💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于大数据的计算机岗位招聘数据可视化分析系统介绍
本系统是一个基于大数据技术的计算机岗位招聘数据可视化分析平台，采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术框架，通过HDFS分布式文件系统存储海量招聘数据，利用Spark SQL进行高效的数据查询和分析处理。系统后端采用Django框架和Spring Boot微服务架构双重支持，结合Python的Pandas、NumPy等数据分析库以及Java的强大处理能力，实现对招聘数据的深度挖掘和智能分析。前端采用Vue.js响应式框架配合ElementUI组件库构建现代化的用户界面，通过Echarts可视化图表库将复杂的数据分析结果以直观的图表形式展现给用户。系统主要功能模块包括系统首页展示、用户个人信息管理、系统管理后台、大屏可视化展示以及多维度的数据分析功能。其中，岗位维度分析模块能够对不同类型的计算机岗位进行分类统计和趋势分析，薪资经验分析模块通过对薪资水平与工作经验的关联性分析帮助用户了解薪酬结构，地域公司分析模块则从地理位置和企业类型角度提供招聘市场的分布情况。整个系统基于MySQL数据库进行数据持久化存储，通过大数据技术栈的综合运用，为用户提供全方位、多角度的计算机岗位招聘市场分析服务，实现了从数据采集、处理、分析到可视化展示的完整技术链条。
基于大数据的计算机岗位招聘数据可视化分析系统演示视频
基于大数据的计算机岗位招聘数据可视化分析系统演示图片
基于大数据的计算机岗位招聘数据可视化分析系统代码展示
spark = SparkSession.builder.appName("JobAnalysisSystem").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/job_analysis").option("dbtable", "recruitment_data").option("user", "root").option("password", "password").load()
def analyze_job_positions(request):
job_data = df.select("position_name", "company_name", "salary_range", "experience_required", "location").cache()
position_counts = job_data.groupBy("position_name").count().orderBy(col("count").desc())
position_result = position_counts.collect()
position_distribution = {}
total_jobs = job_data.count()
for row in position_result:
position_name = row["position_name"]
job_count = row["count"]
percentage = round((job_count / total_jobs) * 100, 2)
position_distribution[position_name] = {"count": job_count, "percentage": percentage}
tech_keywords = ["Java", "Python", "前端", "算法", "大数据", "人工智能", "测试", "运维"]
tech_stats = {}
for keyword in tech_keywords:
keyword_jobs = job_data.filter(col("position_name").contains(keyword))
keyword_count = keyword_jobs.count()
avg_salary = keyword_jobs.agg({"salary_range": "avg"}).collect()[0][0] if keyword_count > 0 else 0
tech_stats[keyword] = {"count": keyword_count, "avg_salary": round(avg_salary, 2) if avg_salary else 0}
experience_levels = job_data.groupBy("experience_required").count().orderBy("experience_required")
exp_analysis = []
for row in experience_levels.collect():
exp_level = row["experience_required"]
exp_count = row["count"]
exp_jobs = job_data.filter(col("experience_required") == exp_level)
popular_positions = exp_jobs.groupBy("position_name").count().orderBy(col("count").desc()).limit(5)
top_positions = [pos_row["position_name"] for pos_row in popular_positions.collect()]
exp_analysis.append({"level": exp_level, "count": exp_count, "top_positions": top_positions})
return JsonResponse({"position_distribution": position_distribution, "tech_stats": tech_stats, "experience_analysis": exp_analysis})
def analyze_salary_experience(request):
salary_exp_data = df.select("salary_range", "experience_required", "position_name", "company_size", "location").filter(col("salary_range").isNotNull() & col("experience_required").isNotNull())
salary_ranges = ["3-5K", "5-10K", "10-15K", "15-20K", "20-30K", "30K以上"]
experience_ranges = ["应届生", "1-3年", "3-5年", "5-10年", "10年以上"]
salary_exp_matrix = {}
for salary_range in salary_ranges:
salary_exp_matrix[salary_range] = {}
for exp_range in experience_ranges:
count = salary_exp_data.filter((col("salary_range") == salary_range) & (col("experience_required") == exp_range)).count()
salary_exp_matrix[salary_range][exp_range] = count
avg_salary_by_exp = salary_exp_data.groupBy("experience_required").agg(avg("salary_range").alias("avg_salary")).orderBy("experience_required")
exp_salary_trend = []
for row in avg_salary_by_exp.collect():
exp_level = row["experience_required"]
avg_sal = round(row["avg_salary"], 2) if row["avg_salary"] else 0
exp_count = salary_exp_data.filter(col("experience_required") == exp_level).count()
exp_salary_trend.append({"experience": exp_level, "avg_salary": avg_sal, "job_count": exp_count})
high_salary_positions = salary_exp_data.filter(col("salary_range").isin(["20-30K", "30K以上"])).groupBy("position_name").count().orderBy(col("count").desc()).limit(10)
high_pay_jobs = []
for row in high_salary_positions.collect():
position = row["position_name"]
count = row["count"]
pos_data = salary_exp_data.filter(col("position_name") == position)
common_exp = pos_data.groupBy("experience_required").count().orderBy(col("count").desc()).first()["experience_required"]
high_pay_jobs.append({"position": position, "count": count, "common_experience": common_exp})
company_size_salary = salary_exp_data.groupBy("company_size").agg(avg("salary_range").alias("avg_salary"), count("*").alias("job_count")).orderBy(col("avg_salary").desc())
size_salary_analysis = []
for row in company_size_salary.collect():
company_size = row["company_size"]
avg_salary = round(row["avg_salary"], 2) if row["avg_salary"] else 0
job_count = row["job_count"]
size_salary_analysis.append({"size": company_size, "avg_salary": avg_salary, "job_count": job_count})
return JsonResponse({"salary_experience_matrix": salary_exp_matrix, "experience_trend": exp_salary_trend, "high_salary_positions": high_pay_jobs, "company_size_analysis": size_salary_analysis})
def analyze_location_company(request):
location_data = df.select("location", "company_name", "company_size", "company_type", "salary_range", "position_name").filter(col("location").isNotNull() & col("company_name").isNotNull())
city_job_distribution = location_data.groupBy("location").count().orderBy(col("count").desc())
city_analysis = []
for row in city_job_distribution.collect():
city = row["location"]
job_count = row["count"]
city_data = location_data.filter(col("location") == city)
avg_salary = city_data.agg(avg("salary_range").alias("avg_sal")).collect()[0]["avg_sal"]
avg_salary = round(avg_salary, 2) if avg_salary else 0
top_companies = city_data.groupBy("company_name").count().orderBy(col("count").desc()).limit(5)
company_list = [comp_row["company_name"] for comp_row in top_companies.collect()]
popular_positions = city_data.groupBy("position_name").count().orderBy(col("count").desc()).limit(3)
position_list = [pos_row["position_name"] for pos_row in popular_positions.collect()]
city_analysis.append({"city": city, "job_count": job_count, "avg_salary": avg_salary, "top_companies": company_list, "popular_positions": position_list})
company_type_analysis = location_data.groupBy("company_type").agg(count("*").alias("job_count"), avg("salary_range").alias("avg_salary")).orderBy(col("job_count").desc())
type_stats = []
for row in company_type_analysis.collect():
comp_type = row["company_type"]
job_count = row["job_count"]
avg_salary = round(row["avg_salary"], 2) if row["avg_salary"] else 0
type_companies = location_data.filter(col("company_type") == comp_type).select("company_name").distinct().count()
type_stats.append({"type": comp_type, "job_count": job_count, "avg_salary": avg_salary, "company_count": type_companies})
company_size_distribution = location_data.groupBy("company_size", "location").count().orderBy("location", col("count").desc())
size_location_map = {}
for row in company_size_distribution.collect():
size = row["company_size"]
location = row["location"]
count = row["count"]
if location not in size_location_map:
size_location_map[location] = {}
size_location_map[location][size] = count
top_companies_overall = location_data.groupBy("company_name").agg(count("*").alias("total_jobs"), avg("salary_range").alias("avg_offer")).orderBy(col("total_jobs").desc()).limit(20)
company_rankings = []
for row in top_companies_overall.collect():
company = row["company_name"]
total_jobs = row["total_jobs"]
avg_offer = round(row["avg_offer"], 2) if row["avg_offer"] else 0
company_locations = location_data.filter(col("company_name") == company).select("location").distinct().count()
company_rankings.append({"company": company, "total_jobs": total_jobs, "avg_salary": avg_offer, "locations": company_locations})
return JsonResponse({"city_analysis": city_analysis, "company_type_stats": type_stats, "size_location_distribution": size_location_map, "top_companies": company_rankings})
基于大数据的计算机岗位招聘数据可视化分析系统文档展示
💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
