graphframes
diff --git a/‎docs/quick-start.md
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+4-4Lines changed: 4 additions & 4 deletions b/‎docs/quick-start.md
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+4-4Lines changed: 4 additions & 4 deletions
diff --git a/‎docs/user-guide.md
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+23-23Lines changed: 23 additions & 23 deletions b/‎docs/user-guide.md
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+23-23Lines changed: 23 additions & 23 deletions
diff --git a/‎src/main/scala/org/graphframes/GraphFrame.scala
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+7-7Lines changed: 7 additions & 7 deletions b/‎src/main/scala/org/graphframes/GraphFrame.scala
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+7-7Lines changed: 7 additions & 7 deletions
diff --git a/‎src/main/scala/org/graphframes/examples/BeliefPropagation.scala
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+2-4Lines changed: 2 additions & 4 deletions b/‎src/main/scala/org/graphframes/examples/BeliefPropagation.scala
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+2-4Lines changed: 2 additions & 4 deletions
@@ -61,13 +61,13 @@ The following example shows how to create a GraphFrame, query it, and run the Pa
 // import graphframes package
 import org.graphframes._
 // Create a Vertex DataFrame with unique ID column "id"
-val v = sqlContext.createDataFrame(List(
+val v = spark.createDataFrame(List(
   ("a", "Alice", 34),
   ("b", "Bob", 36),
   ("c", "Charlie", 30)
 )).toDF("id", "name", "age")
 // Create an Edge DataFrame with "src" and "dst" columns
-val e = sqlContext.createDataFrame(List(
+val e = spark.createDataFrame(List(
   ("a", "b", "friend"),
   ("b", "c", "follow"),
   ("c", "b", "follow")
@@ -91,13 +91,13 @@ results.vertices.select("id", "pagerank").show()
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 # Create a Vertex DataFrame with unique ID column "id"
-v = sqlContext.createDataFrame([
+v = spark.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([
+e = spark.createDataFrame([
   ("a", "b", "friend"),
   ("b", "c", "follow"),
   ("c", "b", "follow"),
 
@@ -37,7 +37,7 @@ The following example demonstrates how to create a GraphFrame from vertex and ed
 {% highlight scala %}
 import org.graphframes.GraphFrame
 // Vertex DataFrame
-val v = sqlContext.createDataFrame(List(
+val v = spark.createDataFrame(List(
   ("a", "Alice", 34),
   ("b", "Bob", 36),
   ("c", "Charlie", 30),
@@ -47,7 +47,7 @@ val v = sqlContext.createDataFrame(List(
   ("g", "Gabby", 60)
 )).toDF("id", "name", "age")
 // Edge DataFrame
-val e = sqlContext.createDataFrame(List(
+val e = spark.createDataFrame(List(
   ("a", "b", "friend"),
   ("b", "c", "follow"),
   ("c", "b", "follow"),
@@ -71,7 +71,7 @@ val g: GraphFrame = examples.Graphs.friends
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 # Vertex DataFrame
-v = sqlContext.createDataFrame([
+v = spark.createDataFrame([
   ("a", "Alice", 34),
   ("b", "Bob", 36),
   ("c", "Charlie", 30),
@@ -81,7 +81,7 @@ v = sqlContext.createDataFrame([
   ("g", "Gabby", 60)
 ], ["id", "name", "age"])
 # Edge DataFrame
-e = sqlContext.createDataFrame([
+e = spark.createDataFrame([
   ("a", "b", "friend"),
   ("b", "c", "follow"),
   ("c", "b", "follow"),
@@ -98,7 +98,7 @@ g = GraphFrame(v, e)
 The GraphFrame constructed above is available in the GraphFrames package:
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()
+g = Graphs.friends()
 {% endhighlight %}
 </div>
 
@@ -166,7 +166,7 @@ val numFollows = g.edges.filter("relationship = 'follow'").count()
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Display the vertex and edge DataFrames
 g.vertices.show()
@@ -294,7 +294,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Search for pairs of vertices with edges in both directions between them.
 motifs = g.find("(a)-[e]->(b); (b)-[e2]->(a)")
@@ -360,7 +360,7 @@ chainWith2Friends2.show()
 from pyspark.sql.functions import col, lit, when
 from pyspark.sql.types import IntegerType
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
 
@@ -414,7 +414,7 @@ val g1 = g.filterVertices("age > 30").filterEdges("relationship = 'friend'").dro
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Select subgraph of users older than 30, and relationships of type "friend".
 # Drop isolated vertices (users) which are not contained in any edges (relationships).
@@ -455,7 +455,7 @@ val g2 = GraphFrame(g.vertices, e2)
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Select subgraph based on edges "e" of type "follow"
 # pointing from a younger user "a" to an older user "b".
@@ -522,7 +522,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Search from "Esther" for users of age < 32.
 paths = g.bfs("name = 'Esther'", "age < 32")
@@ -568,7 +568,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 result = g.connectedComponents()
 result.select("id", "component").orderBy("component").show()
@@ -605,7 +605,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 result = g.stronglyConnectedComponents(maxIter=10)
 result.select("id", "component").orderBy("component").show()
@@ -649,7 +649,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 result = g.labelPropagation(maxIter=5)
 result.select("id", "label").show()
@@ -707,7 +707,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Run PageRank until convergence to tolerance "tol".
 results = g.pageRank(resetProbability=0.15, tol=0.01)
@@ -759,7 +759,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 results = g.shortestPaths(landmarks=["a", "d"])
 results.select("id", "distances").show()
@@ -793,7 +793,7 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 results = g.triangleCount()
 results.select("id", "count").show()
@@ -823,8 +823,8 @@ g.vertices.write.parquet("hdfs://myLocation/vertices")
 g.edges.write.parquet("hdfs://myLocation/edges")
 
 // Load the vertices and edges back.
-val sameV = sqlContext.read.parquet("hdfs://myLocation/vertices")
-val sameE = sqlContext.read.parquet("hdfs://myLocation/edges")
+val sameV = spark.read.parquet("hdfs://myLocation/vertices")
+val sameE = spark.read.parquet("hdfs://myLocation/edges")
 
 // Create an identical GraphFrame.
 val sameG = GraphFrame(sameV, sameE)
@@ -834,15 +834,15 @@ val sameG = GraphFrame(sameV, sameE)
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # Save vertices and edges as Parquet to some location.
 g.vertices.write.parquet("hdfs://myLocation/vertices")
 g.edges.write.parquet("hdfs://myLocation/edges")
 
 # Load the vertices and edges back.
-sameV = sqlContext.read.parquet("hdfs://myLocation/vertices")
-sameE = sqlContext.read.parquet("hdfs://myLocation/edges")
+sameV = spark.read.parquet("hdfs://myLocation/vertices")
+sameE = spark.read.parquet("hdfs://myLocation/edges")
 
 # Create an identical GraphFrame.
 sameG = GraphFrame(sameV, sameE)
@@ -903,7 +903,7 @@ For API details, refer to the
 from pyspark.sql.functions import sum as sqlsum
 from graphframes.lib import AggregateMessages as AM
 from graphframes.examples import Graphs
-g = Graphs(sqlContext).friends()  # Get example graph
+g = Graphs.friends()  # Get example graph
 
 # For each user, sum the ages of the adjacent users.
 msgToSrc = AM.dst["age"]
 
@@ -507,12 +507,12 @@ class GraphFrame private(
           val (nextDF, nextNames) = findIncremental(this, handledPatterns, dfOpt, names, cur)
           (handledPatterns :+ cur, nextDF, nextNames)
       }
-    finalDFOpt.getOrElse(sqlContext.emptyDataFrame)
+    finalDFOpt.getOrElse(spark.emptyDataFrame)
   }
 
   // ========= Other private methods ===========
 
-  private[graphframes] def sqlContext: SQLContext = vertices.sqlContext
+  private[graphframes] def spark: SparkSession = vertices.sparkSession
 
   /**
    * True if the id type can be cast to Long.
@@ -618,8 +618,8 @@ object GraphFrame extends Serializable with Logging {
       joinCol: String,
       hubs: Set[T],
       logPrefix: String): DataFrame = {
-    val sqlContext = a.sqlContext
-    import sqlContext.implicits._
+    val spark = a.sparkSession
+    import spark.implicits._
     if (hubs.isEmpty) {
       // No skew.  Do regular join.
       a.join(b, joinCol)
@@ -719,9 +719,9 @@ object GraphFrame extends Serializable with Logging {
    */
   // TODO: Add version which takes explicit schemas.
   def fromGraphX[VD : TypeTag, ED : TypeTag](graph: Graph[VD, ED]): GraphFrame = {
-    val sqlContext = SparkSession.builder().getOrCreate().sqlContext
-    val vv = sqlContext.createDataFrame(graph.vertices).toDF(ID, ATTR)
-    val ee = sqlContext.createDataFrame(graph.edges).toDF(SRC, DST, ATTR)
+    val spark = SparkSession.builder().getOrCreate()
+    val vv = spark.createDataFrame(graph.vertices).toDF(ID, ATTR)
+    val ee = spark.createDataFrame(graph.edges).toDF(SRC, DST, ATTR)
     GraphFrame(vv, ee)
   }
 
 
@@ -19,7 +19,7 @@ package org.graphframes.examples
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.graphx.{Graph, VertexRDD, Edge => GXEdge}
-import org.apache.spark.sql.{Column, Row, SparkSession, SQLContext}
+import org.apache.spark.sql.{Column, Row, SparkSession}
 import org.apache.spark.sql.functions.{col, lit, sum, udf, when}
 
 import org.graphframes.GraphFrame
@@ -74,10 +74,8 @@ object BeliefPropagation {
       .appName("BeliefPropagation example")
       .getOrCreate()
 
-    val sql = spark.sqlContext
-
     // Create graphical model g of size 3 x 3.
-    val g = gridIsingModel(sql, 3)
+    val g = gridIsingModel(spark, 3)
 
     println("Original Ising model:")
     g.vertices.show()