    for i in 0..100 {
        for j in 1..=5 {
            let device_type = device_types[j % device_types.len()];
            let base_value = match device_type {
                "Temperature Sensor" => 25.0,
                "Humidity Sensor" => 60.0,
                "Pressure Sensor" => 1013.0,
                "Light Sensor" => 500.0,
                _ => 0.0,
            };

            // Simulate data fluctuation
            let value_variation = (i as f64 * 0.1).sin() * 5.0;
            let battery_drain = (i / 20) as u8; // Simulate battery consumption

            let reading = IoTData {
                device_id: format!("DEV_{:03}", j),
                device_type: device_type.to_string(),
                value: base_value + value_variation,
                battery_level: 100 - battery_drain,
                signal_strength: -70 - (i % 30), // Simulate signal strength fluctuation
                timestamp: start_time + Duration::from_secs(i as u64 * 15), // One data point every 15 seconds
            };
            data.push(reading);
        }
    }

    data
}

系统日志分析

Log Anomaly Detection 示例聚焦于系统日志数据，通过自定义窗口分析异常日志模式，及时发现潜在的系统故障和安全威胁，保障系统稳定运行。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::{
    collections::HashMap,
    time::{Duration, SystemTime},
};

#[derive(Clone)]
#[allow(dead_code)]
pub struct LogEvent {
    service: String,
    level: String,
    message: String,
    latency_ms: u64,
    timestamp: SystemTime,
}

#[derive(Clone)]
pub struct AnomalyStats {
    service: String,
    error_rate: f64,
    avg_latency: f64,
    error_count: u32,
    high_latency_count: u32,
    total_events: u32,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample log events
    let events = generate_sample_events();
    let source = CollectionSource::new(events);
    let sink = CollectionSink::new();

    // Build and execute stream processing pipeline
    DataStream::new(source)
        // Group by service name
        .map(|event| (event.service.clone(), event))
        // Create 1-minute sliding window with 10-second slide
        .window(WindowConfig::sliding(
            Duration::from_secs(60), // 1 minute
            Duration::from_secs(10), // 10 seconds
        ))
        // Aggregate anomaly statistics
        .aggregate(HashMap::new(), |mut stats, (service, event)| {
            let entry = stats
                .entry(service.clone())
                .or_insert_with(|| AnomalyStats {
                    service,
                    error_rate: 0.0,
                    avg_latency: 0.0,
                    error_count: 0,
                    high_latency_count: 0,
                    total_events: 0,
                });

            // Update statistics
            entry.total_events += 1;
            entry.avg_latency = (entry.avg_latency * (entry.total_events - 1) as f64
                + event.latency_ms as f64)
                / entry.total_events as f64;

            // Detect errors and high latency
            if event.level == "ERROR" {
                entry.error_count += 1;
            }
            if event.latency_ms > 1000 {
                // Latency over 1 second
                entry.high_latency_count += 1;
            }

            // Calculate error rate
            entry.error_rate = entry.error_count as f64 / entry.total_events as f64;

            stats
        })
        // Output results to sink
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nLog Anomaly Detection Statistics:");
    for result in sink.get_data() {
        for (_, stats) in result {
            println!(
                "Service: {}, Error Rate: {:.2}%, Avg Latency: {:.2}ms, Error Count: {}, High Latency Events: {}, Total Events: {}",
                stats.service,
                stats.error_rate * 100.0,
                stats.avg_latency,
                stats.error_count,
                stats.high_latency_count,
                stats.total_events
            );
        }
    }

    Ok(())
}

// Generate sample log events
fn generate_sample_events() -> Vec<LogEvent> {
    let services = vec![
        "api-gateway",
        "user-service",
        "order-service",
        "payment-service",
    ];
    let mut events = Vec::new();
    let start_time = SystemTime::now();

    for i in 0..200 {
        for service in &services {
            // Simulate different error probabilities for services
            let error_prob = match *service {
                "api-gateway" => 0.05,
                "user-service" => 0.02,
                "order-service" => 0.08,
                "payment-service" => 0.03,
                _ => 0.01,
            };

            // Randomly select log level
            let level = if rand_float() < error_prob {
                "ERROR"
            } else if rand_float() < 0.15 {
                "WARN"
            } else {
                "INFO"
            };

            // Simulate latency
            let base_latency = match *service {
                "api-gateway" => 50,
                "user-service" => 100,
                "order-service" => 150,
                "payment-service" => 200,
                _ => 100,
            };

            let latency = base_latency + (rand_float() * 1000.0) as u64;
            let message = format!("Processing request #{}", i);

            let event = LogEvent {
                service: service.to_string(),
                level: level.to_string(),
                message,
                latency_ms: latency,
                timestamp: start_time + Duration::from_secs(i as u64 / 2), // One event every 0.5 seconds
            };
            events.push(event);
        }
    }

    events
}

// Generate random float between 0 and 1
fn rand_float() -> f64 {
    use std::time::SystemTime;
    let nanos = SystemTime::now()
        .duration_since(SystemTime::UNIX_EPOCH)
        .unwrap()
        .subsec_nanos() as f64;
    (nanos % 1000.0) / 1000.0
}

金融领域

在金融领域，Stock Market Analysis 示例处理实时股票价格数据，运用会话窗口分析交易模式，并计算股票价格指标，为投资者提供决策依据。

use anyhow::Result;
use fluxus_api::{
    DataStream,
    io::{CollectionSink, CollectionSource},
};
use fluxus_core::WindowConfig;
use std::{
    collections::HashMap,
    time::{Duration, SystemTime},
};

#[derive(Clone)]
#[allow(dead_code)]
pub struct StockTrade {
    symbol: String,
    price: f64,
    volume: u64,
    timestamp: SystemTime,
}

#[derive(Clone)]
#[allow(dead_code)]
pub struct StockStats {
    symbol: String,
    vwap: f64, // Volume Weighted Average Price
    total_volume: u64,
    price_change: f64,
    high: f64,
    low: f64,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Generate sample stock trading data
    let trades = generate_sample_trades();
    let source = CollectionSource::new(trades);
    let sink = CollectionSink::new();

    // Build and execute stream processing pipeline
    DataStream::new(source)
        // Group by stock symbol
        .map(|trade| (trade.symbol.clone(), trade))
        // Create 5-minute sliding window with 1-minute slide
        .window(WindowConfig::sliding(
            Duration::from_secs(300), // 5 minutes
            Duration::from_secs(60),  // 1 minute
        ))
        // Aggregate stock statistics within each window
        .aggregate(HashMap::new(), |mut stats, (symbol, trade)| {
            let entry = stats.entry(symbol.clone()).or_insert_with(|| StockStats {
                symbol,
                vwap: 0.0,
                total_volume: 0,
                price_change: 0.0,
                high: trade.price,
                low: trade.price,
            });

            // Update statistics
            let volume_price =
                (entry.vwap * entry.total_volume as f64) + (trade.price * trade.volume as f64);
            entry.total_volume += trade.volume;
            entry.vwap = volume_price / entry.total_volume as f64;
            entry.high = entry.high.max(trade.price);
            entry.low = entry.low.min(trade.price);
            entry.price_change = entry.high - entry.low;

            stats
        })
        // Output results to sink
        .sink(sink.clone())
        .await?;

    // Print results
    println!("\nStock Market Statistics:");
    for result in sink.get_data() {
        for (symbol, stats) in result {
            println!(
                "Stock: {}, VWAP: {:.2}, Volume: {}, Price Change: {:.2}, High: {:.2}, Low: {:.2}",
                symbol, stats.vwap, stats.total_volume, stats.price_change, stats.high, stats.low
            );
        }
    }

    Ok(())
}

// Generate sample trading data
fn generate_sample_trades() -> Vec<StockTrade> {
    let symbols = vec!["AAPL", "GOOGL", "MSFT", "AMZN"];
    let mut trades = Vec::new();
    let start_time = SystemTime::now();

    for i in 0..100 {
        for symbol in &symbols {
            let base_price = match *symbol {
                "AAPL" => 150.0,
                "GOOGL" => 2800.0,
                "MSFT" => 300.0,
                "AMZN" => 3300.0,
                _ => 100.0,
            };

            // Simulate price fluctuation
            let price_variation = (i as f64 * 0.1).sin() * 5.0;
            let trade = StockTrade {
                symbol: symbol.to_string(),
                price: base_price + price_variation,
                volume: 100 + (i as u64 % 900),
                timestamp: start_time + Duration::from_secs(i as u64 * 30), // Data point every 30 seconds
            };
            trades.push(trade);
        }
    }

    trades
}

这些示例遵循相似的结构，从定义数据结构、创建数据源，到构建处理管道、配置窗口、定义聚合，再到输出结果，为开发者提供了清晰的学习路径。

建议开发者按照从基础到进阶的顺序逐步探索，先从 Word Count 示例掌握基本概念，再通过 Temperature Sensor 示例学习时间窗口应用，接着借助 Click Stream 示例了解会话窗口，之后深入研究 Network Log 示例的高级特性，以及 IoT Devices 示例的多数据源处理、Log Anomaly 示例的自定义窗口和 Stock Market 示例的实时监控，全面掌握 Fluxus 流处理引擎的应用技巧。

从文本分析到金融市场洞察，从物联网设备数据处理到网络安全监控，Fluxus 在众多领域都展现出强大的应用潜力。

随着 Fluxus 的正式发布，实时数据处理的大门将被进一步打开。

无论是追求极致性能的企业，还是渴望高效开发的开发者，Fluxus 都有望成为他们在实时数据处理领域的得力助手。