needyit 2020-06-04
大家可能都知道很熟悉Spark的两种常见的数据读取方式(存放到RDD中):(1)、调用parallelize函数直接从集合中获取数据,并存入RDD中;Java版本如下:
JavaRDD<Integer> myRDD = sc.parallelize(Arrays.asList( 1 , 2 , 3 )); |
Scala版本如下:
val myRDD= sc.parallelize(List( 1 , 2 , 3 )) |
这种方式很简单,很容易就可以将一个集合中的数据变成RDD的初始化值;更常见的是(2)、从文本中读取数据到RDD中,这个文本可以是纯文本文件、可以是sequence文件;可以存放在本地(file://)、可以存放在HDFS(hdfs://)上,还可以存放在S3上。其实对文件来说,Spark支持Hadoop所支持的所有文件类型和文件存放位置。Java版如下:
///////////////////////////////////////////////////////////////////// User: 过往记忆 Date: 14 - 6 - 29 Time: 23 : 59 bolg: 本文地址:/archives/ 1051 过往记忆博客,专注于hadoop、hive、spark、shark、flume的技术博客,大量的干货 过往记忆博客微信公共帐号:iteblog_hadoop ///////////////////////////////////////////////////////////////////// import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; SparkConf conf = new SparkConf().setAppName( "Simple Application" ); JavaSparkContext sc = new JavaSparkContext(conf); sc.addFile( "wyp.data" ); JavaRDD<String> lines = sc.textFile(SparkFiles.get( "wyp.data" )); |
Scala版本如下:
import org.apache.spark.SparkContext import org.apache.spark.SparkConf val conf = new SparkConf().setAppName( "Simple Application" ) val sc = new SparkContext(conf) sc.addFile( "spam.data" ) val inFile = sc.textFile(SparkFiles.get( "spam.data" )) |
在实际情况下,我们需要的数据可能不是简单的存放在HDFS文本中,我们需要的数据可能就存放在Hbase中,那么我们如何用Spark来读取Hbase中的数据呢?本文的所有测试是基于Hadoop 2.2.0、Hbase 0.98.2、Spark 0.9.1,不同版本可能代码的编写有点不同。本文只是简单地用Spark来读取Hbase中的数据,如果需要对Hbase进行更强的操作,本文可能不能帮你。话不多说,Spark操作Hbase的Java版本代码如下:
package com.iteblog.spark; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hbase.HBaseConfiguration; import org.apache.hadoop.hbase.client.Result; import org.apache.hadoop.hbase.client.Scan; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.mapreduce.TableInputFormat; import org.apache.hadoop.hbase.protobuf.ProtobufUtil; import org.apache.hadoop.hbase.protobuf.generated.ClientProtos; import org.apache.hadoop.hbase.util.Base64; import org.apache.hadoop.hbase.util.Bytes; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFunction; import scala.Serializable; import scala.Tuple2; import java.io.IOException; import java.util.List; /** * User: iteblog * Date: 14-6-27 * Time: 下午5:18 *blog: http://www.iteblog.com * * Usage: bin/spark-submit --master yarn-cluster --class com.iteblog.spark.SparkFromHbase * --jars /home/q/hbase/hbase-0.96.0-hadoop2/lib/htrace-core-2.01.jar, * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-common-0.96.0-hadoop2.jar, * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-client-0.96.0-hadoop2.jar, * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-protocol-0.96.0-hadoop2.jar, * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-server-0.96.0-hadoop2.jar * ./spark_2.10-1.0.jar */ public class SparkFromHbase implements Serializable { /** * copy from org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil * * @param scan * @return * @throws IOException */ String convertScanToString(Scan scan) throws IOException { ClientProtos.Scan proto = ProtobufUtil.toScan(scan); return Base64.encodeBytes(proto.toByteArray()); } public void start() { SparkConf sparkConf = new SparkConf(); JavaSparkContext sc = new JavaSparkContext(sparkConf); Configuration conf = HBaseConfiguration.create(); Scan scan = new Scan(); //scan.setStartRow(Bytes.toBytes("195861-1035177490")); //scan.setStopRow(Bytes.toBytes("195861-1072173147")); scan.addFamily(Bytes.toBytes("cf")); scan.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("col_1")); try { String tableName = "wyp"; conf.set(TableInputFormat.INPUT_TABLE, tableName); conf.set(TableInputFormat.SCAN, convertScanToString(scan)); JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD = sc.newAPIHadoopRDD(conf, TableInputFormat.class, ImmutableBytesWritable.class, Result.class); JavaPairRDD<String, Integer> levels = hBaseRDD.mapToPair( new PairFunction<Tuple2<ImmutableBytesWritable, Result>, String, Integer>() { @Override public Tuple2<String, Integer> call(Tuple2<ImmutableBytesWritable, Result> immutableBytesWritableResultTuple2) throws Exception { byte[] o = immutableBytesWritableResultTuple2._2().getValue(Bytes.toBytes("cf"), Bytes.toBytes("col_1")); if (o != null) { return new Tuple2<String, Integer>(new String(o), 1); } return null; } }); JavaPairRDD<String, Integer> counts = levels.reduceByKey( new Function2<Integer, Integer, Integer>() { @Override public Integer call(Integer i1, Integer i2) { return i1 + i2; } }); List<Tuple2<String, Integer>> output = counts.collect(); for (Tuple2 tuple : output) { System.out.println(tuple._1() + ": " + tuple._2()); } sc.stop(); } catch (Exception e) { e.printStackTrace(); } } public static void main(String[] args) throws InterruptedException { new SparkFromHbase().start(); System.exit(0); } }
这样本段代码段是从Hbase表名为flight_wap_order_log的数据库中读取cf列簇上的airName一列的数据,这样我们就可以对myRDD进行相应的操作:
System.out.println(myRDD.count()); |
本段代码需要在pom.xml文件加入以下依赖:
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2. 10 </artifactId> <version> 0.9 . 1 </version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase</artifactId> <version> 0.98 . 2 -hadoop2</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-client</artifactId> <version> 0.98 . 2 -hadoop2</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-common</artifactId> <version> 0.98 . 2 -hadoop2</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-server</artifactId> <version> 0.98 . 2 -hadoop2</version> </dependency> |
Scala版如下:
import org.apache.spark._ import org.apache.spark.rdd.NewHadoopRDD import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor} import org.apache.hadoop.hbase.client.HBaseAdmin import org.apache.hadoop.hbase.mapreduce.TableInputFormat ///////////////////////////////////////////////////////////////////// User: 过往记忆 Date: 14 - 6 - 29 Time: 23 : 59 bolg: 本文地址:/archives/ 1051 过往记忆博客,专注于hadoop、hive、spark、shark、flume的技术博客,大量的干货 过往记忆博客微信公共帐号:iteblog_hadoop ///////////////////////////////////////////////////////////////////// object HBaseTest { def main(args: Array[String]) { val sc = new SparkContext(args( 0 ), "HBaseTest" , System.getenv( "SPARK_HOME" ), SparkContext.jarOfClass( this .getClass)) val conf = HBaseConfiguration.create() conf.set(TableInputFormat.INPUT_TABLE, args( 1 )) val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result]) hBaseRDD.count() System.exit( 0 ) } } |
我们需要在加入如下依赖:
libraryDependencies ++= Seq( "org.apache.spark" % "spark-core_2.10" % "0.9.1" , "org.apache.hbase" % "hbase" % "0.98.2-hadoop2" , "org.apache.hbase" % "hbase-client" % "0.98.2-hadoop2" , "org.apache.hbase" % "hbase-common" % "0.98.2-hadoop2" , "org.apache.hbase" % "hbase-server" % "0.98.2-hadoop2" ) |
在测试的时候,需要配置好Hbase、Hadoop环境,否则程序会出现问题,特别是让程序找到Hbase-site.xml配置文件。
package com.iteblog.spark; | |
import org.apache.hadoop.conf.Configuration; | |
import org.apache.hadoop.hbase.HBaseConfiguration; | |
import org.apache.hadoop.hbase.client.Result; | |
import org.apache.hadoop.hbase.client.Scan; | |
import org.apache.hadoop.hbase.io.ImmutableBytesWritable; | |
import org.apache.hadoop.hbase.mapreduce.TableInputFormat; | |
import org.apache.hadoop.hbase.protobuf.ProtobufUtil; | |
import org.apache.hadoop.hbase.protobuf.generated.ClientProtos; | |
import org.apache.hadoop.hbase.util.Base64; | |
import org.apache.hadoop.hbase.util.Bytes; | |
import org.apache.spark.SparkConf; | |
import org.apache.spark.api.java.JavaPairRDD; | |
import org.apache.spark.api.java.JavaSparkContext; | |
import org.apache.spark.api.java.function.Function2; | |
import org.apache.spark.api.java.function.PairFunction; | |
import scala.Serializable; | |
import scala.Tuple2; | |
import java.io.IOException; | |
import java.util.List; | |
/** | |
* User: iteblog | |
* Date: 14-6-27 | |
* Time: 下午5:18 | |
*blog: http://www.iteblog.com | |
* | |
* Usage: bin/spark-submit --master yarn-cluster --class com.iteblog.spark.SparkFromHbase | |
* --jars /home/q/hbase/hbase-0.96.0-hadoop2/lib/htrace-core-2.01.jar, | |
* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-common-0.96.0-hadoop2.jar, | |
* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-client-0.96.0-hadoop2.jar, | |
* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-protocol-0.96.0-hadoop2.jar, | |
* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-server-0.96.0-hadoop2.jar | |
* ./spark_2.10-1.0.jar | |
*/ | |
public class SparkFromHbase implements Serializable { | |
/** | |
* copy from org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil | |
* | |
* @param scan | |
* @return | |
* @throws IOException | |
*/ | |
String convertScanToString(Scan scan) throws IOException { | |
ClientProtos.Scan proto = ProtobufUtil.toScan(scan); | |
return Base64.encodeBytes(proto.toByteArray()); | |
} | |
public void start() { | |
SparkConf sparkConf = new SparkConf(); | |
JavaSparkContext sc = new JavaSparkContext(sparkConf); | |
Configuration conf = HBaseConfiguration.create(); | |
Scan scan = new Scan(); | |
//scan.setStartRow(Bytes.toBytes("195861-1035177490")); | |
//scan.setStopRow(Bytes.toBytes("195861-1072173147")); | |
scan.addFamily(Bytes.toBytes("cf")); | |
scan.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("col_1")); | |
try { | |
String tableName = "wyp"; | |
conf.set(TableInputFormat.INPUT_TABLE, tableName); | |
conf.set(TableInputFormat.SCAN, convertScanToString(scan)); | |
JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD = sc.newAPIHadoopRDD(conf, | |
TableInputFormat.class, ImmutableBytesWritable.class, | |
Result.class); | |
JavaPairRDD<String, Integer> levels = hBaseRDD.mapToPair( | |
new PairFunction<Tuple2<ImmutableBytesWritable, Result>, String, Integer>() { | |
@Override | |
public Tuple2<String, Integer> call(Tuple2<ImmutableBytesWritable, Result> immutableBytesWritableResultTuple2) throws Exception { | |
byte[] o = immutableBytesWritableResultTuple2._2().getValue(Bytes.toBytes("cf"), Bytes.toBytes("col_1")); | |
if (o != null) { | |
return new Tuple2<String, Integer>(new String(o), 1); | |
} | |
return null; | |
} | |
}); | |
JavaPairRDD<String, Integer> counts = levels.reduceByKey( | |
new Function2<Integer, Integer, Integer>() { | |
@Override | |
public Integer call(Integer i1, Integer i2) { | |
return i1 + i2; | |
} | |
}); | |
List<Tuple2<String, Integer>> output = counts.collect(); | |
for (Tuple2 tuple : output) { | |
System.out.println(tuple._1() + ": " + tuple._2()); | |
} | |
sc.stop(); | |
} catch (Exception e) { | |
e.printStackTrace(); | |
} | |
} | |
public static void main(String[] args) throws InterruptedException { | |
new SparkFromHbase().start(); | |
System.exit(0); | |
} | |
} |