[SQL-DS-CACHE-201] Update guide for OAP 1.2.0 (#204)

Author: Hong

docs/Developer-Guide.md

@@ -36,7 +36,7 @@ To use optimized Plasma cache with OAP, you need following components:
 ```
 cd /tmp
 git clone https://github.com/oap-project/arrow.git
-cd arrow && git checkout arrow-4.0.0-oap-1.1.1
+cd arrow && git checkout v4.0.0-oap-1.2.0
 cd cpp
 mkdir release
 cd release

docs/User-Guide.md

@@ -263,7 +263,7 @@ Socket Configuration -> Intel UPI General Configuration -> Stale AtoS :  Disable
 
    For more information you can refer to [Quick Start Guide: Provision Intel® Optane™ DC Persistent Memory](https://software.intel.com/content/www/us/en/develop/articles/quick-start-guide-configure-intel-optane-dc-persistent-memory-on-linux.html)
 
-- SQL Data Source Cache uses Plasma as a node-level external cache service, the benefit of using external cache is data could be shared across process boundaries.  [Plasma](http://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/) is a high-performance shared-memory object store and a component of [Apache Arrow](https://github.com/apache/arrow). We have modified Plasma to support PMem, and make it open source on [oap-project-Arrow](https://github.com/oap-project/arrow/tree/arrow-4.0.0-oap-1.1.1) repo. If you have finished [OAP Installation Guide](OAP-Installation-Guide.md), Plasma will be automatically installed and then you just need copy `arrow-plasma-4.0.0.jar` to `$SPARK_HOME/jars`. For manual building and installation steps you can refer to [Plasma installation](./Developer-Guide.md#Plasma-installation).
+- SQL Data Source Cache uses Plasma as a node-level external cache service, the benefit of using external cache is data could be shared across process boundaries.  [Plasma](http://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/) is a high-performance shared-memory object store and a component of [Apache Arrow](https://github.com/apache/arrow). We have modified Plasma to support PMem, and make it open source on [oap-project-Arrow](https://github.com/oap-project/arrow/tree/arrow-4.0.0-oap-1.2) repo. If you have finished [OAP Installation Guide](OAP-Installation-Guide.md), Plasma will be automatically installed and then you just need copy `arrow-plasma-4.0.0.jar` to `$SPARK_HOME/jars`. For manual building and installation steps you can refer to [Plasma installation](./Developer-Guide.md#Plasma-installation).
 
 
 - Refer to configuration below to apply external cache strategy and start Plasma service on each node and start your workload.
@@ -280,11 +280,11 @@ spark.executor.instances          6
 spark.sql.extensions              org.apache.spark.sql.OapExtensions
 
 # absolute path of the jar on your working node, when in Yarn client mode
-spark.files                       $HOME/miniconda2/envs/oapenv/oap_jars/plasma-sql-ds-cache-<version>-with-spark-<version>.jar,$HOME/miniconda2/envs/oapenv/oap_jars/pmem-common-<version>-with-spark-<version>.jar
+spark.files                       $HOME/miniconda2/envs/oapenv/oap_jars/plasma-sql-ds-cache-<version>-with-spark-<version>.jar,$HOME/miniconda2/envs/oapenv/oap_jars/pmem-common-<version>-with-spark-<version>.jar,$HOME/miniconda2/envs/oapenv/oap_jars/arrow-plasma-4.0.0.jar
 # relative path to spark.files, just specify jar name in current dir, when in Yarn client mode
-spark.executor.extraClassPath     ./plasma-sql-ds-cache-<version>-with-spark-<version>.jar:./pmem-common-<version>-with-spark-<version>.jar
+spark.executor.extraClassPath     ./plasma-sql-ds-cache-<version>-with-spark-<version>.jar:./pmem-common-<version>-with-spark-<version>.jar:./arrow-plasma-4.0.0.jar
 # absolute path of the jar on your working node,when in Yarn client mode
-spark.driver.extraClassPath       $HOME/miniconda2/envs/oapenv/oap_jars/plasma-sql-ds-cache-<version>-with-spark-<version>.jar:$HOME/miniconda2/envs/oapenv/oap_jars/pmem-common-<version>-with-spark-<version>.jar
+spark.driver.extraClassPath       $HOME/miniconda2/envs/oapenv/oap_jars/plasma-sql-ds-cache-<version>-with-spark-<version>.jar:$HOME/miniconda2/envs/oapenv/oap_jars/pmem-common-<version>-with-spark-<version>.jar:$HOME/miniconda2/envs/oapenv/oap_jars/arrow-plasma-4.0.0.jar
 
 # for parquet file format, enable binary cache
 spark.sql.oap.parquet.binary.cache.enabled                   true
@@ -355,15 +355,15 @@ Run ```yarn app -destroy plasma-store-service```to destroy it.
 
 This section provides instructions and tools for running TPC-DS queries to evaluate the cache performance of various configurations. The TPC-DS suite has many queries and we select 9 I/O intensive queries to simplify performance evaluation.
 
-We created some tool scripts [oap-benchmark-tool.zip](https://github.com/oap-project/oap-tools/releases/download/v1.1.1-spark-3.1.1/oap-benchmark-tool.zip) to simplify running the workload. If you are already familiar with TPC-DS data generation and running a TPC-DS tool suite, skip our tool and use the TPC-DS tool suite directly.
+We created some tool scripts [oap-benchmark-tool.zip](https://github.com/oap-project/oap-tools/releases/download/v1.2.0/oap-benchmark-tool.zip) to simplify running the workload. If you are already familiar with TPC-DS data generation and running a TPC-DS tool suite, skip our tool and use the TPC-DS tool suite directly.
 
 ### Prerequisites
 
 - Python 2.7+ is required on the working node. 
 
 ### Prepare the Tool
 
-1. Download [oap-benchmark-tool.zip](https://github.com/oap-project/oap-tools/releases/download/v1.1.1-spark-3.1.1/oap-benchmark-tool.zip) and unzip to a folder (for example, `oap-benchmark-tool` folder) on your working node. 
+1. Download [oap-benchmark-tool.zip](https://github.com/oap-project/oap-tools/releases/download/v1.2.0/oap-benchmark-tool.zip) and unzip to a folder (for example, `oap-benchmark-tool` folder) on your working node. 
 2. Copy `oap-benchmark-tool/tools/tpcds-kits` to ***ALL*** worker nodes under the same folder (for example, `/home/oap/tpcds-kits`).
 
 ### Generate TPC-DS Data