图解大数据 | Spark GraphFrames-基于图的数据分析挖掘

8,245 阅读5分钟

1.GraphFrames介绍

由Databricks、UC Berkeley以及MIT联合为Apache Spark开发了一款图处理类库,名为GraphFrames。该类库构建在DataFrame之上,既能利用DataFrame良好的扩展性和强大的性能,同时也为Scala、Java和Python提供了统一的图处理API。

1) Spark对图计算的支持

Spark从最开始的关系型数据查询,到图算法实现,到GraphFrames库可以完成图查询。

2) GraphFrames的优势

GraphFrames是类似于Spark的GraphX库,支持图处理。但GraphFrames建立在Spark DataFrame之上,具有以下重要的优势:

  • 支持Scala,Java 和Python AP:GraphFrames提供统一的三种编程语言APIs,而GraphX的所有算法支持Python和Java。

  • 方便、简单的图查询:GraphFrames允许用户使用Spark SQL和DataFrame的API查询。

  • 支持导出和导入图:GraphFrames支持DataFrame数据源,使得可以读取和写入多种格式的图,比如Parquet、JSON和CSV格式。

2.构建GraphFrames

以航班分析为例,我们需要构建GraphFrames:

  • ① 先把数据读取成DataFrame。
  • ② 再通过DataFrame查询,构建出点和边。
  • ③ 再通过点和边构建GraphFrames。

# Create Vertices (airports) and Edges (flights)
tripVertices=airports.withColumnRenamed("IATA","id").distinct()
tripEdges=departureDelays
   .select("tripid","delay","src","dst","city_dst","state_dst")

# This GraphFrame builds upon the vertices and edges based on our trips (flights)
tripGraph=GraphFrame(tripVertices, tripEdges)

3.简单query与数据分析

1) 查询机场个数和行程个数

# 查询机场个数和行程个数(查询节点和边的个数)
print("Airports:", tripGraph.vertices.count())
print("Trips:", tripGraph.edges.count())

2) 查询最长的航班延迟

# 查询最长延误时间(通过分组统计完成)
longestDelay = tripGraph.edges.groupby().max("delay")

3) 晚点与准点航班分析

# 晚点与准点航班分析(通过数据选择与过滤,进行边的分析)
print "On-time / Early Flights: %d" % tripGraph.edges.filter("delay <= 0").count()
print "Delayed Flights: %d" % tripGraph.edges.filter("delay > 0").count()

4)从旧金山出发的飞机中延迟最严重的航班

# 从旧金山出发的飞机中延迟最严重的航班(数据选择+边分析+分组统计)
tripGraph.edges.filter(“src = ‘SFO’ and delay > 0”).groupBy(“src”, “dst”).avg(“delay”).sort(desc(“avg(delay)”))

4.图中点与边相关计算

1) 图中度的分析

在航班案例中:入度:抵达本机场的航班数量;出度:从本机场出发的航班数量;度:连接数量。

display(tripGraph.degrees.sort(desc("degree")).limit(20))

2) 图中边的分析

边的分析,通常是对成对的数据进行统计分析的

import pyspark.sql.functions as func 
topTrips = tripGraph.edges.groupBy("src", "dst").agg(func.count("delay").alias("trips"))

5.图入度与出度相关应用

1) 入度出度对图进一步分析

通过入度和出度分析中转站:入度/出度≈1,中转站;入度/出度>1,出发站;入度/出度<1,抵达站。

# Calculate the inDeg (flights into the airport) and outDeg (flights leaving the airport) 
inDeg = tripGraph.inDegrees 
outDeg = tripGraph.outDegrees 

# Calculate the degreeRatio (inDeg/outDeg) 
degreeRatio = inDeg.join(outDeg, inDeg.id == outDeg.id).drop(outDeg.id).selectExpr("id", "double(inDegree)/double(outDegree) as degreeRatio").cache() 

# Join back to the `airports` DataFrame (instead of registering temp table as above) 
nonTransferAirports = degreeRatio.join(airports, degreeRatio.id == airports.IATA) \ 
    .selectExpr("id", "city", "degreeRatio").filter("degreeRatio < .9 or degreeRatio > 1.1") 

# List out the city airports which have abnormal degree ratios. 
display(nonTransferAirports)

# Join back to the `airports` DataFrame (instead of registering temp table as above) 
transferAirports = degreeRatio.join(airports, degreeRatio.id == airports.IATA) \ 
    .selectExpr("id", "city", "degreeRatio").filter("degreeRatio between 0.9 and 1.1") 

# List out the top 10 transfer city airports 
display(transferAirports.orderBy("degreeRatio").limit(10))

2) 广度优先搜索

通过广度优先搜索,可以对图中的两个点进行关联查询:比如我们查询从旧金山到布法罗,中间有一次中转的航班。

# Example 1: Direct Seattle to San Francisco 
filteredPaths = tripGraph.bfs(fromExpr = "id = 'SEA'", toExpr = "id = 'SFO'", maxPathLength = 1) 
display(filteredPaths) 

# Example 2: Direct San Francisco and Buffalo 
filteredPaths = tripGraph.bfs(fromExpr = "id = 'SFO'", toExpr = "id = 'BUF'", maxPathLength = 1) 
display(filteredPaths) 

# Example 2a: Flying from San Francisco to Buffalo 
filteredPaths = tripGraph.bfs(fromExpr = "id = 'SFO'", toExpr = "id = 'BUF'", maxPathLength = 2) 
display(filteredPaths)

6.Pagerank算法与相关应用

可以通过pagerank算法进行机场排序:每个机场都会作为始发站和终点站很多次,可以通过pagerank算法对其重要度进行排序。

# Determining Airport ranking of importance using `pageRank` 
ranks = tripGraph.pageRank(resetProbability=0.15, maxIter=5) 
display(ranks.vertices.orderBy(ranks.vertices.pagerank.desc()).limit(20))

7.参考资料

ShowMeAI相关文章推荐

ShowMeAI系列教程推荐