diff --git a/VERSION b/VERSION
new file mode 100644
index 000000000..e69de29bb
diff --git a/build.sbt b/build.sbt
index 108b371c2..ec2b5c223 100644
--- a/build.sbt
+++ b/build.sbt
@@ -3,7 +3,7 @@
 
 import ReleaseTransformations._
 
-val sparkVer = sys.props.getOrElse("spark.version", "3.5.0")
+val sparkVer = sys.props.getOrElse("spark.version", "3.5.3")
 val sparkBranch = sparkVer.substring(0, 3)
 val defaultScalaVer = sparkBranch match {
   case "3.5" => "2.12.18"
@@ -63,7 +63,13 @@ scalacOptions in (Test, doc) ++= Seq("-groups", "-implicits")
 fork in Test := true
 
 // This and the next line fix a problem with forked run: https://github.com/scalatest/scalatest/issues/770
-javaOptions in Test ++= Seq("-Xmx2048m", "-XX:ReservedCodeCacheSize=384m", "-XX:MaxPermSize=384m")
+javaOptions in Test ++= Seq(
+  "-Xmx2048m",
+  "-XX:ReservedCodeCacheSize=384m",
+  "-XX:MaxMetaspaceSize=384m",
+  "--add-opens=java.base/sun.nio.ch=ALL-UNNAMED",
+  "--add-opens=java.base/java.lang=ALL-UNNAMED"
+)
 
 concurrentRestrictions in Global := Seq(
   Tags.limitAll(1))
diff --git a/build/sbt-launch-lib.bash b/build/sbt-launch-lib.bash
index 9b3f0e934..6ab8d7313 100755
--- a/build/sbt-launch-lib.bash
+++ b/build/sbt-launch-lib.bash
@@ -117,7 +117,7 @@ get_mem_opts () {
   (( $perm < 4096 )) || perm=4096
   local codecache=$(( $perm / 2 ))
 
-  echo "-Xms${mem}m -Xmx${mem}m -XX:MaxPermSize=${perm}m -XX:ReservedCodeCacheSize=${codecache}m"
+  echo "-Xms${mem}m -Xmx${mem}m -XX:MaxMetaspaceSize=${perm}m -XX:ReservedCodeCacheSize=${codecache}m"
 }
 
 require_arg () {
diff --git a/docs/_config.yml b/docs/_config.yml
index 55afbc5f2..4c1ab075c 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -13,7 +13,7 @@ include:
 
 # These allow the documentation to be updated with newer releases
 # of Spark, Scala, and Mesos.
-GRAPHFRAMES_VERSION: 0.8.0
+GRAPHFRAMES_VERSION: 0.8.4
 #SCALA_BINARY_VERSION: "2.10"
 #SCALA_VERSION: "2.10.4"
 #MESOS_VERSION: 0.21.0
diff --git a/docs/index.md b/docs/index.md
index bc8da4cb8..9bd2ccb82 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -27,23 +27,12 @@ Refer to the [User Guide](user-guide.html) for a full list of queries and algori
 
 __Will GraphFrames be part of Apache Spark?__
 
-The GraphX component of Apache Spark has no DataFrames- or Dataset-based equivalent, so it is
-natural to ask this question. The current plan is to keep GraphFrames separate from core Apache
-Spark for the time being:
+The GraphX component of Apache Spark has no DataFrames - or Dataset-based equivalent, so it is
+natural to ask this question.
 
-* we are still considering making small adjustments to the API. The GraphFrames project will be
-considered for inclusion into Spark once we are confident that the current API addresses current
-and future needs.
-
-* some important features present in GraphX such as partitioning are missing. We would like to
-offer some equivalent operations before considering merging with the Spark project.
-
-* GraphFrames is used as a testbed for advanced, graph-specific optimizations into Spark’s
-Catalyst engine. Having them in a separate project accelerates the development cycle.
-
-That being said, GraphFrames follows the same code quality standards as Spark, and it is
-cross-compiled and published for a large number of Spark versions. It is
-easy for users to depend on it.
+**The new plan is to merge GraphFrames with Apache Spark to make it a component of core Spark.
+[Spark committer Holden Karau](https://spark.apache.org/committers.html#:~:text=Holden%20Karau,Netflix)
+is willing to support this effort by reviewing the relevant pull requests.**
 
 # Downloading
 
diff --git a/docs/user-guide.md b/docs/user-guide.md
index bfe07751f..7e66f74ff 100644
--- a/docs/user-guide.md
+++ b/docs/user-guide.md
@@ -6,7 +6,7 @@ description: GraphFrames GRAPHFRAMES_VERSION user guide
 ---
 
 This page gives examples of how to use GraphFrames for basic queries, motif finding, and
-general graph algorithms.  This includes code examples in Scala and Python.
+general graph algorithms. This includes code examples in Scala and Python.
 
 * Table of contents (This text will be scraped.)
 {:toc}
@@ -33,7 +33,7 @@ The vertices will be inferred from the sources and destinations of the edges.
 
 The following example demonstrates how to create a GraphFrame from vertex and edge DataFrames.
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.graphframes.GraphFrame
 // Vertex DataFrame
@@ -64,11 +64,12 @@ val g = GraphFrame(v, e)
 The GraphFrame constructed above is available in the GraphFrames package:
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 {% highlight python %}
 # Vertex DataFrame
 v = spark.createDataFrame([
@@ -98,7 +99,8 @@ g = GraphFrame(v, e)
 The GraphFrame constructed above is available in the GraphFrames package:
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()
+
+g = Graphs(spark).friends()  # Get example graph
 {% endhighlight %}
 </div>
 
@@ -114,9 +116,10 @@ as `vertices` and `edges` fields in the GraphFrame.
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Display the vertex and edge DataFrames
@@ -152,6 +155,7 @@ import org.apache.spark.sql.DataFrame
 
 // Get a DataFrame with columns "id" and "inDeg" (in-degree)
 val vertexInDegrees: DataFrame = g.inDegrees
+vertexInDegrees.show()
 
 // Find the youngest user's age in the graph.
 // This queries the vertex DataFrame.
@@ -163,10 +167,11 @@ val numFollows = g.edges.filter("relationship = 'follow'").count()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 # Display the vertex and edge DataFrames
 g.vertices.show()
@@ -271,16 +276,18 @@ matching edge, even if those edges share the same vertex `u`.
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.GraphFrame).
 
 {% highlight scala %}
+import org.apache.spark.sql.DataFrame
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Search for pairs of vertices with edges in both directions between them.
-val motifs: GraphFrame = g.find("(a)-[e]->(b); (b)-[e2]->(a)")
+val motifs: DataFrame = g.find("(a)-[e]->(b); (b)-[e2]->(a)")
 motifs.show()
 
 // More complex queries can be expressed by applying filters.
@@ -288,13 +295,14 @@ motifs.filter("b.age > 30").show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.find).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).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)")
@@ -329,16 +337,17 @@ In this example, the state is the current count of "friend" edges; in general, i
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
-import org.apache.spark.sql.Column
+import org.apache.spark.sql.{Column, DataFrame}
 import org.apache.spark.sql.functions.{col, when}
 import org.graphframes.{examples,GraphFrame}
 
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Find chains of 4 vertices.
-val chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
+val chain4: DataFrame = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
+chain4.show()
 
 // Query on sequence, with state (cnt)
 //  (a) Define method for updating state given the next element of the motif.
@@ -360,7 +369,8 @@ chainWith2Friends2.show()
 from pyspark.sql.functions import col, lit, when
 from pyspark.sql.types import IntegerType
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
 
@@ -399,9 +409,10 @@ The following example shows how to select a subgraph based upon vertex and edge
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends
 
 // Select subgraph of users older than 30, and relationships of type "friend".
@@ -411,10 +422,11 @@ val g1 = g.filterVertices("age > 30").filterEdges("relationship = 'friend'").dro
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).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).
@@ -435,6 +447,7 @@ triplets by using more complex motifs.
 <div data-lang="scala"  markdown="1">
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Select subgraph based on edges "e" of type "follow"
@@ -443,19 +456,18 @@ val paths = { g.find("(a)-[e]->(b)")
   .filter("e.relationship = 'follow'")
   .filter("a.age < b.age") }
 // "paths" contains vertex info. Extract the edges.
-val e2 = paths.select("e.src", "e.dst", "e.relationship")
-// In Spark 1.5+, the user may simplify this call:
-//  val e2 = paths.select("e.*")
+val e2 = paths.select("e.*")
 
 // Construct the subgraph
 val g2 = GraphFrame(g.vertices, e2)
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 # Select subgraph based on edges "e" of type "follow"
 # pointing from a younger user "a" to an older user "b".
@@ -503,6 +515,7 @@ For API details, refer to the [API docs](api/scala/index.html#org.graphframes.li
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Search from "Esther" for users of age < 32.
@@ -510,19 +523,21 @@ val paths = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32").run()
 paths.show()
 
 // Specify edge filters or max path lengths.
-{ g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
+val paths = { g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
   .edgeFilter("relationship != 'friend'")
   .maxPathLength(3).run() }
+paths.show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.bfs).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 # Search from "Esther" for users of age < 32.
 paths = g.bfs("name = 'Esther'", "age < 32")
@@ -544,7 +559,7 @@ assigned a component ID.
 See [Wikipedia](https://en.wikipedia.org/wiki/Connected_component_(graph_theory)) for background.
 
 NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm
-requires setting a Spark checkpoint directory.  Users can revert to the old algorithm using
+requires setting a Spark checkpoint directory. Users can revert to the old algorithm using
 `connectedComponents.setAlgorithm("graphx")`.
 
 <div class="codetabs">
@@ -555,6 +570,9 @@ For API details, refer to the [API docs](api/scala/index.html#org.graphframes.li
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
+sc.setCheckpointDir("/tmp/spark-checkpoints")
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 val result = g.connectedComponents.run()
@@ -562,13 +580,16 @@ result.select("id", "component").orderBy("component").show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.connectedComponents).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+sc.setCheckpointDir("/tmp/spark-checkpoints")
+
+g = Graphs(spark).friends()  # Get example graph
 
 result = g.connectedComponents()
 result.select("id", "component").orderBy("component").show()
@@ -586,12 +607,13 @@ See [Wikipedia](https://en.wikipedia.org/wiki/Strongly_connected_component) for
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.lib.StronglyConnectedComponents).
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 val result = g.stronglyConnectedComponents.maxIter(10).run()
@@ -599,13 +621,16 @@ result.select("id", "component").orderBy("component").show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.stronglyConnectedComponents).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+sc.setCheckpointDir("/tmp/spark-checkpoints")
+
+g = Graphs(spark).friends()  # Get example graph
 
 result = g.stronglyConnectedComponents(maxIter=10)
 result.select("id", "component").orderBy("component").show()
@@ -630,12 +655,13 @@ See [Wikipedia](https://en.wikipedia.org/wiki/Label_Propagation_Algorithm) for b
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.lib.LabelPropagation).
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 val result = g.labelPropagation.maxIter(5).run()
@@ -643,13 +669,14 @@ result.select("id", "label").show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.labelPropagation).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 result = g.labelPropagation(maxIter=5)
 result.select("id", "label").show()
@@ -674,16 +701,18 @@ See [Wikipedia](https://en.wikipedia.org/wiki/PageRank) for background.
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.lib.PageRank).
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Run PageRank until convergence to tolerance "tol".
-val results = g.pageRank.resetProbability(0.15).tol(0.01).run()
+val results: GraphFrame = g.pageRank.resetProbability(0.15).tol(0.01).run()
+
 // Display resulting pageranks and final edge weights
 // Note that the displayed pagerank may be truncated, e.g., missing the E notation.
 // In Spark 1.5+, you can use show(truncate=false) to avoid truncation.
@@ -697,17 +726,20 @@ val results2 = g.pageRank.resetProbability(0.15).maxIter(10).run()
 val results3 = g.pageRank.resetProbability(0.15).maxIter(10).sourceId("a").run()
 
 // Run PageRank personalized for vertex ["a", "b", "c", "d"] in parallel
-val results3 = g.parallelPersonalizedPageRank.resetProbability(0.15).maxIter(10).sourceIds(Array("a", "b", "c", "d")).run()
+val results4 = g.parallelPersonalizedPageRank.resetProbability(0.15).maxIter(10).sourceIds(Array("a", "b", "c", "d")).run()
+results4.vertices.show()
+results4.edges.show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.pageRank).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 # Run PageRank until convergence to tolerance "tol".
 results = g.pageRank(resetProbability=0.15, tol=0.01)
@@ -740,12 +772,13 @@ See [Wikipedia](https://en.wikipedia.org/wiki/Shortest_path_problem) for backgro
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.lib.ShortestPaths).
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 val results = g.shortestPaths.landmarks(Seq("a", "d")).run()
@@ -753,13 +786,14 @@ results.select("id", "distances").show()
 {% endhighlight %}
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the [API docs](api/python/graphframes.html#graphframes.GraphFrame.shortestPaths).
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 results = g.shortestPaths(landmarks=["a", "d"])
 results.select("id", "distances").show()
@@ -780,6 +814,7 @@ For API details, refer to the [API docs](api/scala/index.html#org.graphframes.li
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 val results = g.triangleCount.run()
@@ -793,7 +828,8 @@ For API details, refer to the [API docs](api/python/graphframes.html#graphframes
 
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 results = g.triangleCount()
 results.select("id", "count").show()
@@ -816,6 +852,7 @@ The below example shows how to save and then load a graph.
 <div data-lang="scala"  markdown="1">
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Save vertices and edges as Parquet to some location.
@@ -834,7 +871,8 @@ val sameG = GraphFrame(sameV, sameE)
 <div data-lang="python"  markdown="1">
 {% highlight python %}
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+
+g = Graphs(spark).friends()  # Get example graph
 
 # Save vertices and edges as Parquet to some location.
 g.vertices.write.parquet("hdfs://myLocation/vertices")
@@ -867,13 +905,14 @@ of adjacent users.
 
 <div class="codetabs">
 
-<div data-lang="scala"  markdown="1">
+<div data-lang="scala" markdown="1">
 
 For API details, refer to the [API docs](api/scala/index.html#org.graphframes.lib.AggregateMessages).
 
 {% highlight scala %}
 import org.graphframes.{examples,GraphFrame}
 import org.graphframes.lib.AggregateMessages
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // We will use AggregateMessages utilities later, so name it "AM" for short.
@@ -894,16 +933,17 @@ For a more complex example, look at the code used to implement the
 
 </div>
 
-<div data-lang="python"  markdown="1">
+<div data-lang="python" markdown="1">
 
 For API details, refer to the
 [API docs](api/python/graphframes.html#graphframes.GraphFrame.aggregateMessages).
 
 {% highlight python %}
-from pyspark.sql.functions import sum as sqlsum
 from graphframes.lib import AggregateMessages as AM
 from graphframes.examples import Graphs
-g = Graphs.friends()  # Get example graph
+from pyspark.sql.functions import sum as sqlsum
+
+g = Graphs(spark).friends()  # Get example graph
 
 # For each user, sum the ages of the adjacent users.
 msgToSrc = AM.dst["age"]
@@ -984,6 +1024,7 @@ For API details, refer to the API docs for:
 import org.apache.spark.graphx.Graph
 import org.apache.spark.sql.Row
 import org.graphframes.{examples,GraphFrame}
+
 val g: GraphFrame = examples.Graphs.friends  // get example graph
 
 // Convert to GraphX