graphframes · WeichenXu123 · Feb 15, 2023 · Feb 10, 2023 · Feb 10, 2023
diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml
@@ -16,9 +16,6 @@ jobs:
           - spark-version: 3.0.3
             scala-version: 2.12.12
             python-version: 3.8
-          - spark-version: 2.4.8
-            scala-version: 2.11.12
-            python-version: 3.7
     runs-on: ubuntu-20.04
     env:
       # define Java options for both official sbt and sbt-extras

diff --git a/.github/workflows/scala-ci.yml b/.github/workflows/scala-ci.yml
@@ -14,8 +14,6 @@ jobs:
             scala-version: 2.12.12
           - spark-version: 3.0.3
             scala-version: 2.12.12
-          - spark-version: 2.4.8
-            scala-version: 2.11.12
     runs-on: ubuntu-20.04
     env:
       # define Java options for both official sbt and sbt-extras

diff --git a/build.sbt b/build.sbt
@@ -12,7 +12,6 @@ val defaultScalaVer = sparkBranch match {
   case "3.2" => "2.12.15"
   case "3.1" => "2.12.15"
   case "3.0" => "2.12.15"
-  case "2.4" => "2.11.12"
   case _ => throw new IllegalArgumentException(s"Unsupported Spark version: $sparkVer.")
 }
 val scalaVer = sys.props.getOrElse("scala.version", defaultScalaVer)

diff --git a/dev/release.py b/dev/release.py
@@ -39,7 +39,7 @@ def verify(prompt, interactive):
 @click.option("--publish-docs", type=bool, default=PUBLISH_DOCS_DEFAULT, show_default=True,
               help="Publish docs to github-pages.")
 @click.option("--spark-version", multiple=True, show_default=True,
-              default=["2.4.8", "3.0.3", "3.1.3", "3.2.2", "3.3.0"])
+              default=["3.0.3", "3.1.3", "3.2.2", "3.3.0"])
 def main(release_version, next_version, publish_to, no_prompt, git_remote, publish_docs,
          spark_version):
     interactive = not no_prompt

diff --git a/python/graphframes/examples/belief_propagation.py b/python/graphframes/examples/belief_propagation.py
@@ -17,14 +17,12 @@
 
 import math
 
-from pyspark import SparkConf, SparkContext
-from pyspark.sql import SQLContext, functions as sqlfunctions, types
-
+# Import subpackage examples here explicitly so that
+# this module can be run directly with spark-submit.
+import graphframes.examples
 from graphframes import GraphFrame
 from graphframes.lib import AggregateMessages as AM
-# Import subpackage examples here explicitly so that this module can be
-# run directly with spark-submit.
-import graphframes.examples
+from pyspark.sql import SparkSession, functions as sqlfunctions, types
 
 __all__ = ['BeliefPropagation']
 
@@ -151,13 +149,11 @@ def _sigmoid(x):
 
 def main():
     """Run the belief propagation algorithm for an example problem."""
-    # setup context
-    conf = SparkConf().setAppName("BeliefPropagation example")
-    sc = SparkContext.getOrCreate(conf)
-    sql = SQLContext.getOrCreate(sc)
+    # setup spark session
+    spark = SparkSession.builder.appName("BeliefPropagation example").getOrCreate()
 
     # create graphical model g of size 3 x 3
-    g = graphframes.examples.Graphs(sql).gridIsingModel(3)
+    g = graphframes.examples.Graphs(spark).gridIsingModel(3)
     print("Original Ising model:")
     g.vertices.show()
     g.edges.show()
@@ -171,7 +167,8 @@ def main():
     print("Done with BP. Final beliefs after {} iterations:".format(numIter))
     beliefs.show()
 
-    sc.stop()
+    spark.stop()
+
 
 if __name__ == '__main__':
     main()
diff --git a/python/graphframes/examples/graphs.py b/python/graphframes/examples/graphs.py
@@ -27,18 +27,17 @@
 class Graphs(object):
     """Example GraphFrames for testing the API
 
-    :param sqlContext: SQLContext
+    :param spark: SparkSession
     """
 
-    def __init__(self, sqlContext):
-        self._sql = sqlContext
-        self._sc = sqlContext._sc
+    def __init__(self, spark):
+        self._spark = spark
+        self._sc = spark._sc
 
     def friends(self):
         """A GraphFrame of friends in a (fake) social network."""
-        sqlContext = self._sql
         # Vertex DataFrame
-        v = sqlContext.createDataFrame([
+        v = self._spark.createDataFrame([
             ("a", "Alice", 34),
             ("b", "Bob", 36),
             ("c", "Charlie", 30),
@@ -47,7 +46,7 @@ def friends(self):
             ("f", "Fanny", 36)
         ], ["id", "name", "age"])
         # Edge DataFrame
-        e = sqlContext.createDataFrame([
+        e = self._spark.createDataFrame([
             ("a", "b", "friend"),
             ("b", "c", "follow"),
             ("c", "b", "follow"),
@@ -92,7 +91,7 @@ def gridIsingModel(self, n, vStd=1.0, eStd=1.0):
                 .format(n))
 
         # create coodinates grid
-        coordinates = self._sql.createDataFrame(
+        coordinates = self._spark.createDataFrame(
             itertools.product(range(n), range(n)),
             schema=('i', 'j'))
 

diff --git a/python/graphframes/graphframe.py b/python/graphframes/graphframe.py
@@ -20,20 +20,20 @@
     basestring = str
 
 from pyspark import SparkContext
-from pyspark.sql import Column, DataFrame, SQLContext
+from pyspark.sql import Column, DataFrame, SparkSession
 from pyspark.storagelevel import StorageLevel
 
 from graphframes.lib import Pregel
 
 
-def _from_java_gf(jgf, sqlContext):
+def _from_java_gf(jgf, spark):
     """
     (internal) creates a python GraphFrame wrapper from a java GraphFrame.
 
     :param jgf:
     """
-    pv = DataFrame(jgf.vertices(), sqlContext)
-    pe = DataFrame(jgf.edges(), sqlContext)
+    pv = DataFrame(jgf.vertices(), spark)
+    pe = DataFrame(jgf.edges(), spark)
     return GraphFrame(pv, pe)
 
 def _java_api(jsc):
@@ -55,16 +55,16 @@ class GraphFrame(object):
 
     >>> localVertices = [(1,"A"), (2,"B"), (3, "C")]
     >>> localEdges = [(1,2,"love"), (2,1,"hate"), (2,3,"follow")]
-    >>> v = sqlContext.createDataFrame(localVertices, ["id", "name"])
-    >>> e = sqlContext.createDataFrame(localEdges, ["src", "dst", "action"])
+    >>> v = spark.createDataFrame(localVertices, ["id", "name"])
+    >>> e = spark.createDataFrame(localEdges, ["src", "dst", "action"])
     >>> g = GraphFrame(v, e)
     """
 
     def __init__(self, v, e):
         self._vertices = v
         self._edges = e
-        self._sqlContext = v.sql_ctx
-        self._sc = self._sqlContext._sc
+        self._spark = SparkSession.getActiveSession()
+        self._sc = self._spark._sc
         self._jvm_gf_api = _java_api(self._sc)
 
         self.ID = self._jvm_gf_api.ID()
@@ -142,7 +142,7 @@ def outDegrees(self):
         :return:  DataFrame with new vertices column "outDegree"
         """
         jdf = self._jvm_graph.outDegrees()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     @property
     def inDegrees(self):
@@ -156,7 +156,7 @@ def inDegrees(self):
         :return:  DataFrame with new vertices column "inDegree"
         """
         jdf = self._jvm_graph.inDegrees()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     @property
     def degrees(self):
@@ -170,7 +170,7 @@ def degrees(self):
         :return:  DataFrame with new vertices column "degree"
         """
         jdf = self._jvm_graph.degrees()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     @property
     def triplets(self):
@@ -185,7 +185,7 @@ def triplets(self):
         :return:  DataFrame with columns 'src', 'edge', and 'dst'
         """
         jdf = self._jvm_graph.triplets()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     @property
     def pregel(self):
@@ -206,7 +206,7 @@ def find(self, pattern):
         :return:  DataFrame with one Row for each instance of the motif found
         """
         jdf = self._jvm_graph.find(pattern)
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def filterVertices(self, condition):
         """
@@ -222,7 +222,7 @@ def filterVertices(self, condition):
             jdf = self._jvm_graph.filterVertices(condition._jc)
         else:
             raise TypeError("condition should be string or Column")
-        return _from_java_gf(jdf, self._sqlContext)
+        return _from_java_gf(jdf, self._spark)
 
     def filterEdges(self, condition):
         """
@@ -237,7 +237,7 @@ def filterEdges(self, condition):
             jdf = self._jvm_graph.filterEdges(condition._jc)
         else:
             raise TypeError("condition should be string or Column")
-        return _from_java_gf(jdf, self._sqlContext)
+        return _from_java_gf(jdf, self._spark)
 
     def dropIsolatedVertices(self):
         """
@@ -246,7 +246,7 @@ def dropIsolatedVertices(self):
         :return: GraphFrame with filtered vertices. 
         """
         jdf = self._jvm_graph.dropIsolatedVertices()
-        return _from_java_gf(jdf, self._sqlContext)
+        return _from_java_gf(jdf, self._spark)
 
     def bfs(self, fromExpr, toExpr, edgeFilter=None, maxPathLength=10):
         """
@@ -263,7 +263,7 @@ def bfs(self, fromExpr, toExpr, edgeFilter=None, maxPathLength=10):
         if edgeFilter is not None:
             builder.edgeFilter(edgeFilter)
         jdf = builder.run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def aggregateMessages(self, aggCol, sendToSrc=None, sendToDst=None):
         """
@@ -305,7 +305,7 @@ def aggregateMessages(self, aggCol, sendToSrc=None, sendToDst=None):
             jdf = builder.agg(aggCol._jc)
         else:
             jdf = builder.agg(aggCol)
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     # Standard algorithms
 
@@ -329,7 +329,7 @@ def connectedComponents(self, algorithm = "graphframes", checkpointInterval = 2,
             .setCheckpointInterval(checkpointInterval) \
             .setBroadcastThreshold(broadcastThreshold) \
             .run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def labelPropagation(self, maxIter):
         """
@@ -341,7 +341,7 @@ def labelPropagation(self, maxIter):
         :return: DataFrame with new vertices column "label"
         """
         jdf = self._jvm_graph.labelPropagation().maxIter(maxIter).run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def pageRank(self, resetProbability = 0.15, sourceId = None, maxIter = None,
                  tol = None):
@@ -369,7 +369,7 @@ def pageRank(self, resetProbability = 0.15, sourceId = None, maxIter = None,
             assert tol is not None, "Exactly one of maxIter or tol should be set."
             builder = builder.tol(tol)
         jgf = builder.run()
-        return _from_java_gf(jgf, self._sqlContext)
+        return _from_java_gf(jgf, self._spark)
 
     def parallelPersonalizedPageRank(self, resetProbability = 0.15, sourceIds = None,
                                      maxIter = None):
@@ -392,7 +392,7 @@ def parallelPersonalizedPageRank(self, resetProbability = 0.15, sourceIds = None
         builder = builder.sourceIds(sourceIds)
         builder = builder.maxIter(maxIter)
         jgf = builder.run()
-        return _from_java_gf(jgf, self._sqlContext)
+        return _from_java_gf(jgf, self._spark)
 
     def shortestPaths(self, landmarks):
         """
@@ -404,7 +404,7 @@ def shortestPaths(self, landmarks):
         :return: DataFrame with new vertices column "distances"
         """
         jdf = self._jvm_graph.shortestPaths().landmarks(landmarks).run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def stronglyConnectedComponents(self, maxIter):
         """
@@ -416,7 +416,7 @@ def stronglyConnectedComponents(self, maxIter):
         :return: DataFrame with new vertex column "component"
         """
         jdf = self._jvm_graph.stronglyConnectedComponents().maxIter(maxIter).run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
     def svdPlusPlus(self, rank = 10, maxIter = 2, minValue = 0.0, maxValue = 5.0,
                     gamma1 = 0.007, gamma2 = 0.007, gamma6 = 0.005, gamma7 = 0.015):
@@ -433,7 +433,7 @@ def svdPlusPlus(self, rank = 10, maxIter = 2, minValue = 0.0, maxValue = 5.0,
         builder.gamma1(gamma1).gamma2(gamma2).gamma6(gamma6).gamma7(gamma7)
         jdf = builder.run()
         loss = builder.loss()
-        v = DataFrame(jdf, self._sqlContext)
+        v = DataFrame(jdf, self._spark)
         return (v, loss)
 
     def triangleCount(self):
@@ -445,15 +445,15 @@ def triangleCount(self):
         :return:  DataFrame with new vertex column "count"
         """
         jdf = self._jvm_graph.triangleCount().run()
-        return DataFrame(jdf, self._sqlContext)
+        return DataFrame(jdf, self._spark)
 
 
 def _test():
     import doctest
     import graphframe
     globs = graphframe.__dict__.copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    globs['sqlContext'] = SQLContext(globs['sc'])
+    globs['spark'] = SparkSession(globs['sc']).builder.getOrCreate()
     (failure_count, test_count) = doctest.testmod(
         globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
     globs['sc'].stop()

diff --git a/python/graphframes/lib/aggregate_messages.py b/python/graphframes/lib/aggregate_messages.py
@@ -16,7 +16,7 @@
 #
 
 from pyspark import SparkContext
-from pyspark.sql import DataFrame, functions as sqlfunctions
+from pyspark.sql import DataFrame, functions as sqlfunctions, SparkSession
 
 
 def _java_api(jsc):
@@ -77,7 +77,7 @@ def getCachedDataFrame(df):
         WARNING: This is NOT the same as `DataFrame.cache()`.
                  The original DataFrame will NOT be cached.
         """
-        sqlContext = df.sql_ctx
-        jvm_gf_api = _java_api(sqlContext._sc)
+        spark = SparkSession.getActiveSession()
+        jvm_gf_api = _java_api(spark._sc)
         jdf = jvm_gf_api.aggregateMessages().getCachedDataFrame(df._jdf)
-        return DataFrame(jdf, sqlContext)
+        return DataFrame(jdf, spark)
diff --git a/python/graphframes/lib/pregel.py b/python/graphframes/lib/pregel.py
@@ -19,9 +19,9 @@
 if sys.version > '3':
     basestring = str
 
-from pyspark.sql import DataFrame
+from pyspark.sql import DataFrame, SparkSession
 from pyspark.sql.functions import col
-from pyspark.ml.wrapper import JavaWrapper, _jvm
+from pyspark.ml.wrapper import JavaWrapper
 
 
 class Pregel(JavaWrapper):
@@ -169,7 +169,7 @@ def run(self):
 
         :return: the result vertex DataFrame from the final iteration including both original and additional columns.
         """
-        return DataFrame(self._java_obj.run(), self.graph.vertices.sql_ctx)
+        return DataFrame(self._java_obj.run(), SparkSession.getActiveSession())
 
     @staticmethod
     def msg():