HBase实操：Spark-Read-HBase-Snapshot-Demo 分享
前言：之前给大家分享了Spark通过接口直接读取HBase的一个小demo：HBase-Spark-Read-Demo，但如果在数据量非常大的情况下，Spark直接扫描HBase表必然会对HBase集群造成不小的压力。基于此，今天再给大家分享一下Spark通过Snapshot直接读取HBase HFile文件的方式。
首先我们先创建一个HBase表：test，并插入几条数据，如下：
hbase(main):003:0> scan 'test'
ROW COLUMN+CELL
r1 column=f:name, timestamp=1583318512414, value=zpb
r2 column=f:name, timestamp=1583318517079, value=lisi
r3 column=f:name, timestamp=1583318520839, value=wang 接着，我们创建该HBase表的快照，其在HDFS上路径如下：
hbase(main):005:0> snapshot 'test', 'test-snapshot'
0 row(s) in 0.3690 seconds
$ hdfs dfs -ls /apps/hbase/data/.hbase-snapshot
Found 1 items
drwxr-xr-x - hbase hdfs 0 2020-03-21 21:24 /apps/hbase/data/.hbase-snapshot/test-snapshot代码如下：
import org.apache.hadoop.fs.Path
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.hbase.client.Scan
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat, TableSnapshotInputFormat}
import org.apache.hadoop.hbase.protobuf.ProtobufUtil
import org.apache.hadoop.hbase.util.{Base64, Bytes}
import org.apache.spark.{SparkConf, SparkContext}
object SparkReadHBaseSnapshotDemo {
// 主函数
def main(args: Array[String]) {
// 设置spark访问入口
val conf = new SparkConf().setAppName("SparkReadHBaseSnapshotDemo")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.setMaster("local")//调试
val sc = new SparkContext(conf)
// 获取HbaseRDD
val job = Job.getInstance(getHbaseConf())
TableSnapshotInputFormat.setInput(job, "test-snapshot", new Path("/user/tmp"))
val hbaseRDD = sc.newAPIHadoopRDD(job.getConfiguration, classOf[TableSnapshotInputFormat],
classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
classOf[org.apache.hadoop.hbase.client.Result])
hbaseRDD.map(_._2).map(getRes(_)).count()
}
def getRes(result: org.apache.hadoop.hbase.client.Result): String = {
val rowkey = Bytes.toString(result.getRow())
val name = Bytes.toString(result.getValue("f".getBytes, "name".getBytes))
println(rowkey+"---"+name)
name
}
// 构造 Hbase 配置信息
def getHbaseConf(): Configuration = {
val conf: Configuration = HBaseConfiguration.create()
conf.set(TableInputFormat.SCAN, getScanStr())
conf
}
// 获取扫描器
def getScanStr(): String = {
val scan = new Scan()
// scan.set.... 各种过滤
val proto = ProtobufUtil.toScan(scan)
Base64.encodeBytes(proto.toByteArray())
}
}注：上述代码需将core-site.xml&hdfs-site.xml&hbase-site.xml文件放在资源目录resources下。否则，应在代码中进行配置，代码如下：
package com.xcar.etl
import org.apache.hadoop.fs.Path
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.hbase.client.Scan
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat, TableSnapshotInputFormat}
import org.apache.hadoop.hbase.protobuf.ProtobufUtil
import org.apache.hadoop.hbase.util.{Base64, Bytes}
import org.apache.spark.{SparkConf, SparkContext}
object SparkReadHBaseSnapshotDemo2 {
val HBASE_ZOOKEEPER_QUORUM = "xxxx.com.cn"
// 主函数
def main(args: Array[String]) {
// 设置spark访问入口
val conf = new SparkConf().setAppName("SparkReadHBaseSnapshotDemo2")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.setMaster("local")//调试
val sc = new SparkContext(conf)
// 获取HbaseRDD
val job = Job.getInstance(getHbaseConf())
TableSnapshotInputFormat.setInput(job, "test-snapshot", new Path("/user/tmp"))
val hbaseRDD = sc.newAPIHadoopRDD(job.getConfiguration, classOf[TableSnapshotInputFormat],
classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
classOf[org.apache.hadoop.hbase.client.Result])
hbaseRDD.map(_._2).map(getRes(_)).count()
}
def getRes(result: org.apache.hadoop.hbase.client.Result): String = {
val rowkey = Bytes.toString(result.getRow())
val name = Bytes.toString(result.getValue("f".getBytes, "name".getBytes))
println(rowkey+"---"+name)
name
}
// 构造 Hbase 配置信息
def getHbaseConf(): Configuration = {
val conf: Configuration = HBaseConfiguration.create()
conf.set("hbase.zookeeper.property.clientPort", "2181")
conf.set("zookeeper.znode.parent", "/hbase")
conf.set("hbase.zookeeper.quorum", HBASE_ZOOKEEPER_QUORUM)
conf.set("hbase.rootdir", "/apps/hbase")
// 设置查询的表名
conf.set(TableInputFormat.INPUT_TABLE, "test")
conf.set("fs.defaultFS","hdfs://xxxxxx:8020")
conf.set(TableInputFormat.SCAN, getScanStr())
conf
}
// 获取扫描器
def getScanStr(): String = {
val scan = new Scan()
// scan.set.... 各种过滤
val proto = ProtobufUtil.toScan(scan)
Base64.encodeBytes(proto.toByteArray())
}
}TableSnapshotInputFormat.setInput 方法参数解析：
public static void setInput(org.apache.hadoop.mapreduce.Job job,
String snapshotName,
org.apache.hadoop.fs.Path restoreDir)
throws IOException
参数解析:
job - the job to configure
snapshotName - the name of the snapshot to read from
restoreDir - a temporary directory to restore the snapshot into.
Current user should have write permissions to this directory, and this should not be a subdirectory of rootdir.
After the job is finished, restoreDir can be deleted.项目用到的 pom.xml 文件：
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.zpb.test</groupId>
<artifactId>spark-read-hbase-snapshot-demo</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>spark-read-hbase-snapshot-demo</name>
<url>http://maven.apache.org</url>
<repositories>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
</repositories>
<properties>
<cdh.hbase.version>1.2.0-cdh5.7.0</cdh.hbase.version>
<cdh.spark.version>1.6.0-cdh5.7.0</cdh.spark.version>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.62</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${cdh.spark.version}</version>
<!--<scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>${cdh.hbase.version}</version>
</dependency>
</dependencies>
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