Merge pull request #27 from wyy566/master

adapt to Spark 3.1.1

README.md

@@ -5,9 +5,9 @@
 Introduction
 ============
 
-The graph algorithm library running on Kunpeng processors is an acceleration library that provides a rich set of high-level tools for graph algorithms. It is developed based on original APIs of Apache [Spark 2.3.2](https://github.com/apache/spark/tree/v2.3.2). The acceleration library greatly improves the computing power in big data scenarios. Additionally, it provides multiple APIs in addition to the original APIs if the Apache Spark graph library.
+The graph algorithm library running on Kunpeng processors is an acceleration library that provides a rich set of high-level tools for graph algorithms. It is developed based on original APIs of Apache [Spark 3.1.1](https://github.com/apache/spark/tree/v3.1.1). The acceleration library greatly improves the computing power in big data scenarios. Additionally, it provides multiple APIs in addition to the original APIs if the Apache Spark graph library.
 
-The library provides 23 graph algorithms: triangle count (TC), weak clique enumeration (WCE), maximal clique enumeration (MCE), modualrity, closeness, cycle detection (CD), label propagation algorithm (LPA), Louvain, PageRank, IncPageRank, Weighted PageRank, shortest-paths, strongly connected components (SCC), connected components (CC), K-core decomposition (KCore), degree centrality (Degree), breadth-first-search (BFS), ClusteringCoefficient, TrustRank, PersonalizedPageRank, Betweenness, Node2Vec and SubgraphMatching. You can find the latest documentation on the project web page. This README file contains only basic setup instructions.
+The library provides 16 graph algorithms: triangle count (TC), weak clique enumeration (WCE), maximal clique enumeration (MCE), modualrity, cycle detection (CD), label propagation algorithm (LPA), Louvain, PageRank, Multiple Source shortest path(MSSP), strongly connected components (SCC), K-core decomposition (KCore), breadth-first-search (BFS), ClusteringCoefficient, PersonalizedPageRank, Betweenness and SubgraphMatching. You can find the latest documentation on the project web page. This README file contains only basic setup instructions.
 
 
 
@@ -20,11 +20,11 @@ Building
 
     mvn package
 
- Obtain "boostkit-graph-acc_2.11-2.1.0-Spark2.3.2.jar" from the "Spark-graph-algo-lib/graph-accelerator/target/" directory
+ Obtain "boostkit-graph-acc_2.12-2.1.0-spark3.1.1.jar" from the "Spark-graph-algo-lib/graph-accelerator/target/" directory
 
- Obtain "boostkit-graph-core_2.11-2.1.0-Spark2.3.2.jar" from the "Spark-graph-algo-lib/graph-core/target/" directory
+ Obtain "boostkit-graph-core_2.12-2.1.0-Spark3.1.1.jar" from the "Spark-graph-algo-lib/graph-core/target/" directory
 
- Obtain "boostkit-graph-kernel-clinet_2.11-2.1.0-Spark2.3.2.jar" from the "Spark-graph-algo-lib/graph-kernel/target/" directory
+ Obtain "boostkit-graph-kernel-clinet_2.12-2.1.0-Spark3.1.1.jar" from the "Spark-graph-algo-lib/graph-kernel/target/" directory
 
 
 

graph-accelerator/pom.xml

@@ -7,20 +7,20 @@
 
   <modelVersion>4.0.0</modelVersion>
   <groupId>org.apache.spark.graphx.lib</groupId>
-  <artifactId>boostkit-graph-acc_2.11</artifactId>
+  <artifactId>boostkit-graph-acc_2.12</artifactId>
   <version>2.1.0</version>
   <name>${project.artifactId}</name>
   <description>Spark graph algo accelerator</description>
 
   <dependencies>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>spark-graphx_2.11</artifactId>
-      <version>2.3.2</version>
+      <artifactId>spark-graphx_2.12</artifactId>
+      <version>3.1.1</version>
     </dependency>
     <dependency>
       <groupId>org.apache.spark.graphx.lib</groupId>
-      <artifactId>boostkit-graph-kernel-client_2.11</artifactId>
+      <artifactId>boostkit-graph-kernel-client_2.12</artifactId>
       <version>2.1.0</version>
       <classifier>${spark.version}</classifier>
       <scope>compile</scope>
@@ -32,7 +32,7 @@
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
-      <artifactId>scalatest_2.11</artifactId>
+      <artifactId>scalatest_2.12</artifactId>
       <version>3.0.5</version>
       <scope>test</scope>
     </dependency>
@@ -43,8 +43,8 @@
     </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
-      <artifactId>spark-mllib_2.11</artifactId>
-      <version>2.3.2</version>
+      <artifactId>spark-mllib_2.12</artifactId>
+      <version>3.1.1</version>
     </dependency>
   </dependencies>