💖💖作者：计算机毕业设计小途 💙💙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BOSS直聘岗位招聘数据可视化分析系统介绍
《基于大数据的BOSS直聘岗位招聘数据可视化分析系统》是一套采用先进大数据技术栈构建的招聘数据智能分析平台，系统以Hadoop分布式存储框架和Spark大数据处理引擎为核心技术底座，通过HDFS实现海量招聘数据的可靠存储，利用Spark SQL进行高效的数据查询与分析处理，结合Python语言的Pandas和NumPy科学计算库实现复杂的数据挖掘算法。系统采用前后端分离的架构设计，后端基于Django框架提供RESTful API接口服务，前端运用Vue.js框架配合ElementUI组件库构建响应式用户界面，通过Echarts图表库实现丰富的数据可视化展示效果。系统核心功能涵盖城市招聘分析模块，深度挖掘不同城市的岗位分布特征与薪资水平；行业规模分析模块，全面展现各行业的招聘需求趋势与发展规模；技能要求分析模块，智能提取并统计岗位技能需求的分布情况；岗位画像分析模块，构建完整的岗位特征画像包括学历要求、经验需求、薪资范围等维度。系统还提供大屏可视化功能，以炫酷的数据大屏形式实时展示招聘市场的整体态势，为用户提供直观、全面、准确的招聘数据分析服务，所有数据处理过程充分利用大数据技术的并行计算优势，确保系统在处理大规模招聘数据时的高性能表现。
基于大数据的BOSS直聘岗位招聘数据可视化分析系统演示视频
基于大数据的BOSS直聘岗位招聘数据可视化分析系统演示图片
基于大数据的BOSS直聘岗位招聘数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("BOSSRecruitmentAnalysis").master("local[*]").getOrCreate()
def city_recruitment_analysis(request):
df = spark.read.option("header", "true").csv("hdfs://localhost:9000/boss_data/recruitment_data.csv")
df.createOrReplaceTempView("recruitment_data")
city_stats = spark.sql("""
SELECT city,
COUNT(*) as job_count,
AVG(CAST(salary_min as DOUBLE)) as avg_salary_min,
AVG(CAST(salary_max as DOUBLE)) as avg_salary_max,
COUNT(DISTINCT company_name) as company_count
FROM recruitment_data
WHERE city IS NOT NULL AND city != ''
GROUP BY city
ORDER BY job_count DESC
LIMIT 20
""")
city_demand_trend = spark.sql("""
SELECT city, publish_date,
COUNT(*) as daily_jobs,
LAG(COUNT(*)) OVER (PARTITION BY city ORDER BY publish_date) as prev_day_jobs
FROM recruitment_data
WHERE publish_date >= date_sub(current_date(), 30)
GROUP BY city, publish_date
ORDER BY city, publish_date
""")
salary_distribution = spark.sql("""
SELECT city,
CASE
WHEN CAST(salary_max as DOUBLE) < 8000 THEN '8k以下'
WHEN CAST(salary_max as DOUBLE) < 15000 THEN '8k-15k'
WHEN CAST(salary_max as DOUBLE) < 25000 THEN '15k-25k'
ELSE '25k以上'
END as salary_range,
COUNT(*) as count
FROM recruitment_data
WHERE city IN (SELECT city FROM (SELECT city, COUNT(*) as cnt FROM recruitment_data GROUP BY city ORDER BY cnt DESC LIMIT 10))
GROUP BY city, salary_range
""")
city_stats_pd = city_stats.toPandas()
city_demand_pd = city_demand_trend.toPandas()
salary_dist_pd = salary_distribution.toPandas()
result_data = {
'city_statistics': city_stats_pd.to_dict('records'),
'demand_trend': city_demand_pd.to_dict('records'),
'salary_distribution': salary_dist_pd.to_dict('records')
}
return JsonResponse(result_data, safe=False)
def industry_scale_analysis(request):
df = spark.read.option("header", "true").csv("hdfs://localhost:9000/boss_data/recruitment_data.csv")
df.createOrReplaceTempView("recruitment_data")
industry_scale = spark.sql("""
SELECT industry,
COUNT(*) as total_positions,
COUNT(DISTINCT company_name) as company_count,
AVG(CAST(salary_max as DOUBLE)) as avg_max_salary,
STDDEV(CAST(salary_max as DOUBLE)) as salary_std,
COUNT(*) * 1.0 / (SELECT COUNT(*) FROM recruitment_data) * 100 as market_share
FROM recruitment_data
WHERE industry IS NOT NULL AND industry != ''
GROUP BY industry
HAVING COUNT(*) >= 50
ORDER BY total_positions DESC
""")
industry_growth = spark.sql("""
SELECT industry,
YEAR(publish_date) as year,
MONTH(publish_date) as month,
COUNT(*) as monthly_jobs,
LAG(COUNT(*)) OVER (PARTITION BY industry ORDER BY YEAR(publish_date), MONTH(publish_date)) as prev_month_jobs
FROM recruitment_data
WHERE publish_date >= date_sub(current_date(), 365)
GROUP BY industry, YEAR(publish_date), MONTH(publish_date)
ORDER BY industry, year, month
""")
company_size_dist = spark.sql("""
SELECT industry, company_size,
COUNT(*) as position_count,
COUNT(DISTINCT company_name) as company_count
FROM recruitment_data
WHERE industry IS NOT NULL AND company_size IS NOT NULL
GROUP BY industry, company_size
""")
experience_requirements = spark.sql("""
SELECT industry,
CASE
WHEN experience_requirement LIKE '%不限%' OR experience_requirement = '' THEN '经验不限'
WHEN experience_requirement LIKE '%1-3%' OR experience_requirement LIKE '%1年%' OR experience_requirement LIKE '%2年%' THEN '1-3年'
WHEN experience_requirement LIKE '%3-5%' OR experience_requirement LIKE '%4年%' THEN '3-5年'
ELSE '5年以上'
END as exp_level,
COUNT(*) as count
FROM recruitment_data
WHERE industry IS NOT NULL
GROUP BY industry, exp_level
""")
industry_scale_pd = industry_scale.toPandas()
industry_growth_pd = industry_growth.toPandas()
company_size_pd = company_size_dist.toPandas()
experience_req_pd = experience_requirements.toPandas()
industry_growth_pd['growth_rate'] = ((industry_growth_pd['monthly_jobs'] - industry_growth_pd['prev_month_jobs']) / industry_growth_pd['prev_month_jobs'] * 100).fillna(0)
result_data = {
'industry_scale': industry_scale_pd.to_dict('records'),
'growth_trend': industry_growth_pd.to_dict('records'),
'company_size_distribution': company_size_pd.to_dict('records'),
'experience_requirements': experience_req_pd.to_dict('records')
}
return JsonResponse(result_data, safe=False)
def skill_requirement_analysis(request):
df = spark.read.option("header", "true").csv("hdfs://localhost:9000/boss_data/recruitment_data.csv")
df.createOrReplaceTempView("recruitment_data")
skill_extraction = spark.sql("""
SELECT job_title, skill_requirements, salary_max, industry, city
FROM recruitment_data
WHERE skill_requirements IS NOT NULL AND skill_requirements != ''
""")
skill_df = skill_extraction.withColumn("skills_array", split(col("skill_requirements"), "[,，、|]"))
skill_exploded = skill_df.select("*", explode("skills_array").alias("skill")).filter(col("skill") != "")
skill_cleaned = skill_exploded.withColumn("skill_clean", trim(regexp_replace(col("skill"), "[\\[\\]()（）]", "")))
skill_stats = skill_cleaned.groupBy("skill_clean").agg(
count("*").alias("demand_count"),
avg(col("salary_max").cast("double")).alias("avg_salary"),
countDistinct("industry").alias("industry_coverage"),
countDistinct("city").alias("city_coverage")
).filter(col("demand_count") >= 10).orderBy(desc("demand_count"))
skill_salary_relation = spark.sql("""
SELECT skill_clean,
CASE
WHEN CAST(salary_max as DOUBLE) < 10000 THEN '10k以下'
WHEN CAST(salary_max as DOUBLE) < 20000 THEN '10k-20k'
WHEN CAST(salary_max as DOUBLE) < 30000 THEN '20k-30k'
ELSE '30k以上'
END as salary_level,
COUNT(*) as count
FROM skill_exploded_view
GROUP BY skill_clean, salary_level
""")
skill_exploded.createOrReplaceTempView("skill_exploded_view")
skill_industry_matrix = skill_cleaned.groupBy("skill_clean", "industry").agg(
count("*").alias("skill_industry_count")
).filter(col("skill_industry_count") >= 5)
trending_skills = spark.sql("""
SELECT skill_clean,
YEAR(publish_date) as year,
MONTH(publish_date) as month,
COUNT(*) as monthly_demand,
LAG(COUNT(*)) OVER (PARTITION BY skill_clean ORDER BY YEAR(publish_date), MONTH(publish_date)) as prev_month_demand
FROM skill_exploded_view
WHERE publish_date >= date_sub(current_date(), 180)
GROUP BY skill_clean, YEAR(publish_date), MONTH(publish_date)
HAVING COUNT(*) >= 5
ORDER BY skill_clean, year, month
""")
skill_stats_pd = skill_stats.toPandas()
skill_salary_pd = skill_salary_relation.toPandas()
skill_industry_pd = skill_industry_matrix.toPandas()
trending_skills_pd = trending_skills.toPandas()
trending_skills_pd['growth_rate'] = ((trending_skills_pd['monthly_demand'] - trending_skills_pd['prev_month_demand']) / trending_skills_pd['prev_month_demand'] * 100).fillna(0)
hot_skills = skill_stats_pd.head(50)
result_data = {
'skill_statistics': hot_skills.to_dict('records'),
'skill_salary_distribution': skill_salary_pd.to_dict('records'),
'skill_industry_matrix': skill_industry_pd.to_dict('records'),
'trending_analysis': trending_skills_pd.to_dict('records')
}
return JsonResponse(result_data, safe=False)
基于大数据的BOSS直聘岗位招聘数据可视化分析系统文档展示
💖💖作者：计算机毕业设计小途 💙💙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
