NRT 近实时搜索深度解析：Elasticsearch 核心机制与应用实践

本文深入探讨 NRT（Near Real-Time）近实时搜索的核心原理，详细分析 Elasticsearch 如何实现近实时搜索，并提供完整的实战应用案例。

一、NRT 近实时搜索：概念与演进

1.1 从实时到近实时：搜索技术的演进

在传统搜索系统中，数据索引通常采用批处理模式，这意味着数据更新后需要等待索引重建完成才能被搜索到。随着互联网应用的快速发展，这种延迟逐渐变得不可接受。

NRT 近实时搜索填补了实时搜索和批处理搜索之间的空白：

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1.2 NRT 的核心定义

NRT（Near Real-Time） 是指数据写入后，能够在秒级（通常1秒内） 被搜索到的能力。与真正的实时搜索（毫秒级）相比，NRT在保证搜索性能的同时，提供了更好的系统稳定性和吞吐量。

// NRT与实时、批处理的对比
public class SearchLatencyComparison {

/**
* 不同搜索模式的延迟对比
*/
public void compareSearchModes() {
// 批处理模式（传统数据库）
long batchLatency = 1000 * 60 * 60; // 1小时

// 近实时模式（Elasticsearch）
long nrtLatency = 1000; // 1秒

// 实时模式（内存数据库）
long realtimeLatency = 10; // 10毫秒

System.out.println("批处理延迟: " + batchLatency + "ms");
System.out.println("近实时延迟: " + nrtLatency + "ms");
System.out.println("实时延迟: " + realtimeLatency + "ms");

// NRT的优势：在可接受的延迟内提供最佳性价比
double costPerformance = calculateCostPerformance(
batchLatency, nrtLatency, realtimeLatency
);
System.out.println("NRT性价比指数: " + costPerformance);
}

/**
* NRT的应用权衡
*/
public void nrtTradeoffs() {
// NRT不是追求绝对实时，而是在多个因素间找到平衡：
// 1. 数据新鲜度 vs 系统性能
// 2. 搜索延迟 vs 写入吞吐量
// 3. 数据一致性 vs 可用性

Map<String, String> tradeoffs = new HashMap<>();
tradeoffs.put("数据新鲜度", "1秒内可搜");
tradeoffs.put("写入性能", "高吞吐量");
tradeoffs.put("系统资源", "合理的内存和CPU使用");
tradeoffs.put("成本效益", "比实时系统成本低70%");

tradeoffs.forEach((k, v) ->
System.out.println(k + ": " + v));
}
}

二、Elasticsearch NRT 架构深度剖析

2.1 Elasticsearch NRT 的核心组件

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Elasticsearch NRT 架构

数据写入

Indexing Buffer

Lucene Segment

Translog

Refresh 操作

新Segment可搜索

搜索请求

所有Segments

返回结果

定期Flush

Segment持久化

清理Translog

2.2 Lucene Segment：NRT 的基础

/**
* Lucene Segment 管理示例
* Segment是Lucene索引的基本单元，也是NRT实现的基础
*/
public class LuceneSegmentExample {

/**
* 模拟Segment的创建和搜索过程
*/
public void segmentLifecycle() {
// 1. 初始状态：空的Segment
List<Document> segment = new ArrayList<>();

// 2. 文档写入：添加到内存中的Segment
for (int i = 0; i < 100; i++) {
Document doc = createDocument(i);
segment.add(doc);

// 每个Segment有大小限制（默认1GB）
if (segment.size() >= 1000) { // 简化示例
// 3. Segment刷新：变得可搜索
refreshSegment(segment);

// 4. 新的空Segment准备接收数据
segment = new ArrayList<>();
}
}

// 5. 搜索时查询所有Segments
SearchResult result = searchAcrossSegments();
System.out.println("搜索到文档数: " + result.getTotalHits());
}

/**
* Segment合并策略
*/
public void segmentMergeStrategy() {
// Elasticsearch的Segment合并策略直接影响NRT性能
Map<String, Object> mergePolicy = new HashMap<>();

mergePolicy.put("策略类型", "TieredMergePolicy");
mergePolicy.put("每层最大Segments", 10);
mergePolicy.put("每层最小Segments", 2);
mergePolicy.put("最大合并Segment大小", "5GB");
mergePolicy.put("合并因子", 10);

// 合并的权衡：
// – 更多小Segments：更好的NRT性能，但搜索更慢
// – 更少大Segments：搜索更快，但NRT延迟增加

System.out.println("Segment合并策略配置:");
mergePolicy.forEach((k, v) ->
System.out.println(" " + k + ": " + v));
}
}

2.3 Refresh 操作：NRT 的关键机制

# Elasticsearch索引的Refresh配置
PUT /my_index/_settings
{
"index": {
# Refresh间隔：控制NRT的"近实时"程度
"refresh_interval": "1s", # 默认1秒，可调整

# Indexing Buffer：控制内存使用
"refresh_interval": "1s",
"translog": {
"flush_threshold_size": "512mb", # Translog大小阈值
"sync_interval": "5s", # 同步间隔
"durability": "request" # 持久化级别
},

# Segment相关设置
"merge": {
"policy": {
"max_merged_segment": "5gb",
"segments_per_tier": 10,
"max_merge_at_once": 10
}
},

# Indexing性能优化
"indexing": {
"slowlog": {
"threshold": {
"index": {
"warn": "10s",
"info": "5s",
"debug": "2s",
"trace": "500ms"
}
}
}
}
}
}

/**
* Refresh操作的工作原理
*/
public class RefreshMechanism {

/**
* 模拟Refresh过程
*/
public void simulateRefresh() {
// 1. 数据写入阶段
System.out.println("=== 数据写入阶段 ===");
List<Document> indexingBuffer = new ArrayList<>();

// 写入一些文档
for (int i = 1; i <= 5; i++) {
Document doc = new Document("doc_" + i, "content_" + i);
indexingBuffer.add(doc);
System.out.println("写入文档: " + doc.getId());
}

// 2. Refresh触发条件检查
System.out.println("\\n=== Refresh触发检查 ===");
boolean shouldRefresh = checkRefreshConditions(indexingBuffer);

if (shouldRefresh) {
// 3. 执行Refresh
executeRefresh(indexingBuffer);
}
}

private boolean checkRefreshConditions(List<Document> buffer) {
// Refresh触发的条件：
// 1. 时间条件：达到refresh_interval（默认1秒）
// 2. 缓冲区条件：达到一定大小
// 3. 手动触发：API调用

long currentTime = System.currentTimeMillis();
long lastRefreshTime = getLastRefreshTime();
long refreshInterval = 1000; // 1秒

// 检查时间间隔
if (currentTime – lastRefreshTime >= refreshInterval) {
System.out.println("时间条件满足：距离上次Refresh超过" + refreshInterval + "ms");
return true;
}

// 检查缓冲区大小
int bufferSizeThreshold = 1000; // 假设阈值1000条
if (buffer.size() >= bufferSizeThreshold) {
System.out.println("缓冲区条件满足：达到" + bufferSizeThreshold + "条");
return true;
}

return false;
}

private void executeRefresh(List<Document> buffer) {
System.out.println("\\n=== 执行Refresh操作 ===");

// 1. 创建新的Lucene Segment
System.out.println("1. 创建新的Segment");

// 2. 将内存中的文档写入Segment
System.out.println("2. 写入" + buffer.size() + "个文档到Segment");

// 3. 重新打开IndexReader（使新数据可搜索）
System.out.println("3. 重新打开IndexReader");

// 4. 清空Indexing Buffer（准备接收新数据）
buffer.clear();
System.out.println("4. 清空Indexing Buffer");

// 5. 更新Segment列表
System.out.println("5. 更新可搜索的Segment列表");

System.out.println("✓ Refresh完成，新数据现在可搜索");
}

/**
* 不同Refresh策略的影响
*/
public void compareRefreshStrategies() {
Map<String, Map<String, Object>> strategies = new HashMap<>();

// 策略1：频繁Refresh（最佳NRT）
strategies.put("频繁Refresh", Map.of(
"interval", "500ms",
"nrt_latency", "~500ms",
"throughput_impact", "高",
"recommended_for", "监控、日志分析"
));

// 策略2：默认Refresh（平衡模式）
strategies.put("默认Refresh", Map.of(
"interval", "1s",
"nrt_latency", "~1s",
"throughput_impact", "中",
"recommended_for", "电商、内容平台"
));

// 策略3：较少Refresh（最佳吞吐量）
strategies.put("较少Refresh", Map.of(
"interval", "30s",
"nrt_latency", "~30s",
"throughput_impact", "低",
"recommended_for", "批量数据处理"
));

// 策略4：关闭自动Refresh
strategies.put("手动Refresh", Map.of(
"interval", "-1",
"nrt_latency", "手动控制",
"throughput_impact", "最低",
"recommended_for", "数据迁移、批量索引"
));

System.out.println("不同Refresh策略对比:");
strategies.forEach((name, config) -> {
System.out.println("\\n" + name + ":");
config.forEach((k, v) ->
System.out.println(" " + k + ": " + v));
});
}
}

2.4 Translog：数据安全性的保障

/**
* Translog（事务日志）工作原理
* Translog保证了数据在刷新到磁盘前的安全性
*/
public class TranslogMechanism {

/**
* Translog的双重作用
*/
public void translogDualRole() {
// 角色1：崩溃恢复（Crash Recovery）
System.out.println("=== Translog的崩溃恢复机制 ===");

// 模拟崩溃场景
simulateCrashRecovery();

// 角色2：实时性保证
System.out.println("\\n=== Translog的实时性保证 ===");

// 即使没有Refresh，通过Translog也能提供实时Get操作
demonstrateRealTimeGet();
}

private void simulateCrashRecovery() {
// 场景：Elasticsearch节点突然崩溃
System.out.println("场景：节点突然崩溃，内存中的数据丢失");

// 恢复过程：
System.out.println("1. 重启节点");
System.out.println("2. 读取Translog（持久化在磁盘）");
System.out.println("3. 重放Translog中的操作");
System.out.println("4. 恢复到崩溃前的状态");
System.out.println("5. 继续正常服务");

// Translog配置选项：
System.out.println("\\nTranslog配置选项：");
System.out.println("- durability: request (每次请求都fsync，最安全)");
System.out.println("- durability: async (异步fsync，更高性能)");
System.out.println("- flush_threshold_size: 512mb (触发flush的大小)");
System.out.println("- sync_interval: 5s (同步到磁盘的间隔)");
}

private void demonstrateRealTimeGet() {
// 即使文档还没有被Refresh到Segment中
// 通过Translog仍然可以实时获取

System.out.println("文档写入流程：");
System.out.println("1. 文档写入 -> Indexing Buffer");
System.out.println("2. 同时写入 -> Translog");
System.out.println("3. 未Refresh前，文档在内存中");

System.out.println("\\n实时Get操作：");
System.out.println("1. 首先检查内存中的Indexing Buffer");
System.out.println("2. 然后检查Translog");
System.out.println("3. 最后检查已刷新的Segments");
System.out.println("✓ 确保总是能获取到最新数据");
}

/**
* Translog与性能的权衡
*/
public void translogPerformanceTradeoff() {
Map<String, TranslogConfig> configs = new HashMap<>();

// 配置1：最高安全性
configs.put("最高安全性", new TranslogConfig(
"request", // durability
100, // flush_threshold_ops
"100mb", // flush_threshold_size
"1s" // sync_interval
));

// 配置2：平衡模式（默认）
configs.put("平衡模式", new TranslogConfig(
"request",
50000,
"512mb",
"5s"
));

// 配置3：最高性能
configs.put("最高性能", new TranslogConfig(
"async",
100000,
"1gb",
"30s"
));

System.out.println("Translog配置性能影响:");
configs.forEach((name, config) -> {
System.out.println("\\n" + name + ":");
System.out.println(" 数据安全性: " +
(config.durability.equals("request") ? "高" : "中"));
System.out.println(" 写入性能: " +
(config.flushThresholdSize.equals("1gb") ? "高" : "中"));
System.out.println(" 适用场景: " +
getRecommendedScenario(config));
});
}

class TranslogConfig {
String durability;
int flushThresholdOps;
String flushThresholdSize;
String syncInterval;

TranslogConfig(String d, int ops, String size, String interval) {
this.durability = d;
this.flushThresholdOps = ops;
this.flushThresholdSize = size;
this.syncInterval = interval;
}
}
}

三、NRT 实战应用场景

3.1 场景一：电商商品搜索

/**
* 电商商品搜索NRT实现
* 需求：商品上架/下架/价格变更后立即可搜
*/
@Service
@Slf4j
public class EcommerceProductSearch {

@Autowired
private RestHighLevelClient esClient;

/**
* 商品索引配置（优化NRT）
*/
public void createProductIndex() throws IOException {
CreateIndexRequest request = new CreateIndexRequest("products");

// NRT优化配置
request.settings(Settings.builder()
.put("index.number_of_shards", 3)
.put("index.number_of_replicas", 1)
// NRT关键配置
.put("index.refresh_interval", "1s") // 1秒刷新
.put("index.translog.durability", "request")
.put("index.translog.sync_interval", "1s")
.put("index.translog.flush_threshold_size", "256mb")
// 索引性能优化
.put("index.merge.scheduler.max_merge_count", 3)
.put("index.merge.scheduler.max_thread_count", 2)
.put("index.unassigned.node_left.delayed_timeout", "5m")
);

// 商品映射
XContentBuilder mapping = JsonXContent.contentBuilder()
.startObject()
.startObject("properties")
.startObject("product_id")
.field("type", "keyword")
.endObject()
.startObject("title")
.field("type", "text")
.field("analyzer", "ik_max_word")
.field("search_analyzer", "ik_smart")
.endObject()
.startObject("price")
.field("type", "double")
.endObject()
.startObject("status")
.field("type", "integer") // 0:下架, 1:上架
.endObject()
.startObject("stock")
.field("type", "integer")
.endObject()
.startObject("update_time")
.field("type", "date")
.field("format", "epoch_millis")
.endObject()
// 用于实时过滤的热数据字段
.startObject("is_hot")
.field("type", "boolean")
.endObject()
.endObject()
.endObject();

request.mapping(mapping);

CreateIndexResponse response = esClient.indices()
.create(request, RequestOptions.DEFAULT);

log.info("商品索引创建完成: {}", response.isAcknowledged());
}

/**
* 商品上架 – NRT保证
*/
@Transactional
public boolean listProduct(ProductDTO product) throws IOException {
String productId = product.getId();

// 1. 更新数据库状态
productRepository.updateStatus(productId, ProductStatus.LISTED);

// 2. 同步到Elasticsearch
IndexRequest request = new IndexRequest("products")
.id(productId)
.source(JsonXContent.contentBuilder()
.startObject()
.field("product_id", productId)
.field("title", product.getTitle())
.field("description", product.getDescription())
.field("price", product.getPrice())
.field("status", 1) // 上架状态
.field("category", product.getCategory())
.field("brand", product.getBrand())
.field("stock", product.getStock())
.field("update_time", System.currentTimeMillis())
.field("is_hot", product.getIsHot())
.endObject()
)
// 设置Refresh策略
.setRefreshPolicy(WriteRequest.RefreshPolicy.WAIT_UNTIL);

IndexResponse response = esClient.index(request, RequestOptions.DEFAULT);

// 3. 验证可搜索性
if (response.getResult() == DocWriteResponse.Result.CREATED ||
response.getResult() == DocWriteResponse.Result.UPDATED) {

// 等待并验证可搜索
return verifySearchability(productId);
}

return false;
}

/**
* 验证NRT搜索能力
*/
private boolean verifySearchability(String productId) {
int maxRetries = 10; // 最大重试10次
int retryInterval = 200; // 每次间隔200ms

for (int i = 0; i < maxRetries; i++) {
try {
// 尝试搜索刚上架的商品
SearchRequest searchRequest = new SearchRequest("products");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.termQuery("product_id", productId));
searchRequest.source(sourceBuilder);

SearchResponse response = esClient.search(
searchRequest, RequestOptions.DEFAULT);

if (response.getHits().getTotalHits().value > 0) {
long latency = (i + 1) * retryInterval;
log.info("商品上架后 {}ms 可搜索: {}", latency, productId);
return true;
}

Thread.sleep(retryInterval);

} catch (Exception e) {
log.warn("验证搜索能力异常", e);
}
}

log.error("商品上架后 {}ms 仍不可搜索: {}",
maxRetries * retryInterval, productId);
return false;
}

/**
* 商品搜索接口（支持实时过滤）
*/
public SearchResult searchProducts(ProductSearchRequest searchReq)
throws IOException {

SearchRequest request = new SearchRequest("products");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

// 构建实时搜索查询
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();

// 关键词搜索
if (StringUtils.isNotBlank(searchReq.getKeyword())) {
boolQuery.must(QueryBuilders.multiMatchQuery(searchReq.getKeyword(),
"title", "description", "category", "brand"));
}

// 实时状态过滤：只显示上架商品
boolQuery.filter(QueryBuilders.termQuery("status", 1));

// 库存过滤：只显示有库存商品
if (searchReq.isInStockOnly()) {
boolQuery.filter(QueryBuilders.rangeQuery("stock").gt(0));
}

// 价格区间过滤
if (searchReq.getMinPrice() != null || searchReq.getMaxPrice() != null) {
RangeQueryBuilder priceRange = QueryBuilders.rangeQuery("price");
if (searchReq.getMinPrice() != null) {
priceRange.gte(searchReq.getMinPrice());
}
if (searchReq.getMaxPrice() != null) {
priceRange.lte(searchReq.getMaxPrice());
}
boolQuery.filter(priceRange);
}

// 热销商品优先（实时排序）
if (searchReq.isPrioritizeHot()) {
sourceBuilder.sort(SortBuilders.fieldSort("is_hot").order(SortOrder.DESC));
}

// 最新商品优先（基于更新时间）
sourceBuilder.sort(SortBuilders.fieldSort("update_time").order(SortOrder.DESC));

sourceBuilder.query(boolQuery);
sourceBuilder.from(searchReq.getFrom());
sourceBuilder.size(searchReq.getSize());

// 添加高亮显示
HighlightBuilder highlightBuilder = new HighlightBuilder();
highlightBuilder.field("title");
highlightBuilder.field("description");
highlightBuilder.preTags("<em>");
highlightBuilder.postTags("</em>");
sourceBuilder.highlighter(highlightBuilder);

// 添加聚合分析（实时统计）
TermsAggregationBuilder categoryAgg = AggregationBuilders
.terms("by_category")
.field("category.keyword");
sourceBuilder.aggregation(categoryAgg);

request.source(sourceBuilder);

// 执行搜索
SearchResponse response = esClient.search(request, RequestOptions.DEFAULT);

// 解析结果
return parseSearchResult(response);
}

/**
* 监控NRT性能指标
*/
@Scheduled(fixedDelay = 60000) // 每分钟执行
public void monitorNrtPerformance() throws IOException {
// 获取索引统计信息
IndicesStatsRequest statsRequest = new IndicesStatsRequest()
.indices("products")
.clear()
.refresh(true)
.flush(true);

IndicesStatsResponse stats = esClient.indices()
.stats(statsRequest, RequestOptions.DEFAULT);

// 分析Refresh性能
RefreshStats refreshStats = stats.getIndex("products").getRefresh();
FlushStats flushStats = stats.getIndex("products").getFlush();

Map<String, Object> metrics = new HashMap<>();
metrics.put("total_refreshes", refreshStats.getTotal());
metrics.put("refresh_time_ms", refreshStats.getTotalTimeInMillis());
metrics.put("avg_refresh_latency",
refreshStats.getTotalTimeInMillis() / Math.max(refreshStats.getTotal(), 1));
metrics.put("total_flushes", flushStats.getTotal());
metrics.put("flush_time_ms", flushStats.getTotalTimeInMillis());

// 计算NRT延迟指标
double nrtLatencyPercentile = calculateNrtLatencyPercentile();
metrics.put("nrt_latency_p95", nrtLatencyPercentile);

// 发送到监控系统
sendToMonitoringSystem("nrt_performance", metrics);

log.info("NRT性能监控: {}", metrics);
}
}

3.2 场景二：日志监控与分析

/**
* 日志监控系统的NRT实现
* 需求：日志产生后秒级可分析、可告警
*/
@Component
@Slf4j
public class LogMonitoringSystem {

@Autowired
private RestHighLevelClient esClient;

/**
* 日志索引模板（支持NRT）
*/
public void createLogIndexTemplate() throws IOException {
PutIndexTemplateRequest request = new PutIndexTemplateRequest("logs_template");

// 匹配所有logs-*索引
request.patterns(Collections.singletonList("logs-*"));

// NRT优化设置
request.settings(Settings.builder()
.put("index.number_of_shards", 5) // 更多分片提高并发
.put("index.number_of_replicas", 1)
.put("index.refresh_interval", "1s") // 1秒刷新
.put("index.translog.durability", "async") // 异步提高吞吐
.put("index.translog.sync_interval", "5s")
.put("index.translog.flush_threshold_size", "1gb") // 更大提高性能
// 优化写入性能
.put("index.indexing.slowlog.threshold.index.warn", "10s")
.put("index.indexing.slowlog.threshold.index.info", "5s")
.put("index.indexing.slowlog.threshold.index.debug", "2s")
// 优化查询性能
.put("index.queries.cache.enabled", true)
.put("index.max_result_window", 100000)
);

// 日志映射定义
XContentBuilder mapping = JsonXContent.contentBuilder()
.startObject()
.startObject("properties")
.startObject("@timestamp")
.field("type", "date")
.field("format", "epoch_millis")
.endObject()
.startObject("level")
.field("type", "keyword") // ERROR, WARN, INFO, DEBUG
.endObject()
.startObject("message")
.field("type", "text")
.field("analyzer", "standard")
.endObject()
.startObject("service")
.field("type", "keyword")
.endObject()
.startObject("host")
.field("type", "keyword")
.endObject()
.startObject("trace_id")
.field("type", "keyword")
.endObject()
// 动态字段：用户自定义字段
.startObject("fields")
.field("type", "object")
.field("dynamic", true)
.endObject()
.endObject()
.endObject();

request.mapping(mapping);

// 别名设置（方便搜索）
request.alias(new Alias("logs_current"));

esClient.indices().putTemplate(request, RequestOptions.DEFAULT);
log.info("日志索引模板创建完成");
}

/**
* 批量写入日志（优化NRT性能）
*/
public void bulkIndexLogs(List<LogEntry> logs) throws IOException {
if (logs == null || logs.isEmpty()) {
return;
}

BulkRequest bulkRequest = new BulkRequest();
String indexName = getDailyIndexName(); // 按天分索引

for (LogEntry logEntry : logs) {
IndexRequest request = new IndexRequest(indexName)
.source(JsonXContent.contentBuilder()
.startObject()
.field("@timestamp", logEntry.getTimestamp())
.field("level", logEntry.getLevel())
.field("message", logEntry.getMessage())
.field("service", logEntry.getService())
.field("host", logEntry.getHost())
.field("trace_id", logEntry.getTraceId())
.field("fields", logEntry.getFields())
.endObject()
);

bulkRequest.add(request);
}

// 设置Refresh策略（平衡性能与实时性）
bulkRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.NONE);

// 执行批量写入
BulkResponse bulkResponse = esClient.bulk(bulkRequest, RequestOptions.DEFAULT);

if (bulkResponse.hasFailures()) {
log.error("批量写入日志失败: {}", bulkResponse.buildFailureMessage());
} else {
log.debug("批量写入 {} 条日志完成，耗时: {}ms",
logs.size(), bulkResponse.getTook().getMillis());
}

// 定期手动Refresh（优化NRT）
schedulePeriodicRefresh(indexName);
}

/**
* 实时日志搜索
*/
public LogSearchResult searchLogs(LogSearchRequest searchReq) throws IOException {
SearchRequest request = new SearchRequest("logs_current"); // 使用别名

SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

// 构建时间范围查询（实时数据）
RangeQueryBuilder timeRange = QueryBuilders.rangeQuery("@timestamp")
.gte(searchReq.getStartTime())
.lte(searchReq.getEndTime());

BoolQueryBuilder boolQuery = QueryBuilders.boolQuery()
.must(timeRange);

// 日志级别过滤
if (searchReq.getLevels() != null && !searchReq.getLevels().isEmpty()) {
TermsQueryBuilder levelQuery = QueryBuilders
.termsQuery("level", searchReq.getLevels());
boolQuery.must(levelQuery);
}

// 服务名称过滤
if (StringUtils.isNotBlank(searchReq.getService())) {
boolQuery.must(QueryBuilders.termQuery("service", searchReq.getService()));
}

// 关键词搜索
if (StringUtils.isNotBlank(searchReq.getKeyword())) {
boolQuery.must(QueryBuilders.matchQuery("message", searchReq.getKeyword()));
}

sourceBuilder.query(boolQuery);
sourceBuilder.size(searchReq.getSize());

// 按时间倒序（最新日志在前）
sourceBuilder.sort("@timestamp", SortOrder.DESC);

// 添加聚合分析（实时统计）
if (searchReq.isEnableAggregation()) {
// 按日志级别聚合
TermsAggregationBuilder levelAgg = AggregationBuilders
.terms("log_levels")
.field("level")
.size(10);

// 按服务聚合
TermsAggregationBuilder serviceAgg = AggregationBuilders
.terms("services")
.field("service")
.size(20);

// 时间直方图（最近1小时）
DateHistogramAggregationBuilder timeAgg = AggregationBuilders
.dateHistogram("logs_over_time")
.field("@timestamp")
.calendarInterval(DateHistogramInterval.MINUTE)
.minDocCount(0);

sourceBuilder.aggregation(levelAgg);
sourceBuilder.aggregation(serviceAgg);
sourceBuilder.aggregation(timeAgg);
}

request.source(sourceBuilder);

// 执行搜索
SearchResponse response = esClient.search(request, RequestOptions.DEFAULT);

return parseLogSearchResult(response);
}

/**
* 实时告警系统
*/
@Scheduled(fixedDelay = 10000) // 每10秒执行
public void checkRealTimeAlerts() throws IOException {
long currentTime = System.currentTimeMillis();
long startTime = currentTime – 60000; // 最近1分钟

SearchRequest request = new SearchRequest("logs_current");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

// 查询最近1分钟的ERROR日志
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery()
.must(QueryBuilders.rangeQuery("@timestamp")
.gte(startTime)
.lte(currentTime))
.must(QueryBuilders.termQuery("level", "ERROR"));

// 按服务分组
TermsAggregationBuilder serviceAgg = AggregationBuilders
.terms("errors_by_service")
.field("service")
.size(50);

// 子聚合：错误数量
serviceAgg.subAggregation(AggregationBuilders.count("error_count"));

sourceBuilder.query(boolQuery);
sourceBuilder.aggregation(serviceAgg);
sourceBuilder.size(0); // 不需要返回具体文档

request.source(sourceBuilder);

SearchResponse response = esClient.search(request, RequestOptions.DEFAULT);

// 解析聚合结果，检查是否触发告警
Terms serviceTerms = response.getAggregations().get("errors_by_service");

for (Terms.Bucket bucket : serviceTerms.getBuckets()) {
String service = bucket.getKeyAsString();
long errorCount = ((ValueCount) bucket.getAggregations().get("error_count")).getValue();

// 检查告警规则
AlertRule rule = alertRuleService.getRuleForService(service);
if (rule != null && errorCount >= rule.getThreshold()) {
// 触发实时告警
triggerAlert(service, errorCount, rule);

log.warn("服务 {} 在最近1分钟内产生 {} 个ERROR日志，触发告警",
service, errorCount);
}
}
}

/**
* NRT性能监控面板
*/
public MonitoringDashboard getNrtDashboard() throws IOException {
MonitoringDashboard dashboard = new MonitoringDashboard();

// 1. 获取索引统计
IndicesStatsRequest statsRequest = new IndicesStatsRequest()
.indices("logs-*")
.clear()
.refresh(true);

IndicesStatsResponse stats = esClient.indices()
.stats(statsRequest, RequestOptions.DEFAULT);

// 2. 计算NRT指标
RefreshStats refreshStats = stats.getTotal().getRefresh();
dashboard.setRefreshCount(refreshStats.getTotal());
dashboard.setRefreshTime(refreshStats.getTotalTimeInMillis());
dashboard.setAvgRefreshTime(refreshStats.getTotalTimeInMillis() /
Math.max(refreshStats.getTotal(), 1));

// 3. 获取Segment信息
SegmentsStats segmentsStats = stats.getTotal().getSegments();
dashboard.setSegmentCount(segmentsStats.getCount());
dashboard.setMemoryUsed(segmentsStats.getMemoryInBytes());

// 4. 查询延迟统计（最近5分钟）
long fiveMinutesAgo = System.currentTimeMillis() – 300000;

SearchRequest latencyRequest = new SearchRequest("logs_current");
SearchSourceBuilder latencySource = new SearchSourceBuilder();

latencySource.query(QueryBuilders.rangeQuery("@timestamp")
.gte(fiveMinutesAgo));
latencySource.aggregation(AggregationBuilders
.percentiles("indexing_latency")
.field("indexing_latency") // 假设日志包含索引延迟字段
.percentiles(50, 95, 99));

latencyRequest.source(latencySource);

SearchResponse latencyResponse = esClient.search(
latencyRequest, RequestOptions.DEFAULT);

// 解析延迟百分位数
Percentiles percentiles = latencyResponse.getAggregations()
.get("indexing_latency");

dashboard.setP50Latency(percentiles.percentile(50));
dashboard.setP95Latency(percentiles.percentile(95));
dashboard.setP99Latency(percentiles.percentile(99));

return dashboard;
}
}

3.3 场景三：新闻资讯平台

/**
* 新闻资讯平台的NRT实现
* 需求：新闻发布后秒级可搜，支持实时热点排行
*/
@Service
@Slf4j
public class NewsPlatformNRT {

@Autowired
private RestHighLevelClient esClient;

/**
* 新闻索引配置（优化实时性）
*/
public void createNewsIndex() throws IOException {
CreateIndexRequest request = new CreateIndexRequest("news");

// 针对新闻发布的NRT优化配置
request.settings(Settings.builder()
.put("index.number_of_shards", 2) // 新闻数据量不大，分片少些
.put("index.number_of_replicas", 1)
// 关键NRT配置
.put("index.refresh_interval", "500ms") // 更短的刷新间隔
.put("index.translog.durability", "request") // 确保数据安全
.put("index.translog.flush_threshold_size", "128mb")
// 优化新闻搜索性能
.put("index.max_ngram_diff", 10)
.put("index.highlight.max_analyzed_offset", 1000000)
);

// 新闻映射定义
XContentBuilder mapping = JsonXContent.contentBuilder()
.startObject()
.startObject("properties")
.startObject("news_id")
.field("type", "keyword")
.endObject()
.startObject("title")
.field("type", "text")
.field("analyzer", "ik_max_word")
.field("search_analyzer", "ik_smart")
.startObject("fields")
.startObject("keyword")
.field("type", "keyword")
.field("ignore_above", 256)
.endObject()
.endObject()
.endObject()
.startObject("content")
.field("type", "text")
.field("analyzer", "ik_max_word")
.field("search_analyzer", "ik_smart")
.endObject()
.startObject("category")
.field("type", "keyword")
.endObject()
.startObject("tags")
.field("type", "keyword")
.endObject()
.startObject("publish_time")
.field("type", "date")
.field("format", "epoch_millis")
.endObject()
.startObject("update_time")
.field("type", "date")
.field("format", "epoch_millis")
.endObject()
// 实时互动数据
.startObject("view_count")
.field("type", "long")
.endObject()
.startObject("like_count")
.field("type", "long")
.endObject()
.startObject("comment_count")
.field("type", "long")
.endObject()
.startObject("share_count")
.field("type", "long")
.endObject()
// 实时热度分数（动态计算）
.startObject("hot_score")
.field("type", "double")
.endObject()
// 新闻状态（0:草稿, 1:已发布, 2:已删除）
.startObject("status")
.field("type", "integer")
.endObject()
.endObject()
.endObject();

request.mapping(mapping);

esClient.indices().create(request, RequestOptions.DEFAULT);
log.info("新闻索引创建完成");
}

/**
* 发布新闻（确保NRT可用性）
*/
@Transactional
public boolean publishNews(NewsDTO news) throws IOException {
String newsId = news.getNewsId();

// 1. 更新数据库状态
newsRepository.updateStatus(newsId, NewsStatus.PUBLISHED);

// 2. 同步到Elasticsearch（强制Refresh确保实时）
IndexRequest request = new IndexRequest("news")
.id(newsId)
.source(JsonXContent.contentBuilder()
.startObject()
.field("news_id", newsId)
.field("title", news.getTitle())
.field("content", news.getContent())
.field("summary", news.getSummary())
.field("author", news.getAuthor())
.field("category", news.getCategory())
.field("tags", news.getTags())
.field("cover_image", news.getCoverImage())
.field("publish_time", System.currentTimeMillis())
.field("update_time", System.currentTimeMillis())
.field("view_count", 0)
.field("like_count", 0)
.field("comment_count", 0)
.field("share_count", 0)
.field("hot_score", calculateInitialHotScore(news))
.field("status", 1) // 已发布
.endObject()
)
.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE); // 立即刷新

IndexResponse response = esClient.index(request, RequestOptions.DEFAULT);

// 3. 验证发布成功
if (response.getResult() == DocWriteResponse.Result.CREATED) {
log.info("新闻发布成功，news_id: {}", newsId);

// 4. 触发实时索引预热
preheatNewsIndex(newsId);

return true;
}

return false;
}

/**
* 新闻搜索（支持实时过滤和排序）
*/
public NewsSearchResult searchNews(NewsSearchRequest searchReq)
throws IOException {

SearchRequest request = new SearchRequest("news");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();

// 必须条件：只搜索已发布的新闻
boolQuery.filter(QueryBuilders.termQuery("status", 1));

// 关键词搜索（标题和内容）
if (StringUtils.isNotBlank(searchReq.getKeyword())) {
MultiMatchQueryBuilder multiMatch = QueryBuilders
.multiMatchQuery(searchReq.getKeyword())
.field("title", 3.0f) // 标题权重更高
.field("content", 1.0f)
.field("summary", 2.0f)
.type(MultiMatchQueryBuilder.Type.BEST_FIELDS);

boolQuery.must(multiMatch);
}

// 分类过滤
if (StringUtils.isNotBlank(searchReq.getCategory())) {
boolQuery.filter(QueryBuilders.termQuery("category",
searchReq.getCategory()));
}

// 标签过滤
if (searchReq.getTags() != null && !searchReq.getTags().isEmpty()) {
boolQuery.filter(QueryBuilders
.termsQuery("tags", searchReq.getTags()));
}

// 时间范围过滤
if (searchReq.getStartTime() != null || searchReq.getEndTime() != null) {
RangeQueryBuilder timeRange = QueryBuilders
.rangeQuery("publish_time");

if (searchReq.getStartTime() != null) {
timeRange.gte(searchReq.getStartTime());
}
if (searchReq.getEndTime() != null) {
timeRange.lte(searchReq.getEndTime());
}

boolQuery.filter(timeRange);
}

sourceBuilder.query(boolQuery);
sourceBuilder.from(searchReq.getFrom());
sourceBuilder.size(searchReq.getSize());

// 实时排序策略
configureRealtimeSorting(sourceBuilder, searchReq);

// 高亮显示
HighlightBuilder highlightBuilder = new HighlightBuilder();
highlightBuilder.field("title");
highlightBuilder.field("content");
highlightBuilder.preTags("<em>");
highlightBuilder.postTags("</em>");
highlightBuilder.fragmentSize(200);
highlightBuilder.numOfFragments(3);
sourceBuilder.highlighter(highlightBuilder);

// 实时聚合分析
if (searchReq.isEnableAggregation()) {
addRealtimeAggregations(sourceBuilder);
}

request.source(sourceBuilder);

// 执行搜索
SearchResponse response = esClient.search(request, RequestOptions.DEFAULT);

return parseNewsSearchResult(response);
}

/**
* 配置实时排序
*/
private void configureRealtimeSorting(
SearchSourceBuilder sourceBuilder,
NewsSearchRequest searchReq) {

String sortBy = searchReq.getSortBy();
SortOrder sortOrder = searchReq.getSortOrder();

if ("hot".equals(sortBy)) {
// 按实时热度排序
sourceBuilder.sort(SortBuilders
.fieldSort("hot_score")
.order(sortOrder != null ? sortOrder : SortOrder.DESC));

} else if ("time".equals(sortBy)) {
// 按发布时间排序
sourceBuilder.sort(SortBuilders
.fieldSort("publish_time")
.order(sortOrder != null ? sortOrder : SortOrder.DESC));

} else if ("view".equals(sortBy)) {
// 按实时浏览量排序
sourceBuilder.sort(SortBuilders
.fieldSort("view_count")
.order(sortOrder != null ? sortOrder : SortOrder.DESC));

} else {
// 默认：相关性 + 时间加权
ScriptSortBuilder scriptSort = SortBuilders.scriptSort(
new Script("_score * Math.log(2 + doc['view_count'].value)"),
ScriptSortBuilder.ScriptSortType.NUMBER
).order(SortOrder.DESC);

sourceBuilder.sort(scriptSort);
}
}

/**
* 实时热点新闻计算
*/
@Scheduled(fixedDelay = 30000) // 每30秒计算一次
public void calculateHotNews() throws IOException {
long currentTime = System.currentTimeMillis();
long timeWindow = 24 * 60 * 60 * 1000; // 24小时

// 查询最近24小时发布的新闻
SearchRequest request = new SearchRequest("news");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

BoolQueryBuilder boolQuery = QueryBuilders.boolQuery()
.filter(QueryBuilders.termQuery("status", 1))
.filter(QueryBuilders.rangeQuery("publish_time")
.gte(currentTime – timeWindow));

// 按互动数据排序
sourceBuilder.query(boolQuery);
sourceBuilder.size(1000); // 最多1000条

// 计算热度分数（基于浏览、点赞、评论、分享）
ScriptFieldsBuilder scriptFields = new ScriptFieldsBuilder();
scriptFields.addScriptField("calculated_hot_score",
new Script("""
// 热度计算公式
double timeFactor = 1.0 / (1.0 + (params.now – doc['publish_time'].value) / 3600000.0);
double viewScore = Math.log(1 + doc['view_count'].value) * 1.0;
double likeScore = doc['like_count'].value * 2.0;
double commentScore = doc['comment_count'].value * 3.0;
double shareScore = doc['share_count'].value * 5.0;

return timeFactor * (viewScore + likeScore + commentScore + shareScore);
""",
Map.of("now", currentTime)));

sourceBuilder.scriptFields(scriptFields);

request.source(sourceBuilder);

SearchResponse response = esClient.search(request, RequestOptions.DEFAULT);

// 批量更新热度分数
BulkRequest bulkRequest = new BulkRequest();

for (SearchHit hit : response.getHits().getHits()) {
String newsId = hit.getId();
double hotScore = (double) hit.getFields()
.get("calculated_hot_score").getValues().get(0);

UpdateRequest updateRequest = new UpdateRequest("news", newsId)
.doc(Map.of("hot_score", hotScore));

bulkRequest.add(updateRequest);
}

if (bulkRequest.numberOfActions() > 0) {
esClient.bulk(bulkRequest, RequestOptions.DEFAULT);
log.info("更新了 {} 条新闻的热度分数", bulkRequest.numberOfActions());
}
}

/**
* 新闻互动实时更新（浏览、点赞等）
*/
public void updateNewsInteraction(String newsId, String interactionType)
throws IOException {

// 使用Update By Query实现原子更新
UpdateByQueryRequest updateRequest = new UpdateByQueryRequest("news");

// 只更新指定的新闻
updateRequest.setQuery(QueryBuilders.termQuery("news_id", newsId));

// 定义更新脚本
Script script = new Script("""
// 原子增加计数
if (ctx._source.status == 1) { // 只更新已发布的新闻
if (params.type == 'view') {
ctx._source.view_count += 1;
} else if (params.type == 'like') {
ctx._source.like_count += 1;
} else if (params.type == 'comment') {
ctx._source.comment_count += 1;
} else if (params.type == 'share') {
ctx._source.share_count += 1;
}

// 实时重新计算热度分数（简化版）
double timeFactor = 1.0 / (1.0 + (params.now – ctx._source.publish_time) / 3600000.0);
double viewScore = Math.log(1 + ctx._source.view_count) * 1.0;
double likeScore = ctx._source.like_count * 2.0;
double commentScore = ctx._source.comment_count * 3.0;
double shareScore = ctx._source.share_count * 5.0;

ctx._source.hot_score = timeFactor * (viewScore + likeScore + commentScore + shareScore);
ctx._source.update_time = params.now;
}
""",
ScriptType.INLINE,
"painless",
Map.of("type", interactionType, "now", System.currentTimeMillis())
);

updateRequest.setScript(script);
updateRequest.setRefresh(true); // 立即刷新

// 执行更新
BulkByScrollResponse response = esClient.updateByQuery(
updateRequest, RequestOptions.DEFAULT);

log.debug("新闻互动更新完成，news_id: {}, type: {}, 更新文档数: {}",
newsId, interactionType, response.getUpdated());
}
}

四、NRT 性能优化策略

4.1 索引配置优化

# NRT优化配置模板
PUT /_template/nrt_optimized_template
{
"index_patterns": ["nrt-*"],
"settings": {
# 分片配置
"number_of_shards": 3,
"number_of_replicas": 1,

# NRT核心配置
"refresh_interval": "1s",
"translog": {
"durability": "async",
"sync_interval": "5s",
"flush_threshold_size": "512mb"
},

# 索引缓冲区
"indexing": {
"buffer": {
"size": "10%"
}
},

# Segment合并优化
"merge": {
"scheduler": {
"max_thread_count": 2,
"max_merge_count": 3
},
"policy": {
"max_merged_segment": "5gb",
"segments_per_tier": 10,
"max_merge_at_once": 10
}
},

# 查询缓存
"queries": {
"cache": {
"enabled": true
}
},

# 字段数据缓存
"fielddata": {
"cache": {
"size": "20%"
}
}
}
}

4.2 写入优化策略

/**
* NRT写入优化实践
*/
@Component
@Slf4j
public class NRTWriteOptimization {

/**
* 批量写入优化
*/
public void optimizedBulkWrite(List<Document> documents) throws IOException {
BulkRequest bulkRequest = new BulkRequest();

// 1. 分批处理（每批1000条）
int batchSize = 1000;
for (int i = 0; i < documents.size(); i += batchSize) {
int end = Math.min(i + batchSize, documents.size());
List<Document> batch = documents.subList(i, end);

// 2. 添加到批量请求
addToBulkRequest(bulkRequest, batch);

// 3. 执行批量写入
if (bulkRequest.numberOfActions() >= batchSize) {
executeBulkWithOptimization(bulkRequest);
bulkRequest = new BulkRequest(); // 重置
}
}

// 处理剩余文档
if (bulkRequest.numberOfActions() > 0) {
executeBulkWithOptimization(bulkRequest);
}
}

private void executeBulkWithOptimization(BulkRequest bulkRequest)
throws IOException {

// 优化配置
bulkRequest.timeout(TimeValue.timeValueMinutes(2));
bulkRequest.setRefreshPolicy(WriteRequest.RefreshPolicy.NONE);

// 使用管道提高吞吐
BulkResponse response = esClient.bulk(bulkRequest, RequestOptions.DEFAULT);

if (response.hasFailures()) {
handleBulkFailures(response);
}

// 定期手动刷新（优化NRT延迟）
scheduleManualRefresh();
}

/**
* 索引预热策略
*/
public void warmUpIndex(String indexName) throws IOException {
// 1. 执行一些典型查询，预热缓存
SearchRequest warmupRequest = new SearchRequest(indexName);
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();

// 执行不同类型的查询预热
sourceBuilder.query(QueryBuilders.matchAllQuery());
sourceBuilder.size(0); // 不返回文档

// 添加聚合预热
sourceBuilder.aggregation(AggregationBuilders
.terms("warmup_terms").field("category.keyword"));

warmupRequest.source(sourceBuilder);

// 2. 执行预热查询
esClient.search(warmupRequest, RequestOptions.DEFAULT);

// 3. 预热字段数据缓存
FieldStatsRequest fieldStatsRequest = new FieldStatsRequest()
.indices(indexName)
.fields("hot_score", "publish_time");

esClient.fieldStats(fieldStatsRequest, RequestOptions.DEFAULT);

log.info("索引预热完成: {}", indexName);
}
}

4.3 监控与调优

/**
* NRT性能监控与调优
*/
@Component
@Slf4j
public class NRTPerformanceMonitor {

@Autowired
private RestHighLevelClient esClient;

@Scheduled(fixedDelay = 30000) // 每30秒监控一次
public void monitorNrtPerformance() throws IOException {
// 1. 获取集群健康状态
ClusterHealthRequest healthRequest = new ClusterHealthRequest()
.timeout(TimeValue.timeValueSeconds(5));

ClusterHealthResponse healthResponse = esClient.cluster()
.health(healthRequest, RequestOptions.DEFAULT);

// 2. 获取索引统计
IndicesStatsRequest statsRequest = new IndicesStatsRequest()
.all();

IndicesStatsResponse statsResponse = esClient.indices()
.stats(statsRequest, RequestOptions.DEFAULT);

// 3. 分析NRT关键指标
NrtMetrics metrics = analyzeNrtMetrics(statsResponse);

// 4. 自动调优建议
List<TuningRecommendation> recommendations =
generateTuningRecommendations(metrics);

// 5. 发送告警（如果指标异常）
checkAndSendAlerts(metrics);

// 6. 记录监控数据
saveMonitoringData(metrics);
}

private NrtMetrics analyzeNrtMetrics(IndicesStatsResponse stats) {
NrtMetrics metrics = new NrtMetrics();

// 分析Refresh性能
RefreshStats refreshStats = stats.getTotal().getRefresh();
metrics.setAvgRefreshLatency(
refreshStats.getTotalTimeInMillis() /
Math.max(refreshStats.getTotal(), 1)
);
metrics.setRefreshRate(
refreshStats.getTotal() /
(refreshStats.getTotalTimeInMillis() / 1000.0)
);

// 分析Segment性能
SegmentsStats segmentsStats = stats.getTotal().getSegments();
metrics.setSegmentCount(segmentsStats.getCount());
metrics.setSegmentMemory(segmentsStats.getMemoryInBytes());

// 分析索引延迟
IndexingStats indexingStats = stats.getTotal().getIndexing();
metrics.setIndexingLatency(
indexingStats.getTotal().getIndexTimeInMillis() /
Math.max(indexingStats.getTotal().getIndexCount(), 1)
);

return metrics;
}

private List<TuningRecommendation> generateTuningRecommendations(
NrtMetrics metrics) {

List<TuningRecommendation> recommendations = new ArrayList<>();

// 1. Refresh间隔调优
if (metrics.getAvgRefreshLatency() > 500) {
recommendations.add(new TuningRecommendation(
"refresh_interval",
"增大refresh_interval到2s",
"当前平均刷新延迟过高: " + metrics.getAvgRefreshLatency() + "ms",
"PUT /_settings {\\"index.refresh_interval\\": \\"2s\\"}"
));
}

// 2. Segment合并建议
if (metrics.getSegmentCount() > 1000) {
recommendations.add(new TuningRecommendation(
"segment_merge",
"优化Segment合并策略",
"Segment数量过多: " + metrics.getSegmentCount(),
"调整merge.policy.segments_per_tier和max_merge_at_once"
));
}

// 3. 内存优化建议
long segmentMemoryGB = metrics.getSegmentMemory() / (1024 * 1024 * 1024);
if (segmentMemoryGB > 10) {
recommendations.add(new TuningRecommendation(
"memory_usage",
"增加堆内存或优化字段类型",
"Segment内存占用过高: " + segmentMemoryGB + "GB",
"考虑使用keyword代替text，或增加JVM堆内存"
));
}

return recommendations;
}
}

五、NRT 常见问题与解决方案

5.1 问题诊断清单

5.2 NRT 延迟分析工具

/**
* NRT延迟诊断工具
*/
@Component
@Slf4j
public class NrtLatencyAnalyzer {

/**
* 诊断NRT延迟问题
*/
public NrtDiagnosis diagnoseLatency(String indexName) throws IOException {
NrtDiagnosis diagnosis = new NrtDiagnosis();

// 1. 检查索引配置
diagnosis.setIndexSettings(getIndexSettings(indexName));

// 2. 分析当前状态
diagnosis.setCurrentState(analyzeCurrentState(indexName));

// 3. 收集性能指标
diagnosis.setPerformanceMetrics(collectPerformanceMetrics(indexName));

// 4. 生成诊断报告
diagnosis.setRecommendations(generateDiagnosticReport(diagnosis));

return diagnosis;
}

/**
* NRT性能测试工具
*/
public NrtBenchmarkResult runBenchmark(String indexName, int documentCount)
throws IOException, InterruptedException {

NrtBenchmarkResult result = new NrtBenchmarkResult();

// 1. 准备测试数据
List<Document> testDocuments = generateTestDocuments(documentCount);

// 2. 执行写入测试
long startWrite = System.currentTimeMillis();
bulkIndexDocuments(indexName, testDocuments);
long endWrite = System.currentTimeMillis();

result.setWriteLatency(endWrite – startWrite);
result.setWriteThroughput(documentCount / ((endWrite – startWrite) / 1000.0));

// 3. 测试NRT延迟（从写入到可搜索）
List<Long> nrtLatencies = new ArrayList<>();

for (Document doc : testDocuments.subList(0, Math.min(100, documentCount))) {
long writeTime = System.currentTimeMillis();

// 写入单个文档
indexDocument(indexName, doc);

// 测试搜索延迟
long searchLatency = measureSearchLatency(indexName, doc.getId());
nrtLatencies.add(searchLatency);

log.debug("文档 {} 的NRT延迟: {}ms", doc.getId(), searchLatency);
}

// 计算延迟统计
result.setAvgNrtLatency(calculateAverage(nrtLatencies));
result.setP95NrtLatency(calculatePercentile(nrtLatencies, 95));
result.setP99NrtLatency(calculatePercentile(nrtLatencies, 99));

// 4. 清理测试数据
cleanupTestData(indexName, testDocuments);

return result;
}

private long measureSearchLatency(String indexName, String docId)
throws IOException, InterruptedException {

long startTime = System.currentTimeMillis();
int maxWaitTime = 10000; // 最多等待10秒
int checkInterval = 100; // 每100ms检查一次

for (int i = 0; i < maxWaitTime / checkInterval; i++) {
// 尝试搜索文档
SearchRequest searchRequest = new SearchRequest(indexName);
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.termQuery("_id", docId));
sourceBuilder.size(1);