将 GraphFrames ShortestPath Map 转换为 PySpark 中的 DataFrame 行

Question

我正在尝试找到最有效的方法来从 GraphFrames 函数 shortestPaths 获取 Map 输出并将每个顶点的距离映射展平到新 DataFrame 中的单独行中。通过将距离列拉入字典，然后从那里转换为 pandas 数据帧，然后再转换回 Spark 数据帧，我已经能够非常笨拙地做到这一点，但我知道必须有更好的方法。

from graphframes import *

v = sqlContext.createDataFrame([
  ("a", "Alice", 34),
  ("b", "Bob", 36),
  ("c", "Charlie", 30),
], ["id", "name", "age"])

# Create an Edge DataFrame with "src" and "dst" columns
e = sqlContext.createDataFrame([
  ("a", "b", "friend"),
  ("b", "c", "follow"),
  ("c", "b", "follow"),
], ["src", "dst", "relationship"])

# Create a GraphFrame
g = GraphFrame(v, e)

results = g.shortestPaths(landmarks=["a", "b","c"])
results.select("id","distances").show()

+---+--------------------+
| id|           distances|
+---+--------------------+
|  a|Map(a -> 0, b -> ...|
|  b| Map(b -> 0, c -> 1)|
|  c| Map(c -> 0, b -> 1)|
+---+--------------------+

我想要的是采用上面的输出并拉平距离，同时将 id 保持为如下所示：

+---+---+---------+      
| id| v | distance|
+---+---+---------+
|  a| a | 0       |
|  a| b | 1       |
|  a| c | 2       |
|  b| b | 0       |
|  b| c | 1       |
|  c| c | 0       |
|  c| b | 1       |
+---+---+---------+

谢谢。

Answer 1

你可以爆炸：

>>> from pyspark.sql.functions import explode
>>> results.select("id", explode("distances"))

将 GraphFrames ShortestPath Map 转换为 PySpark 中的 DataFrame 行

Convert GraphFrames ShortestPath Map into DataFrame rows in PySpark

python

apache-spark

pyspark

spark-dataframe

graphframes