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deleteTable(tableName);//注[1] } LOG.info("Drop table successfully."); } catch (IOException e) { LOG.error("Drop table failed
提供Spark的各种功能，如连接Spark集群，创建RDD，累积量和广播量等。它的作用相当于一个容器。 SparkConf：Spark应用配置类，如设置应用名称，执行模式，executor内存等。 JavaRDD：用于在java应用中定义JavaRDD的类，功能类似于scala中的RDD(Resilient
提供Spark的各种功能，如连接Spark集群，创建RDD，累积量和广播量等。它的作用相当于一个容器。 SparkConf：Spark应用配置类，如设置应用名称，执行模式，executor内存等。 JavaRDD：用于在java应用中定义JavaRDD的类，功能类似于scala中的RDD(Resilient
com/huaweicloud/huaweicloud-sdk-go-v3/services/mrs/v2" "github.com/huaweicloud/huaweicloud-sdk-go-v3/services/mrs/v2/model" region "github.com
提供Spark的各种功能，如连接Spark集群，创建RDD，累积量和广播量等。它的作用相当于一个容器。 SparkConf：Spark应用配置类，如设置应用名称，执行模式，executor内存等。 JavaRDD：用于在java应用中定义JavaRDD的类，功能类似于scala中的RDD(Resilient
request. table.delete(delete); LOG.info("Delete table successfully."); } catch (IOException e) { LOG.error("Delete table
LOG.info("result is : " + strBuffer.toString()); LOG.info("success to read."); } finally { // make sure the streams are closed
LOG.info("result is : " + strBuffer.toString()); LOG.info("success to read."); } finally { // make sure the streams are closed
LOG.info("result is : " + strBuffer.toString()); LOG.info("success to read."); } finally { // make sure the streams are closed
request. table.delete(delete); LOG.info("Delete table successfully."); } catch (IOException e) { LOG.error("Delete table
request. table.delete(delete); LOG.info("Delete table successfully."); } catch (IOException e) { LOG.error("Delete table
admin.deleteTable(tableName); } LOG.info("Drop table successfully."); } catch (IOException e) { LOG.error("Drop table
[TBLPROPERTIES ('column.encode.columns'='col_name1,col_name2'| 'column.encode.indices'='col_id1,col_id2','column.encode.classname'='encode_classname')]...;
request. table.delete(delete); LOG.info("Delete table successfully."); } catch (IOException e) { LOG.error("Delete table
LOG.info("result is : " + strBuffer.toString()); LOG.info("success to read."); } finally { // make sure the streams are closed
[TBLPROPERTIES ('column.encode.columns'='col_name1,col_name2'| 'column.encode.indices'='col_id1,col_id2','column.encode.classname'='encode_classname')]...;
case scan: CarbonDataSourceScan if scan.rdd.isInstanceOf[CarbonScanRDD[InternalRow]] => scan.rdd case scan: RowDataSourceScanExec if scan.rdd.i
case scan: CarbonDataSourceScan if scan.rdd.isInstanceOf[CarbonScanRDD[InternalRow]] => scan.rdd case scan: RowDataSourceScanExec if scan.rdd.i
code 2” 问题现象 执行语句select count(*) from XXX;时客户端报错： Error:Error while processing statement :FAILED:Execution Error,return code 2 from ... 这个报错return
含的文件数量很多时，串行读取Split信息变得缓慢，影响性能。故对此做了优化，当表包含的文件大于一定阈值（即spark.sql.sources.parallelSplitDiscovery.threshold参数值）时，会生成一个Job，利用Executor的并行能力去读取，从而提升执行效率。
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