spark读写hbase性能对比
一、spark写入hbase
hbase client以put方式封装数据,并支持逐条或批量插入。spark中内置saveAsHadoopDataset和saveAsNewAPIHadoopDataset两种方式写入hbase。为此,将同样的数据插入其中对比性能。
依赖如下:
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>2.3.1</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-client --> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-client</artifactId> <version>1.4.6</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-common --> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-common</artifactId> <version>1.4.6</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-server --> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-server</artifactId> <version>1.4.6</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-protocol --> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-protocol</artifactId> <version>1.4.6</version> </dependency> <!-- https://mvnrepository.com/artifact/commons-cli/commons-cli --> <dependency> <groupId>commons-cli</groupId> <artifactId>commons-cli</artifactId> <version>1.4</version> </dependency>
1. put逐条插入
1.1 hbase客户端建表
create 'keyword1',{NAME=>'info',BLOCKSIZE=>'16384',BLOCKCACHE=>'false'},{NUMREGIONS=>10,SPLITALGO=>'HexStringSplit'}
1.2 code
val start_time1 = new Date().getTime keyword.foreachPartition(records =>{ HBaseUtils1x.init() records.foreach(f => { val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) HBaseUtils1x.insertData(tableName1, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }) HBaseUtils1x.closeConnection() }) var end_time1 =new Date().getTime println("HBase逐条插入运行时间为:" + (end_time1 - start_time1))
2.put批量插入
2.1 建表
create 'keyword2',{NAME=>'info',BLOCKSIZE=>'16384',BLOCKCACHE=>'false'},{NUMREGIONS=>10,SPLITALGO=>'HexStringSplit'}
2.2 代码
val start_time2 = new Date().getTime keyword.foreachPartition(records =>{ HBaseUtils1x.init() val puts = ArrayBuffer[Put]() records.foreach(f => { val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) try{ puts.append(HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }catch{ case e:Throwable => println(f) } }) import collection.JavaConverters._ HBaseUtils1x.addDataBatchEx(tableName2, puts.asJava) HBaseUtils1x.closeConnection() }) val end_time2 = new Date().getTime println("HBase批量插入运行时间为:" + (end_time2 - start_time2))
3. saveAsHadoopDataset写入
使用旧的Hadoop API将RDD输出到任何Hadoop支持的存储系统,为该存储系统使用Hadoop JobConf对象。JobConf设置一个OutputFormat和任何需要输出的路径,就像为Hadoop MapReduce作业配置那样。
3.1 建表
create 'keyword3',{NAME=>'info',BLOCKSIZE=>'16384',BLOCKCACHE=>'false'},{NUMREGIONS=>10,SPLITALGO=>'HexStringSplit'}
3.2 代码
val start_time3 = new Date().getTime keyword.rdd.map(f =>{ val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) (new ImmutableBytesWritable, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }).saveAsHadoopDataset(HBaseUtils1x.getJobConf(tableName3)) val end_time3 = new Date().getTime println("saveAsHadoopDataset方式写入运行时间为:" + (end_time3 - start_time3))
4. saveAsNewAPIHadoopDataset写入
使用新的Hadoop API将RDD输出到任何Hadoop支持存储系统,为该存储系统使用Hadoop Configuration对象.Conf设置一个OutputFormat和任何需要的输出路径,就像为Hadoop MapReduce作业配置那样。
4.1 建表
create 'keyword4',{NAME=>'info',BLOCKSIZE=>'16384',BLOCKCACHE=>'false'},{NUMREGIONS=>10,SPLITALGO=>'HexStringSplit'}
4.2 code
val start_time4 = new Date().getTime keyword.rdd.map(f =>{ val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) (new ImmutableBytesWritable, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }).saveAsNewAPIHadoopDataset(HBaseUtils1x.getNewJobConf(tableName4,spark.sparkContext)) val end_time4 = new Date().getTime println("saveAsNewAPIHadoopDataset方式写入运行时间为:" + (end_time4 - start_time4))
5. 性能对比
可以看出,saveAsHadoopDataset和saveAsNewAPIHadoopDataset方式要优于put逐条插入和批量插入。
二、spark读取hbase
newAPIHadoopRDD API可以将hbase表转化为RDD,具体使用如下:
val start_time1 = new Date().getTime val hbaseRdd = spark.sparkContext.newAPIHadoopRDD(HBaseUtils1x.getNewConf(tableName1), classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result]) println(hbaseRdd.count()) hbaseRdd.foreach{ case(_,result) => { // 获取行键 val rowKey = Bytes.toString(result.getRow) val keyword = Bytes.toString(result.getValue(cf.getBytes(), "keyword".getBytes())) val keyword_catalog_pv_rate = Bytes.toDouble(result.getValue(cf.getBytes(), "keyword_catalog_pv_rate".getBytes())) println(rowKey + "," + keyword + "," + keyword_catalog_pv_rate) } }
三、完整代码
package com.sparkStudy.utils import java.util.Date import org.apache.hadoop.hbase.client.{Put, Result} import org.apache.hadoop.hbase.io.ImmutableBytesWritable import org.apache.hadoop.hbase.mapreduce.TableInputFormat import org.apache.hadoop.hbase.util.{Bytes, MD5Hash} import org.apache.spark.sql.SparkSession import scala.collection.mutable.ArrayBuffer /**
* @Author: JZ.lee
* @Description: TODO
* @Date: 18-8-28 下午4:28
* @Modified By:
*/ object SparkRWHBase { def main(args: Array[String]): Unit = { val spark = SparkSession.builder() .appName("SparkRWHBase") .master("local[2]") .config("spark.some.config.option", "some-value") .getOrCreate() val keyword = spark.read .format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat") .option("header",false) .option("delimiter",",") .load("file:/opt/data/keyword_catalog_day.csv") val tableName1 = "keyword1" val tableName2 = "keyword2" val tableName3 = "keyword3" val tableName4 = "keyword4" val cf = "info" val columns = Array("keyword", "app_id", "catalog_name", "keyword_catalog_pv", "keyword_catalog_pv_rate") val start_time1 = new Date().getTime keyword.foreachPartition(records =>{ HBaseUtils1x.init() records.foreach(f => { val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) HBaseUtils1x.insertData(tableName1, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }) HBaseUtils1x.closeConnection() }) var end_time1 =new Date().getTime println("HBase逐条插入运行时间为:" + (end_time1 - start_time1)) val start_time2 = new Date().getTime keyword.foreachPartition(records =>{ HBaseUtils1x.init() val puts = ArrayBuffer[Put]() records.foreach(f => { val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) try{ puts.append(HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }catch{ case e:Throwable => println(f) } }) import collection.JavaConverters._ HBaseUtils1x.addDataBatchEx(tableName2, puts.asJava) HBaseUtils1x.closeConnection() }) val end_time2 = new Date().getTime println("HBase批量插入运行时间为:" + (end_time2 - start_time2)) val start_time3 = new Date().getTime keyword.rdd.map(f =>{ val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) (new ImmutableBytesWritable, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }).saveAsHadoopDataset(HBaseUtils1x.getJobConf(tableName3)) val end_time3 = new Date().getTime println("saveAsHadoopDataset方式写入运行时间为:" + (end_time3 - start_time3)) // val start_time4 = new Date().getTime keyword.rdd.map(f =>{ val keyword = f.getString(0) val app_id = f.getString(1) val catalog_name = f.getString(2) val keyword_catalog_pv = f.getString(3) val keyword_catalog_pv_rate = f.getString(4) val rowKey = MD5Hash.getMD5AsHex(Bytes.toBytes(keyword+app_id)).substring(0,8) val cols = Array(keyword,app_id,catalog_name,keyword_catalog_pv,keyword_catalog_pv_rate) (new ImmutableBytesWritable, HBaseUtils1x.getPutAction(rowKey, cf, columns, cols)) }).saveAsNewAPIHadoopDataset(HBaseUtils1x.getNewJobConf(tableName4,spark.sparkContext)) val end_time4 = new Date().getTime println("saveAsNewAPIHadoopDataset方式写入运行时间为:" + (end_time4 - start_time4)) val hbaseRdd = spark.sparkContext.newAPIHadoopRDD(HBaseUtils1x.getNewConf(tableName1), classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result]) println(hbaseRdd.count()) hbaseRdd.foreach{ case(_,result) => { // 获取行键 val rowKey = Bytes.toString(result.getRow) val keyword = Bytes.toString(result.getValue(cf.getBytes(), "keyword".getBytes())) val keyword_catalog_pv_rate = Bytes.toDouble(result.getValue(cf.getBytes(), "keyword_catalog_pv_rate".getBytes())) println(rowKey + "," + keyword + "," + keyword_catalog_pv_rate) } } } } package com.sparkStudy.utils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.hbase.client.BufferedMutator.ExceptionListener import org.apache.hadoop.hbase.client._ import org.apache.hadoop.hbase.io.ImmutableBytesWritable import org.apache.hadoop.hbase.protobuf.ProtobufUtil import org.apache.hadoop.hbase.util.{Base64, Bytes} import org.apache.hadoop.hbase.{HBaseConfiguration, HColumnDescriptor, HTableDescriptor, TableName} import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.Job import org.apache.spark.SparkContext import org.slf4j.LoggerFactory /**
* @Author: JZ.Lee
* @Description:HBase1x增删改查
* @Date: Created at 上午11:02 18-8-14
* @Modified By:
*/ object HBaseUtils1x { private val LOGGER = LoggerFactory.getLogger(this.getClass) private var connection:Connection = null private var conf:Configuration = null def init() = { conf = HBaseConfiguration.create() conf.set("hbase.zookeeper.quorum", "lee") connection = ConnectionFactory.createConnection(conf) } def getJobConf(tableName:String) = { val conf = HBaseConfiguration.create() val jobConf = new JobConf(conf) jobConf.set("hbase.zookeeper.quorum", "lee") jobConf.set("hbase.zookeeper.property.clientPort", "2181") jobConf.set(org.apache.hadoop.hbase.mapred.TableOutputFormat.OUTPUT_TABLE,tableName) jobConf.setOutputFormat(classOf[org.apache.hadoop.hbase.mapred.TableOutputFormat]) jobConf } def getNewConf(tableName:String) = { conf = HBaseConfiguration.create() conf.set("hbase.zookeeper.quorum", "lee") conf.set("hbase.zookeeper.property.clientPort", "2181") conf.set(org.apache.hadoop.hbase.mapreduce.TableInputFormat.INPUT_TABLE,tableName) val scan = new Scan() conf.set(org.apache.hadoop.hbase.mapreduce.TableInputFormat.SCAN,Base64.encodeBytes(ProtobufUtil.toScan(scan).toByteArray)) conf } def getNewJobConf(tableName:String) = { val conf = HBaseConfiguration.create()
conf.set("hbase.zookeeper.quorum", Constants.ZOOKEEPER_SERVER_NODE)
conf.set("hbase.zookeeper.property.clientPort", "2181")
conf.set("hbase.defaults.for.version.skip", "true")
conf.set(org.apache.hadoop.hbase.mapreduce.TableOutputFormat.OUTPUT_TABLE, tableName)
conf.setClass("mapreduce.job.outputformat.class", classOf[org.apache.hadoop.hbase.mapreduce.TableOutputFormat[String]],
classOf[org.apache.hadoop.mapreduce.OutputFormat[String, Mutation]])
new JobConf(conf)
} def closeConnection(): Unit = { connection.close() } def getGetAction(rowKey: String):Get = { val getAction = new Get(Bytes.toBytes(rowKey)); getAction.setCacheBlocks(false); getAction } def getPutAction(rowKey: String, familyName:String, column: Array[String], value: Array[String]):Put = { val put: Put = new Put(Bytes.toBytes(rowKey)); for (i <- 0 until(column.length)) { put.add(Bytes.toBytes(familyName), Bytes.toBytes(column(i)), Bytes.toBytes(value(i))); } put } def insertData(tableName:String, put: Put) = { val name = TableName.valueOf(tableName) val table = connection.getTable(name) table.put(put) } def addDataBatchEx(tableName:String, puts:java.util.List[Put]): Unit = { val name = TableName.valueOf(tableName) val table = connection.getTable(name) val listener = new ExceptionListener { override def onException (e: RetriesExhaustedWithDetailsException, bufferedMutator: BufferedMutator): Unit = { for(i <-0 until e.getNumExceptions){ LOGGER.info("写入put失败:" + e.getRow(i)) } } } val params = new BufferedMutatorParams(name) .listener(listener) .writeBufferSize(4*1024*1024) try{ val mutator = connection.getBufferedMutator(params) mutator.mutate(puts) mutator.close() }catch { case e:Throwable => e.printStackTrace() } } }
https://blog.csdn.net/baymax_007/article/details/82191188
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