Repository: Angel-ML/angel (language: java)

Issue:
[Docs] Modify docs of PageRank
Modify docs of PageRank: add some description of new parameters



Discussion / hints:

Fix patch:
diff --git a/docs/algo/sona/pagerank_on_sona.md b/docs/algo/sona/pagerank_on_sona.md
index 96c492ef8..5e1f994ec 100644
--- a/docs/algo/sona/pagerank_on_sona.md
+++ b/docs/algo/sona/pagerank_on_sona.md
@@ -18,7 +18,12 @@
   - dataPartitionNum：输入数据的partition数，一般设为spark executor个数乘以executor core数的3-4倍
   - tol：停止更新条件，越小表示结果越准确，默认为0.01
   - resetProp：随机重置概率(alpha), 默认为0.15
-
+  - isWeight：边是否带权，默认为false
+  - version：网络切割方式（edge-cut：按边切割，vertex-cut：按点切割），默认为edge-cut
+  - batchSize：保存节点rank值时分批拉取结果，batchSize为批次的大小，默认为1000
+  - storageLevel: 存储级别（[可选值参考](https://spark.apache.org/docs/0.8.1/api/core/org/apache/spark/storage/StorageLevel$.html)），默认为MEMORY_ONLY
+  - useBalancePartition：是否使用均衡分区， 默认为否
+ 
 ### 资源参数
 
   - Angel PS个数和内存大小：ps.instance与ps.memory的乘积是ps总的配置内存。为了保证Angel不挂掉，需要配置模型大小两倍左右的内存。对于PageRank来说，模型大小的计算公式为： 节点数 * 3 * 4 Byte，据此可以估算不同规模的Graph输入下需要配置的ps内存大小
@@ -43,8 +48,8 @@ $SPARK_HOME/bin/spark-submit \
   --executor-cores 4 \
   --executor-memory 10g \
   --class com.tencent.angel.spark.examples.cluster.PageRankExample \
-  ../lib/spark-on-angel-examples-3.1.0.jar \
-  input:$input output:$output tol:0.01 :5 resetProp:0.15
+  ../lib/spark-on-angel-examples-3.2.0.jar \
+  input:$input output:$output tol:0.01 resetProp:0.15 version:edge-cut batchSize:1000 psPartitionNum:10 dataPartitionNum:10
 ```
 
 ### 常见问题
diff --git a/docs/algo/sona/pagerank_on_sona_en.md b/docs/algo/sona/pagerank_on_sona_en.md
index 7a104060b..1d2a2e7e5 100644
--- a/docs/algo/sona/pagerank_on_sona_en.md
+++ b/docs/algo/sona/pagerank_on_sona_en.md
@@ -18,6 +18,11 @@ We implemented large-scale PageRank calculation based on Spark On Angel, where p
   - dataPartitionNum：The number of input data partitions is generally set to 3-4 times the number of spark executors times the number of executor cores.
   - tol：the tolerance allowed at convergence (smaller => more accurate), the default is 0.01
   - resetProp：the random reset probability (alpha), the default is 0.15
+  - isWeight：whether the edge is weighted, the default is false
+  - version：edge network cutting method(edge-cut：cut by edge，vertex-cut：cut by vertex), the default is edge-cut
+  - batchSize：pull the results in batches when saving the node rank value, batchSize is the size of the batch, the default is 1000
+  - storageLevel: storage level([details](https://spark.apache.org/docs/0.8.1/api/core/org/apache/spark/storage/StorageLevel$.html)), the default is MEMORY_ONLY
+  - useBalancePartition：whether use balance partition, the default is false
 
 ### Resource parameters
 
@@ -42,8 +47,8 @@ $SPARK_HOME/bin/spark-submit \
   --executor-cores 4 \
   --executor-memory 10g \
   --class com.tencent.angel.spark.examples.cluster.PageRankExample \
-  ../lib/spark-on-angel-examples-3.1.0.jar \
-  input:$input output:$output tol:0.01 :5 resetProp:0.15
+  ../lib/spark-on-angel-examples-3.2.0.jar \
+  input:$input output:$output tol:0.01 resetProp:0.15 version:edge-cut batchSize:1000 psPartitionNum:10 dataPartitionNum:10
 ```
 
 ### FAQ


Test patch: