大型广告系统架构设计与实战：从0到1打造日均千亿曝光的广告平台
大型广告系统架构设计与实战：从0到1打造日均千亿曝光的广告平台
老板说要搞个广告系统，日均曝光要达到千亿级别，还要支持实时竞价、精准投放、效果追踪...听起来是不是很刺激？今天就来聊聊如何从0到1打造一个大型广告系统，让你的广告平台既能扛住高并发，又能实现精准投放！
一、广告系统的核心挑战
在开始设计架构之前，我们先来理解广告系统面临的挑战。
1.1 业务复杂性
// 广告系统的业务复杂性
public class AdSystemChallenges {
public void businessComplexity() {
System.out.println("=== 广告系统的核心挑战 ===");
System.out.println("1. 高并发处理：日均千亿级曝光请求");
System.out.println("2. 实时性要求：毫秒级响应");
System.out.println("3. 精准投放：基于用户画像的个性化推荐");
System.out.println("4. 实时竞价：RTB（Real Time Bidding）");
System.out.println("5. 效果追踪：点击率、转化率等指标统计");
System.out.println("6. 反作弊：识别和过滤无效流量");
System.out.println("7. 数据分析：海量数据的实时处理");
}
}
1.2 技术难点
广告系统的技术难点主要体现在以下几个方面：
// 技术难点分析
public class TechnicalChallenges {
public void technicalDifficulties() {
System.out.println("=== 广告系统的技术难点 ===");
System.out.println("数据处理：");
System.out.println("- 海量用户行为数据实时处理");
System.out.println("- 多维度用户画像构建");
System.out.println("- 实时特征计算");
System.out.println("\n系统架构：");
System.out.println("- 高可用性设计");
System.out.println("- 水平扩展能力");
System.out.println("- 容错和降级机制");
System.out.println("\n算法优化：");
System.out.println("- CTR预估模型");
System.out.println("- 出价策略优化");
System.out.println("- 排序算法设计");
}
}
二、广告系统架构设计
2.1 整体架构图
graph TB
A[用户请求] --> B[流量接入层]
B --> C[广告检索服务]
C --> D[用户画像服务]
C --> E[广告库存服务]
C --> F[竞价服务]
D --> G[实时计算引擎]
E --> H[广告库存数据库]
F --> I[DSP平台]
G --> J[用户画像存储]
H --> K[广告管理平台]
I --> L[竞价决策]
L --> M[广告投放引擎]
M --> N[广告展示]
N --> O[效果追踪]
O --> P[数据分析平台]
style A fill:#ffe4c4,stroke:#333
style B fill:#98fb98,stroke:#333
style C fill:#87ceeb,stroke:#333
style D fill:#dda0dd,stroke:#333
style E fill:#ffb6c1,stroke:#333
style F fill:#f0e68c,stroke:#333
style G fill:#98fb98,stroke:#333
style H fill:#87ceeb,stroke:#333
style I fill:#dda0dd,stroke:#333
style J fill:#ffb6c1,stroke:#333
style K fill:#f0e68c,stroke:#333
style L fill:#98fb98,stroke:#333
style M fill:#87ceeb,stroke:#333
style N fill:#dda0dd,stroke:#333
style O fill:#ffb6c1,stroke:#333
style P fill:#f0e68c,stroke:#333
2.2 核心模块设计
2.2.1 流量接入层
// 流量接入层设计
@Component
@Slf4j
public class TrafficAccessLayer {
@Autowired
private AdRetrievalService adRetrievalService;
@Autowired
private AntiCheatingService antiCheatingService;
/**
* 处理广告请求
*/
public AdResponse handleAdRequest(AdRequest request) {
try {
// 1. 请求验证和反作弊检测
if (!antiCheatingService.isValidRequest(request)) {
log.warn("无效广告请求: requestId={}", request.getRequestId());
return AdResponse.emptyResponse();
}
// 2. 参数解析和标准化
StandardizedRequest standardizedRequest = standardizeRequest(request);
// 3. 调用广告检索服务
AdResponse response = adRetrievalService.retrieveAds(standardizedRequest);
// 4. 响应处理和日志记录
logAdRequest(standardizedRequest, response);
return response;
} catch (Exception e) {
log.error("处理广告请求失败: requestId={}", request.getRequestId(), e);
return AdResponse.errorResponse();
}
}
private StandardizedRequest standardizeRequest(AdRequest request) {
// 标准化请求参数
StandardizedRequest standardized = new StandardizedRequest();
standardized.setRequestId(request.getRequestId());
standardized.setUserId(request.getUserId());
standardized.setDeviceId(request.getDeviceId());
standardized.setGeoLocation(request.getGeoLocation());
standardized.setUserAgent(request.getUserAgent());
standardized.setTimestamp(System.currentTimeMillis());
return standardized;
}
private void logAdRequest(StandardizedRequest request, AdResponse response) {
// 记录广告请求日志，用于后续分析
AdRequestLog logEntry = new AdRequestLog();
logEntry.setRequestId(request.getRequestId());
logEntry.setUserId(request.getUserId());
logEntry.setTimestamp(request.getTimestamp());
logEntry.setAdCount(response.getAds().size());
logEntry.setResponseTime(System.currentTimeMillis() - request.getTimestamp());
// 异步写入日志系统
logService.asyncLogAdRequest(logEntry);
}
}
2.2.2 广告检索服务
@Service
@Slf4j
public class AdRetrievalService {
@Autowired
private UserProfileService userProfileService;
@Autowired
private AdInventoryService adInventoryService;
@Autowired
private BiddingService biddingService;
@Autowired
private RankingService rankingService;
/**
* 广告检索主流程
*/
public AdResponse retrieveAds(StandardizedRequest request) {
long startTime = System.currentTimeMillis();
try {
// 1. 获取用户画像
UserProfile userProfile = userProfileService.getUserProfile(request.getUserId());
// 2. 获取候选广告
List<AdCandidate> candidates = adInventoryService.getCandidates(
request.getGeoLocation(), request.getDeviceType());
// 3. 实时竞价
List<BidResult> bidResults = biddingService.realTimeBidding(candidates, request);
// 4. 广告排序
List<RankedAd> rankedAds = rankingService.rankAds(bidResults, userProfile, request);
// 5. 广告过滤和截断
List<RankedAd> filteredAds = filterAndLimitAds(rankedAds, request.getAdSlot());
// 6. 构造响应
AdResponse response = buildAdResponse(filteredAds, request);
long endTime = System.currentTimeMillis();
log.info("广告检索完成: requestId={}, candidateCount={}, finalCount={}, time={}ms",
request.getRequestId(), candidates.size(), filteredAds.size(),
endTime - startTime);
return response;
} catch (Exception e) {
log.error("广告检索失败: requestId={}", request.getRequestId(), e);
return AdResponse.errorResponse();
}
}
private List<RankedAd> filterAndLimitAds(List<RankedAd> rankedAds, AdSlot adSlot) {
return rankedAds.stream()
.filter(ad -> ad.getScore() > 0) // 过滤低分广告
.filter(ad -> ad.getBidPrice() <= adSlot.getMaxBid()) // 价格过滤
.limit(adSlot.getCapacity()) // 数量限制
.collect(Collectors.toList());
}
private AdResponse buildAdResponse(List<RankedAd> ads, StandardizedRequest request) {
AdResponse response = new AdResponse();
response.setRequestId(request.getRequestId());
response.setAds(ads.stream()
.map(this::convertToAdDto)
.collect(Collectors.toList()));
response.setTimestamp(System.currentTimeMillis());
return response;
}
private AdDto convertToAdDto(RankedAd rankedAd) {
AdDto dto = new AdDto();
dto.setAdId(rankedAd.getAdId());
dto.setCreativeId(rankedAd.getCreativeId());
dto.setAdvertiserId(rankedAd.getAdvertiserId());
dto.setBidPrice(rankedAd.getBidPrice());
dto.setScore(rankedAd.getScore());
dto.setClickUrl(rankedAd.getClickUrl());
dto.setImpressionUrl(rankedAd.getImpressionUrl());
return dto;
}
}
三、核心算法实现
3.1 CTR预估模型
@Service
@Slf4j
public class CtrPredictionService {
// 逻辑回归模型参数
private double[] weights;
private double bias;
/**
* CTR预估
*/
public double predictCtr(AdFeature adFeature, UserFeature userFeature, ContextFeature contextFeature) {
try {
// 1. 特征工程
double[] features = extractFeatures(adFeature, userFeature, contextFeature);
// 2. 逻辑回归预测
double linearScore = bias;
for (int i = 0; i < features.length && i < weights.length; i++) {
linearScore += features[i] * weights[i];
}
// 3. Sigmoid激活函数
double ctr = sigmoid(linearScore);
return Math.max(0.0, Math.min(1.0, ctr)); // 保证CTR在[0,1]范围内
} catch (Exception e) {
log.error("CTR预估失败", e);
return 0.01; // 默认CTR
}
}
/**
* 特征提取
*/
private double[] extractFeatures(AdFeature adFeature, UserFeature userFeature, ContextFeature contextFeature) {
List<Double> featureList = new ArrayList<>();
// 广告特征
featureList.add(normalize(adFeature.getBidPrice(), 0, 10)); // 出价归一化
featureList.add(adFeature.getAdvertiserQualityScore()); // 广告主质量分
featureList.add(adFeature.getCreativeRelevanceScore()); // 创意相关性得分
// 用户特征
featureList.add(userFeature.getHistoricalCtr()); // 历史CTR
featureList.add(userFeature.getConversionRate()); // 转化率
featureList.add(userFeature.getEngagementScore()); // 用户活跃度
// 上下文特征
featureList.add(contextFeature.getTimeOfDayFactor()); // 时段因子
featureList.add(contextFeature.getDeviceTypeFactor()); // 设备类型因子
featureList.add(contextFeature.getGeoFactor()); // 地理位置因子
return featureList.stream().mapToDouble(Double::doubleValue).toArray();
}
private double sigmoid(double x) {
return 1.0 / (1.0 + Math.exp(-x));
}
private double normalize(double value, double min, double max) {
return (value - min) / (max - min);
}
}
3.2 实时竞价算法
@Service
@Slf4j
public class BiddingService {
@Autowired
private CtrPredictionService ctrPredictionService;
@Autowired
private UserModelService userModelService;
/**
* 实时竞价
*/
public List<BidResult> realTimeBidding(List<AdCandidate> candidates, StandardizedRequest request) {
List<BidResult> bidResults = new ArrayList<>();
for (AdCandidate candidate : candidates) {
try {
// 1. CTR预估
double ctr = ctrPredictionService.predictCtr(
candidate.getAdFeature(),
candidate.getUserFeature(),
candidate.getContextFeature());
// 2. eCPM计算 (eCPM = 出价 * CTR * 1000)
double ecpm = candidate.getBidPrice() * ctr * 1000;
// 3. 出价调整（基于用户模型）
double adjustedBid = adjustBid(candidate, request, ctr);
BidResult bidResult = new BidResult();
bidResult.setAdId(candidate.getAdId());
bidResult.setBidPrice(adjustedBid);
bidResult.setEcpm(ecpm);
bidResult.setCtr(ctr);
bidResults.add(bidResult);
} catch (Exception e) {
log.error("广告竞价失败: adId={}", candidate.getAdId(), e);
}
}
// 4. 按eCPM排序
bidResults.sort((a, b) -> Double.compare(b.getEcpm(), a.getEcpm()));
return bidResults;
}
/**
* 出价调整
*/
private double adjustBid(AdCandidate candidate, StandardizedRequest request, double ctr) {
// 获取用户出价模型
UserBiddingModel userModel = userModelService.getBiddingModel(request.getUserId());
// 基于用户历史行为调整出价
double adjustmentFactor = 1.0;
if (userModel != null) {
// 如果用户历史转化率高，可以适当提高出价
if (userModel.getConversionRate() > 0.05) {
adjustmentFactor = 1.2;
}
// 如果用户活跃度低，降低出价
else if (userModel.getActivityScore() < 0.3) {
adjustmentFactor = 0.8;
}
}
// 结合CTR进行调整
double ctrFactor = Math.min(ctr * 100, 2.0); // CTR越高，出价越高，但有上限
return candidate.getBidPrice() * adjustmentFactor * ctrFactor;
}
}
四、高并发架构优化
4.1 缓存策略
@Service
@Slf4j
public class CacheOptimizationService {
@Autowired
private RedisTemplate<String, Object> redisTemplate;
@Autowired
private LoadingCache<String, UserProfile> userProfileCache;
// 用户画像缓存（本地+Redis）
private final LoadingCache<String, UserProfile> userProfileLocalCache =
Caffeine.newBuilder()
.maximumSize(100000)
.expireAfterWrite(30, TimeUnit.MINUTES)
.recordStats()
.build(this::loadUserProfile);
/**
* 多级缓存获取用户画像
*/
public UserProfile getUserProfile(String userId) {
// 1. 本地缓存
UserProfile profile = userProfileLocalCache.getIfPresent(userId);
if (profile != null) {
return profile;
}
// 2. Redis缓存
String redisKey = "user:profile:" + userId;
profile = (UserProfile) redisTemplate.opsForValue().get(redisKey);
if (profile != null) {
userProfileLocalCache.put(userId, profile);
return profile;
}
// 3. 数据库查询
profile = loadUserProfileFromDatabase(userId);
if (profile != null) {
// 回写缓存
redisTemplate.opsForValue().set(redisKey, profile, Duration.ofHours(1));
userProfileLocalCache.put(userId, profile);
}
return profile != null ? profile : new DefaultUserProfile();
}
/**
* 批量预热缓存
*/
public void preheatUserProfiles(List<String> userIds) {
try {
List<UserProfile> profiles = loadUserProfilesFromDatabase(userIds);
for (UserProfile profile : profiles) {
String redisKey = "user:profile:" + profile.getUserId();
redisTemplate.opsForValue().set(redisKey, profile, Duration.ofHours(1));
userProfileLocalCache.put(profile.getUserId(), profile);
}
log.info("预热用户画像缓存完成: count={}", profiles.size());
} catch (Exception e) {
log.error("预热用户画像缓存失败", e);
}
}
private UserProfile loadUserProfile(String userId) {
return loadUserProfileFromDatabase(userId);
}
private UserProfile loadUserProfileFromDatabase(String userId) {
// 数据库查询逻辑
return null; // 简化实现
}
private List<UserProfile> loadUserProfilesFromDatabase(List<String> userIds) {
// 批量数据库查询逻辑
return new ArrayList<>(); // 简化实现
}
}
4.2 异步处理
@Component
@Slf4j
public class AsyncProcessingService {
@Autowired
private ExecutorService adProcessingExecutor;
@Autowired
private KafkaTemplate<String, Object> kafkaTemplate;
/**
* 异步处理广告效果数据
*/
@Async
public void asyncProcessAdEffect(AdEffectData effectData) {
try {
// 1. 写入消息队列
kafkaTemplate.send("ad-effect-topic", effectData.getRequestId(), effectData);
// 2. 更新实时统计
updateRealTimeMetrics(effectData);
// 3. 异步写入数据仓库
asyncWriteToDataWarehouse(effectData);
} catch (Exception e) {
log.error("异步处理广告效果数据失败: requestId={}", effectData.getRequestId(), e);
}
}
/**
* 批量处理广告数据
*/
public void batchProcessAdEffects(List<AdEffectData> effectDataList) {
adProcessingExecutor.submit(() -> {
try {
// 批量处理逻辑
processAdEffectsInBatch(effectDataList);
} catch (Exception e) {
log.error("批量处理广告效果数据失败", e);
}
});
}
private void updateRealTimeMetrics(AdEffectData effectData) {
// 更新实时指标统计
}
private void asyncWriteToDataWarehouse(AdEffectData effectData) {
// 异步写入数据仓库
}
private void processAdEffectsInBatch(List<AdEffectData> effectDataList) {
// 批量处理逻辑
}
}
五、数据处理与分析
5.1 实时计算引擎
@Component
@Slf4j
public class RealTimeComputationEngine {
@Autowired
private StreamProcessingService streamProcessingService;
/**
* 实时特征计算
*/
public void computeRealTimeFeatures() {
// 1. 用户行为流处理
streamProcessingService.processUserBehaviorStream()
.filter(event -> isValidEvent(event))
.map(event -> extractFeatures(event))
.keyBy(feature -> feature.getUserId())
.window(TumblingProcessingTimeWindows.of(Time.minutes(5)))
.aggregate(new UserFeatureAggregator())
.addSink(new UserFeatureSink());
// 2. 广告效果流处理
streamProcessingService.processAdEffectStream()
.filter(effect -> isValidEffect(effect))
.keyBy(effect -> effect.getAdId())
.window(SlidingProcessingTimeWindows.of(Time.minutes(10), Time.minutes(1)))
.aggregate(new AdEffectAggregator())
.addSink(new AdEffectSink());
}
/**
* 用户特征聚合器
*/
public static class UserFeatureAggregator implements AggregateFunction<UserEvent, UserFeatureAccumulator, UserFeature> {
@Override
public UserFeatureAccumulator createAccumulator() {
return new UserFeatureAccumulator();
}
@Override
public UserFeatureAccumulator add(UserEvent event, UserFeatureAccumulator accumulator) {
accumulator.addEvent(event);
return accumulator;
}
@Override
public UserFeature getResult(UserFeatureAccumulator accumulator) {
return accumulator.toUserFeature();
}
@Override
public UserFeatureAccumulator merge(UserFeatureAccumulator a, UserFeatureAccumulator b) {
return a.merge(b);
}
}
}
5.2 离线数据分析
@Service
@Slf4j
public class OfflineDataAnalysisService {
@Autowired
private SparkSession sparkSession;
/**
* 广告效果分析
*/
public AdPerformanceReport analyzeAdPerformance(String dateRange) {
try {
// 1. 加载数据
Dataset<Row> adImpressions = sparkSession.read()
.parquet("hdfs://ad-impressions/" + dateRange);
Dataset<Row> adClicks = sparkSession.read()
.parquet("hdfs://ad-clicks/" + dateRange);
// 2. 计算CTR
Dataset<Row> ctrAnalysis = adImpressions
.join(adClicks, "ad_id")
.groupBy("ad_id", "advertiser_id")
.agg(
count("impression_id").as("impressions"),
count("click_id").as("clicks"),
(count("click_id").divide(count("impression_id"))).as("ctr")
);
// 3. 生成报告
AdPerformanceReport report = new AdPerformanceReport();
report.setDateRange(dateRange);
report.setTotalImpressions(ctrAnalysis.agg(sum("impressions")).first().getLong(0));
report.setTotalClicks(ctrAnalysis.agg(sum("clicks")).first().getLong(0));
report.setAverageCtr(ctrAnalysis.agg(avg("ctr")).first().getDouble(0));
return report;
} catch (Exception e) {
log.error("广告效果分析失败", e);
throw new RuntimeException("数据分析失败", e);
}
}
/**
* 用户画像分析
*/
public UserProfileAnalysis analyzeUserProfiles(String dateRange) {
try {
Dataset<Row> userData = sparkSession.read()
.parquet("hdfs://user-profiles/" + dateRange);
// 用户分群分析
Dataset<Row> userSegments = userData
.groupBy("age_group", "interest_category")
.agg(count("*").as("user_count"))
.orderBy(desc("user_count"));
UserProfileAnalysis analysis = new UserProfileAnalysis();
analysis.setDateRange(dateRange);
// 设置分析结果...
return analysis;
} catch (Exception e) {
log.error("用户画像分析失败", e);
throw new RuntimeException("用户分析失败", e);
}
}
}
六、监控与告警
6.1 核心指标监控
@Component
public class AdSystemMetrics {
private final MeterRegistry meterRegistry;
// QPS指标
private final Counter qpsCounter;
private final Timer responseTimeTimer;
// 业务指标
private final Counter impressionCounter;
private final Counter clickCounter;
private final Gauge ctrGauge;
// 系统指标
private final Gauge cacheHitRateGauge;
private final Counter errorCounter;
public AdSystemMetrics(MeterRegistry meterRegistry) {
this.meterRegistry = meterRegistry;
this.qpsCounter = Counter.builder("ad.system.qps")
.description("广告系统QPS")
.register(meterRegistry);
this.responseTimeTimer = Timer.builder("ad.system.response.time")
.description("广告系统响应时间")
.register(meterRegistry);
this.impressionCounter = Counter.builder("ad.impressions")
.description("广告曝光数")
.register(meterRegistry);
this.clickCounter = Counter.builder("ad.clicks")
.description("广告点击数")
.register(meterRegistry);
this.ctrGauge = Gauge.builder("ad.ctr")
.description("点击率")
.register(meterRegistry, this, AdSystemMetrics::calculateCurrentCtr);
this.cacheHitRateGauge = Gauge.builder("ad.cache.hit.rate")
.description("缓存命中率")
.register(meterRegistry, this, AdSystemMetrics::getCacheHitRate);
this.errorCounter = Counter.builder("ad.errors")
.description("广告系统错误数")
.register(meterRegistry);
}
public void recordAdRequest() {
qpsCounter.increment();
}
public Sample startResponseTimer() {
return Timer.start(meterRegistry);
}
public void recordResponseTime(Sample sample) {
sample.stop(responseTimeTimer);
}
public void recordImpression() {
impressionCounter.increment();
}
public void recordClick() {
clickCounter.increment();
}
public void recordError() {
errorCounter.increment();
}
private double calculateCurrentCtr() {
double impressions = impressionCounter.count();
double clicks = clickCounter.count();
return impressions > 0 ? clicks / impressions : 0.0;
}
private double getCacheHitRate() {
// 获取缓存命中率逻辑
return 0.95; // 示例值
}
}
6.2 告警规则配置
# Prometheus告警规则
groups:
- name: ad_system.rules
rules:
# QPS异常告警
- alert: HighQPS
expr: rate(ad_system_qps[5m]) > 1000000
for: 2m
labels:
severity: critical
annotations:
summary: "广告系统QPS过高"
description: "5分钟平均QPS超过100万: {{ $value }}"
# 响应时间告警
- alert: HighResponseTime
expr: histogram_quantile(0.95, rate(ad_system_response_time_seconds_bucket[5m])) > 0.1
for: 1m
labels:
severity: warning
annotations:
summary: "广告系统响应时间过长"
description: "95%请求响应时间超过100ms: {{ $value }}"
# 错误率告警
- alert: HighErrorRate
expr: rate(ad_errors[5m]) / rate(ad_system_qps[5m]) > 0.01
for: 1m
labels:
severity: warning
annotations:
summary: "广告系统错误率过高"
description: "错误率超过1%: {{ $value }}"
# CTR异常告警
- alert: LowCTR
expr: ad_ctr < 0.001
for: 10m
labels:
severity: warning
annotations:
summary: "广告点击率过低"
description: "CTR低于0.1%: {{ $value }}"
七、性能优化实践
7.1 数据库优化
@Repository
public class AdInventoryRepository {
@Autowired
private JdbcTemplate jdbcTemplate;
/**
* 批量查询广告库存
*/
public List<AdInventory> batchQueryInventories(List<Long> adIds) {
String sql = "SELECT * FROM ad_inventory WHERE ad_id IN (" +
adIds.stream().map(String::valueOf).collect(Collectors.joining(",")) +
") AND status = 1 AND remaining_budget > 0";
return jdbcTemplate.query(sql, new AdInventoryRowMapper());
}
/**
* 分页查询广告
*/
public List<AdInventory> queryAdsByPage(String conditions, int offset, int limit) {
String sql = "SELECT * FROM ad_inventory WHERE " + conditions +
" ORDER BY priority DESC, bid_price DESC LIMIT ?, ?";
return jdbcTemplate.query(sql, new AdInventoryRowMapper(), offset, limit);
}
/**
* 更新广告预算
*/
@Modifying
@Transactional
public int updateAdBudget(Long adId, BigDecimal consumedAmount) {
String sql = "UPDATE ad_inventory SET remaining_budget = remaining_budget - ?, " +
"consumed_budget = consumed_budget + ? WHERE ad_id = ? AND remaining_budget >= ?";
return jdbcTemplate.update(sql, consumedAmount, consumedAmount, adId, consumedAmount);
}
}
@Component
public class AdInventoryRowMapper implements RowMapper<AdInventory> {
@Override
public AdInventory mapRow(ResultSet rs, int rowNum) throws SQLException {
AdInventory inventory = new AdInventory();
inventory.setAdId(rs.getLong("ad_id"));
inventory.setAdvertiserId(rs.getLong("advertiser_id"));
inventory.setBidPrice(rs.getBigDecimal("bid_price"));
inventory.setRemainingBudget(rs.getBigDecimal("remaining_budget"));
inventory.setDailyBudget(rs.getBigDecimal("daily_budget"));
inventory.setPriority(rs.getInt("priority"));
inventory.setStatus(rs.getInt("status"));
inventory.setStartTime(rs.getTimestamp("start_time"));
inventory.setEndTime(rs.getTimestamp("end_time"));
return inventory;
}
}
7.2 内存优化
@Service
@Slf4j
public class MemoryOptimizationService {
// 对象池减少GC压力
private final ObjectPool<AdRequestContext> contextPool =
new GenericObjectPool<>(new AdRequestContextFactory());
/**
* 获取请求上下文（从对象池）
*/
public AdRequestContext borrowContext() {
try {
return contextPool.borrowObject();
} catch (Exception e) {
log.error("从对象池获取上下文失败", e);
return new AdRequestContext(); // 备用创建
}
}
/**
* 归还请求上下文（到对象池）
*/
public void returnContext(AdRequestContext context) {
try {
context.reset(); // 重置状态
contextPool.returnObject(context);
} catch (Exception e) {
log.error("归还上下文到对象池失败", e);
}
}
/**
* 使用Off-Heap内存存储热点数据
*/
public void storeHotDataOffHeap(String key, byte[] data) {
// 使用Off-Heap内存库（如Ehcache的Terracotta)
OffHeapStore.getInstance().put(key, data);
}
public byte[] getHotDataOffHeap(String key) {
return OffHeapStore.getInstance().get(key);
}
}
八、容错与降级
8.1 熔断机制
@Service
@Slf4j
public class CircuitBreakerService {
// 用户画像服务熔断器
private final CircuitBreaker userProfileCircuitBreaker =
CircuitBreaker.ofDefaults("userProfileService");
// 广告库存服务熔断器
private final CircuitBreaker inventoryCircuitBreaker =
CircuitBreaker.ofDefaults("inventoryService");
@Autowired
private UserProfileService userProfileService;
@Autowired
private AdInventoryService adInventoryService;
/**
* 带熔断的用户画像获取
*/
public UserProfile getUserProfileSafely(String userId) {
return CircuitBreaker.decorateSupplier(userProfileCircuitBreaker,
() -> userProfileService.getUserProfile(userId))
.recover(throwable -> {
log.warn("用户画像服务熔断，使用默认画像: userId={}", userId);
return new DefaultUserProfile();
})
.get();
}
/**
* 带熔断的广告库存获取
*/
public List<AdCandidate> getInventorySafely(GeoLocation location, DeviceType deviceType) {
return CircuitBreaker.decorateSupplier(inventoryCircuitBreaker,
() -> adInventoryService.getCandidates(location, deviceType))
.recover(throwable -> {
log.warn("广告库存服务熔断，使用备用库存");
return getFallbackInventory();
})
.get();
}
private List<AdCandidate> getFallbackInventory() {
// 返回备用广告库存
return new ArrayList<>();
}
}
8.2 降级策略
@Service
@Slf4j
public class DegradationStrategyService {
@Autowired
private RedisTemplate<String, Object> redisTemplate;
/**
* 降级处理策略
*/
public AdResponse handleDegradation(StandardizedRequest request, DegradationLevel level) {
switch (level) {
case LIGHT:
// 轻度降级：减少广告数量，使用缓存数据
return handleLightDegradation(request);
case MEDIUM:
// 中度降级：只返回高优先级广告，关闭实时竞价
return handleMediumDegradation(request);
case HEAVY:
// 重度降级：返回默认广告，关闭个性化
return handleHeavyDegradation(request);
default:
return AdResponse.errorResponse();
}
}
private AdResponse handleLightDegradation(StandardizedRequest request) {
// 减少广告返回数量
// 使用本地缓存的用户画像
log.info("执行轻度降级策略: requestId={}", request.getRequestId());
return buildDegradedResponse(request, 3); // 返回3个广告
}
private AdResponse handleMediumDegradation(StandardizedRequest request) {
// 只返回高优先级广告
// 关闭实时竞价，使用固定出价
log.info("执行中度降级策略: requestId={}", request.getRequestId());
return buildDegradedResponse(request, 2); // 返回2个广告
}
private AdResponse handleHeavyDegradation(StandardizedRequest request) {
// 返回默认广告
// 关闭个性化推荐
log.info("执行重度降级策略: requestId={}", request.getRequestId());
return buildDefaultResponse(request); // 返回默认广告
}
private AdResponse buildDegradedResponse(StandardizedRequest request, int adCount) {
// 构建降级响应
return new AdResponse();
}
private AdResponse buildDefaultResponse(StandardizedRequest request) {
// 构建默认响应
return new AdResponse();
}
}
九、最佳实践总结
9.1 架构设计原则
public class ArchitecturePrinciples {
public void explainPrinciples() {
System.out.println("=== 广告系统架构设计原则 ===");
System.out.println("1. 高内聚低耦合：各模块职责清晰");
System.out.println("2. 可扩展性：支持水平扩展");
System.out.println("3. 高可用性：具备容错和降级能力");
System.out.println("4. 性能优先：毫秒级响应要求");
System.out.println("5. 数据驱动：基于数据的决策优化");
System.out.println("6. 安全可靠：防作弊和数据保护");
}
}
9.2 开发运维规范
public class DevOpsPractices {
public void explainPractices() {
System.out.println("=== 广告系统开发运维规范 ===");
System.out.println("开发规范：");
System.out.println("- 代码审查制度");
System.out.println("- 单元测试覆盖率>80%");
System.out.println("- 性能基准测试");
System.out.println("- A/B测试机制");
System.out.println("\n运维规范：");
System.out.println("- 自动化部署");
System.out.println("- 灰度发布策略");
System.out.println("- 容量规划");
System.out.println("- 故障演练");
System.out.println("- 监控告警体系");
}
}
结语
打造一个大型广告系统是一个复杂而充满挑战的过程，需要综合考虑业务需求、技术架构、性能优化、监控运维等多个方面。通过合理的架构设计和持续的优化，我们可以构建出既能满足高并发需求，又能实现精准投放的广告平台。
关键要点总结：
- 合理的架构分层：流量接入、广告检索、实时计算、数据存储等层次清晰
- 核心算法优化：CTR预估、实时竞价、排序算法等是系统的核心竞争力
- 高并发处理：缓存策略、异步处理、数据库优化等手段提升系统性能
- 监控告警体系：完善的指标收集和告警机制保障系统稳定运行
- 容错降级机制：熔断、降级等手段保证系统在异常情况下的可用性
如果你觉得这篇文章对你有帮助，欢迎分享给更多的朋友。在构建高并发广告系统的路上，我们一起成长！
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标题：大型广告系统架构设计与实战：从0到1打造日均千亿曝光的广告平台
作者：jiangyi
地址：http://jiangyi.space/articles/2025/12/21/1766304292859.html
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