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Elasticsearch赋能3D打印机任务统计分析

ops 2025/9/3 5:33:55

背景与挑战：打印任务统计的痛点

在3D打印机管理系统中，PrinterController负责提供各类打印任务统计接口，如打印时长分布、耗材用量趋势、任务成功率等关键指标。随着系统运行时间增长（假设已累计10万+打印任务），基于MySQL的传统统计方案逐渐面临严峻挑战：

  1. 统计查询性能瓶颈：复杂的GROUP BY和ORDER BY组合查询导致CPU利用率飙升，部分报表接口响应时间超过10秒
    如查看不同型号设备的月度打印时长TOP10
/* 耗时12.4s (10万条记录) */
SELECT p.device_model,AVG(j.print_duration)/60 AS avg_hours,SUM(j.print_duration)/3600 AS total_hours
FROM PrinterJobStatus j
JOIN Printer p ON j.printer_id = p.id
WHERE j.print_start_time BETWEEN '2025-07-01' AND '2025-07-31'
GROUP BY p.device_model
ORDER BY total_hours DESC
LIMIT 10;

• 需要扫描PrinterJobStatus全表（10万+行）

• 关联Printer表时反复索引查询

• 排序前需生成临时表存储所有分组结果

• 生产影响​​：报表页面卡死，无法快速定位高负荷设备
​
2. 实时性要求难以满足：管理员需要实时查看当前打印任务状态和趋势，但MySQL不适合高频聚合分析
大屏监控系统需 ​​实时刷新当前车间任务成功率​​（5秒轮询）

/* 耗时3.2s (每秒触发20+次) */
SELECT (SELECT COUNT(*) FROM PrinterJobStatus WHERE status=1 AND end_time>NOW()-INTERVAL 1 HOUR) * 100.0 /(SELECT COUNT(*) FROM PrinterJobStatus WHERE end_time>NOW()-INTERVAL 1 HOUR) AS success_rate;

• 每小时数据需扫描2万+条记录

• 高频触发导致MySQL QPS暴增（压测峰值CPU达85%）

• 生产影响​​：监控大屏数据延迟，故障响应滞后

  1. 数据关联复杂度高：统计分析往往需要关联Printer、PrinterJobStatus和PrinterStatistics等多张表
    统计各部门季度耗材成本​
/* 3表关联查询 耗时9.8s */
SELECT d.name AS department,SUM(s.material_used * m.unit_price) AS cost
FROM PrinterStatistics s
JOIN Printer p ON s.printer_id = p.id
JOIN Department d ON p.department_id = d.id
JOIN Material m ON s.material_id = m.id
WHERE s.quarter = '2025-Q2'
GROUP BY d.id;

• 需要4次索引跳转（printer_id→department_id→material_id）

• 中间结果集膨胀至15万行

• 生产影响​​：月末结算延迟，成本核算误差达5%
4. 历史数据分析能力不足：难以高效地进行跨月、跨季度的长期趋势分析
分析 ​​2022全年各月打印失败原因分布​

/* 跨12个月分区 耗时22.6s */
SELECT DATE_FORMAT(print_end_time,'%Y-%m') AS month,failure_reason,COUNT(*) AS count
FROM PrinterJobStatus
WHERE status=0 AND print_end_time BETWEEN '2022-01-01' AND '2022-12-31'
GROUP BY month, failure_reason;

• 需扫描全年12个分区（总计80万条记录）

• GROUP BY双字段分组产生大量中间数据

• 生产影响​​：无法快速识别喷头堵塞的季节性规律

技术方案：Elasticsearch的聚合分析优势

针对以上挑战，Elasticsearch凭借其强大的聚合分析能力提供了理想解决方案：
mermaid

数据模型设计：从关系型到文档型

1. 现有MySQL表结构分析

目前系统中与打印任务统计相关的核心表包括：

- *PrinterJobStatus*：记录打印任务的执行状态和关键指标

- *Printer*：存储打印机基本信息

- *PrinterStatistics*：记录打印机的统计数据

2. Elasticsearch索引设计

为支持高效统计分析，我们设计了名为print_job_history的索引，采用宽表设计模式，将多个相关表的字段整合到单一文档中：

{"mappings": {"properties": {"jobId": { "type": "keyword" },"deviceSn": { "type": "keyword" },"deviceName": { "type": "text", "fields": { "keyword": { "type": "keyword" } } },"deviceModel": { "type": "keyword" },"printStartTime": { "type": "date" },"printEndTime": { "type": "date" },"printDuration": { "type": "long" }, // 打印时长（秒）"materialUsage": { "type": "float" }, // 耗材用量（克）"jobStatus": { "type": "integer" }, // 0失败/1成功/2进行中"layerHeight": { "type": "float" },"nozzleTemp": { "type": "integer" },"bedTemp": { "type": "integer" },"materialType": { "type": "keyword" },"fileSize": { "type": "long" },"creator": { "type": "keyword" },"department": { "type": "keyword" },"successRate": { "type": "float" },"failureReason": { "type": "text", "analyzer": "ik_max_word" }}}}

系统架构与数据流程

整体架构图


┌─────────────┐    ┌─────────────┐    ┌─────────────┐│ 3D Printer  │─────>│ Spring Boot │─────>│  MySQL   │└─────────────┘    └──────┬──────┘    └─────────────┘​                   │​          ┌────────┴────────┐​          ▼                 ▼​     ┌─────────────┐   ┌─────────────┐​      │  Canal   │────>│ Elasticsearch│​      └─────────────┘   └──────┬──────┘​                    │​                ┌──────┴──────┐​                │  Kibana   │​                └─────────────┘

核心数据流程

  1. *实时数据同步*：

  • 通过AOP监听PrinterController中任务状态变更的方法

  • 任务完成时（状态变更为完成/失败），异步推送数据到Elasticsearch

  1. *历史数据迁移*：

  • 使用Canal监听MySQL的binlog，实现存量数据的批量同步

  • 设计分片策略，按时间（每月/每季度）创建索引分片

  1. *查询服务重构*：

  • 重构PrinterController中的统计接口，优先从ES获取数据

  • 对于ES中不存在的冷数据，设计自动回源MySQL的机制

核心功能实现：从代码层到业务层

完整案例

  1. 项目依赖 (pom.xml 示例)
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>2.7.18</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.example</groupId><artifactId>printer-analytics</artifactId><version>0.0.1-SNAPSHOT</version><name>printer-analytics</name><description>3D Printer Analytics with Elasticsearch</description><properties><java.version>17</java.version><elasticsearch.version>7.17.2</elasticsearch.version><spring-data-elasticsearch.version>4.4.0</spring-data-elasticsearch.version></properties><dependencies><!-- Spring Boot Web Starter --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><!-- Spring Data Elasticsearch Starter --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-elasticsearch</artifactId></dependency><!-- Elasticsearch High Level REST Client --><dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-high-level-client</artifactId><version>${elasticsearch.version}</version></dependency><!-- Elasticsearch Core --><dependency><groupId>org.elasticsearch</groupId><artifactId>elasticsearch</artifactId><version>${elasticsearch.version}</version></dependency><!-- Spring Boot Test Starter --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency><dependency><groupId>org.springframework.data</groupId><artifactId>spring-data-elasticsearch</artifactId><version>${spring-data-elasticsearch.version}</version></dependency></dependencies><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build>
</project>
  1. 配置文件 (application.properties)
# Elasticsearch Configuration
# 确保你的 ES 实例运行在 9200 端口
spring.elasticsearch.uris=http://localhost:9200
# 如果需要认证，添加用户名和密码
# spring.elasticsearch.username=your_username
# spring.elasticsearch.password=your_password# 应用端口
server.port=8080# 日志级别 (可选，用于调试)
logging.level.org.elasticsearch.client=DEBUG
  1. Elasticsearch 配置类
package com.grant.code.esdemo.config;import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;@Configuration
public class ElasticsearchConfig {@Value("${spring.elasticsearch.uris}")private String elasticsearchUri;@Bean(destroyMethod = "close")public RestHighLevelClient elasticsearchClient() {// 解析 URI (假设是 http://host:port 格式)String[] parts = elasticsearchUri.replace("http://", "").split(":");String hostname = parts[0];int port = Integer.parseInt(parts[1]);// 创建 RestHighLevelClient [[7]]return new RestHighLevelClient(RestClient.builder(new HttpHost(hostname, port, "http")));}
}
  1. 实体类 (映射 ES 文档)
package com.grant.code.esdemo.model;import org.springframework.data.annotation.Id;
import org.springframework.data.elasticsearch.annotations.*;
import java.util.Date;import java.util.Date;@Document(indexName = "print_job_history") // 指定索引名
public class PrintJobDocument {@Id // 标识文档IDprivate String jobId;@Field(type = FieldType.Keyword)private String deviceSn;@MultiField(mainField = @Field(type = FieldType.Text),otherFields = {@InnerField(suffix = "keyword", type = FieldType.Keyword)})private String deviceName;@Field(type = FieldType.Keyword)private String deviceModel;@Field(type = FieldType.Date, format = DateFormat.date_time) // 日期时间格式private Date printStartTime;@Field(type = FieldType.Date, format = DateFormat.date_time)private Date printEndTime;@Field(type = FieldType.Long) // 打印时长（秒）private Long printDuration;@Field(type = FieldType.Float) // 耗材用量（克）private Float materialUsage;@Field(type = FieldType.Integer) // 0失败/1成功/2进行中private Integer jobStatus;@Field(type = FieldType.Float)private Float layerHeight;@Field(type = FieldType.Integer)private Integer nozzleTemp;@Field(type = FieldType.Integer)private Integer bedTemp;@Field(type = FieldType.Keyword)private String materialType;@Field(type = FieldType.Long)private Long fileSize;@Field(type = FieldType.Keyword)private String creator;@Field(type = FieldType.Keyword)private String department;@Field(type = FieldType.Float)private Float successRate;@Field(type = FieldType.Text)private String failureReason;// Getters and Setterspublic String getJobId() { return jobId; }public void setJobId(String jobId) { this.jobId = jobId; }public String getDeviceSn() { return deviceSn; }public void setDeviceSn(String deviceSn) { this.deviceSn = deviceSn; }public String getDeviceName() { return deviceName; }public void setDeviceName(String deviceName) { this.deviceName = deviceName; }public String getDeviceModel() { return deviceModel; }public void setDeviceModel(String deviceModel) { this.deviceModel = deviceModel; }public Date getPrintStartTime() { return printStartTime; }public void setPrintStartTime(Date printStartTime) { this.printStartTime = printStartTime; }public Date getPrintEndTime() { return printEndTime; }public void setPrintEndTime(Date printEndTime) { this.printEndTime = printEndTime; }public Long getPrintDuration() { return printDuration; }public void setPrintDuration(Long printDuration) { this.printDuration = printDuration; }public Float getMaterialUsage() { return materialUsage; }public void setMaterialUsage(Float materialUsage) { this.materialUsage = materialUsage; }public Integer getJobStatus() { return jobStatus; }public void setJobStatus(Integer jobStatus) { this.jobStatus = jobStatus; }public Float getLayerHeight() { return layerHeight; }public void setLayerHeight(Float layerHeight) { this.layerHeight = layerHeight; }public Integer getNozzleTemp() { return nozzleTemp; }public void setNozzleTemp(Integer nozzleTemp) { this.nozzleTemp = nozzleTemp; }public Integer getBedTemp() { return bedTemp; }public void setBedTemp(Integer bedTemp) { this.bedTemp = bedTemp; }public String getMaterialType() { return materialType; }public void setMaterialType(String materialType) { this.materialType = materialType; }public Long getFileSize() { return fileSize; }public void setFileSize(Long fileSize) { this.fileSize = fileSize; }public String getCreator() { return creator; }public void setCreator(String creator) { this.creator = creator; }public String getDepartment() { return department; }public void setDepartment(String department) { this.department = department; }public Float getSuccessRate() { return successRate; }public void setSuccessRate(Float successRate) { this.successRate = successRate; }public String getFailureReason() { return failureReason; }public void setFailureReason(String failureReason) { this.failureReason = failureReason; }@Overridepublic String toString() {return "PrintJobDocument{" +"jobId='" + jobId + '\'' +", deviceSn='" + deviceSn + '\'' +", deviceName='" + deviceName + '\'' +", deviceModel='" + deviceModel + '\'' +", printStartTime=" + printStartTime +", printEndTime=" + printEndTime +", printDuration=" + printDuration +", materialUsage=" + materialUsage +", jobStatus=" + jobStatus +", layerHeight=" + layerHeight +", nozzleTemp=" + nozzleTemp +", bedTemp=" + bedTemp +", materialType='" + materialType + '\'' +", fileSize=" + fileSize +", creator='" + creator + '\'' +", department='" + department + '\'' +", successRate=" + successRate +", failureReason='" + failureReason + '\'' +'}';}
}
  1. Repository (数据访问层)
package com.example.printeranalytics.repository;import com.example.printeranalytics.model.PrintJobDocument;
import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
import org.springframework.stereotype.Repository;import java.util.List;// 使用 Spring Data Elasticsearch Repository [[31]]
@Repository
public interface PrintJobRepository extends ElasticsearchRepository<PrintJobDocument, String> {// Spring Data 自动根据方法名生成查询 [[38]]List<PrintJobDocument> findByDeviceModel(String deviceModel);// 可以添加更多自定义查询方法，例如基于注解 [[36]]// @Query("{\"bool\": {\"must\": [{\"match\": {\"jobStatus\": \"?0\"}}]}}")// List<PrintJobDocument> findByStatus(int status);
}
  1. Service (业务逻辑层)
package com.grant.code.esdemo.service;import com.grant.code.esdemo.repository.PrintJobRepository;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.BucketOrder;
import org.elasticsearch.search.aggregations.bucket.filter.FiltersAggregator;
import org.elasticsearch.search.aggregations.bucket.filter.ParsedFilters;
import org.elasticsearch.search.aggregations.bucket.terms.ParsedStringTerms;
import org.elasticsearch.search.aggregations.bucket.terms.Terms;
import org.elasticsearch.search.aggregations.bucket.terms.TermsAggregationBuilder;
import org.elasticsearch.search.aggregations.metrics.ParsedAvg;
import org.elasticsearch.search.aggregations.metrics.ParsedSum;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;import java.io.IOException;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.*;@Service
public class AnalyticsService {@Autowiredprivate PrintJobRepository printJobRepository; // 使用 Repository 进行基础操作@Autowiredprivate RestHighLevelClient elasticsearchClient; // 使用 High Level Client 进行复杂聚合查询 [[7]]/*** 获取指定月份内各设备型号的打印时长统计 (TOP 10)* 对应原文中的第一个挑战查询*/public List<Map<String, Object>> getDeviceModelPrintDurationStats(String yearMonth) throws IOException {// 构建日期范围 (假设 yearMonth 格式为 "yyyy-MM")DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM");LocalDateTime start = LocalDateTime.parse(yearMonth + "-01T00:00:00");LocalDateTime end = start.plusMonths(1);SearchRequest searchRequest = new SearchRequest("print_job_history"); // 指定索引名SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 添加日期范围和聚合查询searchSourceBuilder.query(QueryBuilders.rangeQuery("printStartTime").gte(start).lt(end).format("strict_date_optional_time||epoch_millis")); // 设置日期格式// 添加聚合查询TermsAggregationBuilder aggregation = AggregationBuilders.terms("by_device_model") // 聚合名称.field("deviceModel") // 聚合字段.subAggregation(AggregationBuilders.avg("avg_hours").field("printDuration")) // 平均时长.subAggregation(AggregationBuilders.sum("total_hours").field("printDuration")) // 总时长.size(10) // TOP 10.order(BucketOrder.aggregation("total_hours", false)); // 按总时长降序searchSourceBuilder.aggregation(aggregation);searchSourceBuilder.size(0); // 不返回文档内容，只返回聚合结果searchRequest.source(searchSourceBuilder); // 设置查询源SearchResponse searchResponse = elasticsearchClient.search(searchRequest, RequestOptions.DEFAULT);// 解析聚合结果List<Map<String, Object>> results = new ArrayList<>();ParsedStringTerms modelAgg = searchResponse.getAggregations().get("by_device_model"); // 根据聚合名称获取聚合结果for (Terms.Bucket bucket : modelAgg.getBuckets()) {Map<String, Object> result = new HashMap<>();result.put("deviceModel", bucket.getKeyAsString());// 处理可能为null的情况ParsedAvg avgHours = bucket.getAggregations().get("avg_hours");result.put("avgHours", avgHours != null ? avgHours.getValue() / 3600.0 : 0.0); // 转换为小时ParsedSum totalHours = bucket.getAggregations().get("total_hours");result.put("totalHours", totalHours != null ? totalHours.getValue() / 3600.0 : 0.0); // 转换为小时results.add(result);}return results;}/*** 获取过去一小时的任务成功率* 对应原文中的第二个挑战查询*/public double getRecentSuccessRate() throws IOException {LocalDateTime now = LocalDateTime.now();LocalDateTime oneHourAgo = now.minusHours(1);SearchRequest searchRequest = new SearchRequest("print_job_history");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 查询过去一小时结束的任务searchSourceBuilder.query(QueryBuilders.rangeQuery("printEndTime").gte(oneHourAgo).lte(now).format("strict_date_optional_time||epoch_millis"));// 使用单一聚合计算成功率searchSourceBuilder.aggregation(AggregationBuilders.filters("status_stats",new FiltersAggregator.KeyedFilter("success", QueryBuilders.termQuery("jobStatus", 1)),new FiltersAggregator.KeyedFilter("failed", QueryBuilders.termQuery("jobStatus", 0))));searchSourceBuilder.size(0); // 不返回文档内容searchRequest.source(searchSourceBuilder);SearchResponse searchResponse = elasticsearchClient.search(searchRequest, RequestOptions.DEFAULT);// 解析聚合结果ParsedFilters filters = searchResponse.getAggregations().get("status_stats");long successCount = filters.getBucketByKey("success").getDocCount();long totalCount = successCount + filters.getBucketByKey("failed").getDocCount();if (totalCount == 0) {return 0.0;}return (double) successCount / totalCount * 100;}// 可以添加更多统计方法...
}
  1. Controller (API 接口层)
package com.grant.code.esdemo.controller;import com.grant.code.esdemo.service.AnalyticsService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;import java.io.IOException;
import java.util.List;
import java.util.Map;@RestController
@RequestMapping("/api/analytics")
public class AnalyticsController {@Autowiredprivate AnalyticsService analyticsService;/*** 获取指定月份的打印设备型号统计* @param yearMonth* @return*/@GetMapping("/device-model-stats/{yearMonth}")public List<Map<String, Object>> getDeviceModelStats(@PathVariable String yearMonth) {try {return analyticsService.getDeviceModelPrintDurationStats(yearMonth);} catch (IOException e) {// 处理异常，例如返回 500 错误或记录日志e.printStackTrace();throw new RuntimeException("Failed to fetch device model stats", e);}}/*** 获取最近一个月的打印成功率* @return*/@GetMapping("/success-rate")public Map<String, Object> getSuccessRate() {try {double rate = analyticsService.getRecentSuccessRate();return Map.of("successRate", rate);} catch (IOException e) {// 处理异常e.printStackTrace();throw new RuntimeException("Failed to fetch success rate", e);}}// 添加更多 API 端点...
}
  1. 初始化索引和映射
package com.grant.code.esdemo.config;import com.fasterxml.jackson.databind.ObjectMapper;
import org.elasticsearch.action.admin.indices.create.CreateIndexRequest;
import org.elasticsearch.action.admin.indices.create.CreateIndexResponse;
import org.elasticsearch.action.admin.indices.get.GetIndexRequest;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.xcontent.XContentType;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.context.event.ApplicationReadyEvent;
import org.springframework.context.event.EventListener;
import org.springframework.core.io.ClassPathResource;
import org.springframework.stereotype.Component;import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.concurrent.TimeUnit;@Component
public class IndexInitializer {private static final String INDEX_NAME = "print_job_history";private static final String MAPPING_FILE = "mapping/print_job_history_mapping.json";@Autowiredprivate RestHighLevelClient elasticsearchClient;@Autowiredprivate ObjectMapper objectMapper;@EventListener(ApplicationReadyEvent.class)public void initIndices() {try {// 添加重试机制int retryCount = 0;while (retryCount < 3) {try {if (!indexExists(INDEX_NAME)) {createIndexWithMapping(INDEX_NAME, MAPPING_FILE);} else {System.out.println("索引 " + INDEX_NAME + " 已存在");}break; // 成功则退出循环} catch (IOException e) {retryCount++;if (retryCount >= 3) {throw e; // 重试3次后抛出异常}System.err.println("索引初始化失败，正在重试 (" + retryCount + "/3)...");TimeUnit.SECONDS.sleep(2); // 等待2秒后重试}}} catch (IOException | InterruptedException e) {System.err.println("### 索引初始化严重错误 ###");e.printStackTrace();// 这里可以添加通知逻辑，如发送邮件或短信}}private boolean indexExists(String indexName) throws IOException {// 正确创建 GetIndexRequest 并指定索引名称GetIndexRequest request = new GetIndexRequest().indices(indexName);return elasticsearchClient.indices().exists(request, RequestOptions.DEFAULT);}private void createIndexWithMapping(String indexName, String mappingFilePath) throws IOException {// 1. 创建索引请求CreateIndexRequest request = new CreateIndexRequest(indexName);// 2. 设置索引设置（可选）request.settings(Settings.builder().put("index.number_of_shards", 3).put("index.number_of_replicas", 1).put("index.refresh_interval", "1s").build());// 3. 加载映射文件ClassPathResource resource = new ClassPathResource(mappingFilePath);if (!resource.exists()) {throw new IOException("映射文件未找到: " + mappingFilePath);}String mappingSource = new String(Files.readAllBytes(Paths.get(resource.getURI())), StandardCharsets.UTF_8);request.mapping(mappingSource, XContentType.JSON);// 4. 创建索引CreateIndexResponse createIndexResponse = elasticsearchClient.indices().create(request, RequestOptions.DEFAULT);// 5. 处理响应if (createIndexResponse.isAcknowledged()) {System.out.println("索引 " + indexName + " 创建成功");} else {System.err.println("索引创建请求未被确认");System.err.println("响应信息: " + createIndexResponse);}}
}

对应的 src/main/resources/mapping/print_job_history_mapping.json 文件:

{"mappings": {"properties": {"jobId": { "type": "keyword" },"deviceSn": { "type": "keyword" },"deviceName": { "type": "text", "fields": { "keyword": { "type": "keyword" } } },"deviceModel": { "type": "keyword" },"printStartTime": { "type": "date", "format": "strict_date_optional_time||epoch_millis" },"printEndTime": { "type": "date", "format": "strict_date_optional_time||epoch_millis" },"printDuration": { "type": "long" },"materialUsage": { "type": "float" },"jobStatus": { "type": "integer" },"layerHeight": { "type": "float" },"nozzleTemp": { "type": "integer" },"bedTemp": { "type": "integer" },"materialType": { "type": "keyword" },"fileSize": { "type": "long" },"creator": { "type": "keyword" },"department": { "type": "keyword" },"successRate": { "type": "float" },"failureReason": { "type": "text" }}}
}

  1. 启动程序
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  2. 测试端点
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生成大量模拟数据

使用 Java Faker 库
添加依赖 (pom.xml)

<!-- Java Faker for generating fake data -->
<dependency><groupId>com.github.javafaker</groupId><artifactId>javafaker</artifactId><version>1.0.2</version> <!-- 使用较新版本 -->
</dependency>

创建数据生成服务

package com.grant.code.esdemo.controller;import com.grant.code.esdemo.service.DataGenerationService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;@RestController
@RequestMapping("/api/data")
public class DataGenerationController {@Autowiredprivate DataGenerationService dataGenerationService;@PostMapping("/generate")public String generateData(@RequestParam(defaultValue = "10000") int count) {try {dataGenerationService.generateAndSaveData(count);return "Successfully generated and saved " + count + " data entries.";} catch (Exception e) {e.printStackTrace();return "Error generating data: " + e.getMessage();}}
}

创建 Controller 端点触发数据生成

package com.grant.code.esdemo.controller;import com.grant.code.esdemo.service.DataGenerationService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;@RestController
@RequestMapping("/api/data")
public class DataGenerationController {@Autowiredprivate DataGenerationService dataGenerationService;@PostMapping("/generate")public String generateData(@RequestParam(defaultValue = "10000") int count) {try {dataGenerationService.generateAndSaveData(count);return "Successfully generated and saved " + count + " data entries.";} catch (Exception e) {e.printStackTrace();return "Error generating data: " + e.getMessage();}}
}

通过postman发送请求，生成50000条测试数据
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观察控制台输出
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查看kibana
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测试

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测试查询 1：设备型号月度打印时长 TOP 10
假设查询 2023 年 10 月的数据

POST /print_job_history/_search
{"size": 0,"query": {"range": {"printStartTime": {"gte": "2023-10-01T00:00:00","lt": "2023-11-01T00:00:00","format": "strict_date_optional_time"}}},"aggs": {"by_device_model": {"terms": {"field": "deviceModel","size": 10,"order": { "total_hours": "desc" }},"aggs": {"avg_hours": {"avg": {"field": "printDuration"}},"total_hours": {"sum": {"field": "printDuration"}},"total_hours_in_hours": {"bucket_script": {"buckets_path": {"totalSeconds": "total_hours"},"script": "params.totalSeconds / 3600"}},"avg_hours_in_hours": {"bucket_script": {"buckets_path": {"avgSeconds": "avg_hours"},"script": "params.avgSeconds / 3600"}}}}}
}

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查看返回结果的 aggregations 部分，确认是否得到了预期的设备型号及其总时长和平均时长。特别注意 took 字段，它表示 ES 执行查询所花费的时间（毫秒），应该远小于原始 MySQL 的 12.4 秒。 这里5万条数据仅需 154毫秒！！！！

测试查询 2：近期任务成功率
查询过去一小时的成功率。
这里直接测试接口
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可以发现速度还是飞快的！！！
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总结与展望

通过引入Elasticsearch，3D打印机管理系统的打印任务统计分析功能实现了质的飞跃。不仅解决了传统MySQL方案的性能瓶颈，还为系统带来了更丰富的数据分析能力和更好的用户体验。

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