[NSE-1161] Format sql config string key

arrow-data-source/parquet/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSQLConf.scala

@@ -24,7 +24,7 @@ object ParquetSQLConf {
   // arrow-datasource-parquet.jar file is supposed to be placed into Spark's lib folder. Which
   // means it's user's intention to use the replaced ParquetDataSource.
   val OVERWRITE_PARQUET_DATASOURCE_READ =
-  SQLConf.buildConf("spark.sql.arrow.overwrite.parquet.read")
+  SQLConf.buildConf("spark.oap.sql.arrow.overwrite.parquet.read")
     .doc("Overwrite Parquet datasource v1 with reader of Arrow datasource.")
     .booleanConf
     .createWithDefault(true)

arrow-data-source/standard/src/main/scala/com/intel/oap/spark/sql/execution/datasources/v2/arrow/ArrowSQLConf.scala

@@ -20,20 +20,20 @@ package com.intel.oap.spark.sql.execution.datasources.v2.arrow
 import org.apache.spark.sql.internal.SQLConf
 
 object ArrowSQLConf {
-  val ARROW_FILTER_PUSHDOWN_ENABLED = SQLConf.buildConf("spark.sql.arrow.filterPushdown")
+  val ARROW_FILTER_PUSHDOWN_ENABLED = SQLConf.buildConf("spark.oap.sql.arrow.filterPushdown")
     .doc("Enables Arrow filter push-down optimization when set to true.")
     .booleanConf
     .createWithDefault(true)
 
-  val FILES_DYNAMIC_MERGE_ENABLED = SQLConf.buildConf("spark.sql.files.dynamicMergeEnabled")
+  val FILES_DYNAMIC_MERGE_ENABLED = SQLConf.buildConf("spark.oap.sql.files.dynamicMergeEnabled")
     .doc("Whether to merge file partition dynamically. If true, It will use the total size, " +
       "file count and expectPartitionNum to dynamic merge filePartition. This is better to set " +
       "true if there are many small files in the read path. This configuration is effective " +
       "only when using file-based sources such as Parquet, JSON and ORC.")
     .booleanConf
     .createWithDefault(false)
 
-  val FILES_EXPECTED_PARTITION_NUM = SQLConf.buildConf("spark.sql.files.expectedPartitionNum")
+  val FILES_EXPECTED_PARTITION_NUM = SQLConf.buildConf("spark.oap.sql.files.expectedPartitionNum")
     .doc("The expected number of File partitions. It will automatically merge file splits to " +
       "provide the best concurrency when the file partitions after split exceed the " +
       "expected num and the size of file partition is less than maxSplitSize. If not set, " +
@@ -44,7 +44,7 @@ object ArrowSQLConf {
     .checkValue(v => v > 0, "The expected partition number must be a positive integer.")
     .createOptional
 
-  val FILES_MAX_NUM_IN_PARTITION = SQLConf.buildConf("spark.sql.files.maxNumInPartition")
+  val FILES_MAX_NUM_IN_PARTITION = SQLConf.buildConf("spark.oap.sql.files.maxNumInPartition")
     .doc("The max number of files in one filePartition. If set, it will limit the max file num " +
       "in FilePartition while merging files. This can avoid too many little io in one task. " +
       "This configuration is effective only when using file-based sources such as Parquet, " +

docs/Configuration.md

@@ -34,7 +34,7 @@ You can add these configuration into spark-defaults.conf to enable or disable th
 | spark.oap.sql.columnar.preferColumnar | Enable or Disable Columnar Operators, default is false.<br /> This parameter could impact the performance in different case. In some cases, to set false can get some performance boost. | false |
 | spark.oap.sql.columnar.joinOptimizationLevel | Fallback to row operators if there are several continous joins | 18 |
 | spark.sql.execution.arrow.maxRecordsPerBatch | Set up the Max Records per Batch | 10000 |
-| spark.sql.execution.sort.spillThreshold | Set up the Max sort in memory threshold in bytes, default is disabled | -1 |
+| spark.oap.sql.execution.sort.spillThreshold | Set up the Max sort in memory threshold in bytes, default is disabled | -1 |
 | spark.oap.sql.columnar.wholestagecodegen.breakdownTime | Enable or Disable metrics in Columnar WholeStageCodeGen | false |
 | spark.oap.sql.columnar.tmp_dir | Set up a folder to store the codegen files, default is disabled | "" |
 | spark.oap.sql.columnar.shuffle.customizedCompression.codec | Set up the codec to be used for Columnar Shuffle, default is lz4. The other option is fastpfor which can bring better perf on compressing fixed-size based contents like int| lz4 |

native-sql-engine/core/src/main/scala/com/intel/oap/GazellePluginConfig.scala

@@ -191,7 +191,7 @@ class GazellePluginConfig(conf: SQLConf) extends Logging {
     conf.getConfString("spark.sql.execution.arrow.maxRecordsPerBatch", "10000").toInt
 
   val sortSpillThreshold: Long =
-    conf.getConfString("spark.sql.execution.sort.spillThreshold", "-1").toLong
+    conf.getConfString("spark.oap.sql.execution.sort.spillThreshold", "-1").toLong
 
   // enable or disable metrics in columnar wholestagecodegen operator
   val enableMetricsTime: Boolean =
@@ -204,7 +204,7 @@ class GazellePluginConfig(conf: SQLConf) extends Logging {
     conf.getConfString("spark.oap.sql.columnar.tmp_dir", null)
 
   @deprecated val broadcastCacheTimeout: Int =
-    conf.getConfString("spark.sql.columnar.sort.broadcast.cache.timeout", "-1").toInt
+    conf.getConfString("spark.oap.sql.columnar.sort.broadcast.cache.timeout", "-1").toInt
 
   // Whether to spill the partition buffers when buffers are full.
   // If false, the partition buffers will be cached in memory first,