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测试条件
以下是我的PC信息
依赖：
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.3.1</version>
</dependency>
</dependencies>1. 第一种方法
每次写进一条，调用API
/**
* Puts some data in the table.
*
* @param put The data to put.
* @throws IOException if a remote or network exception occurs.
* @since 0.20.0
*/
void put(Put put) throws IOException我的代码
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
object Word {
def main(args: Array[String]): Unit = {
val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
rdd.foreachPartition(x => {
val hbaseConf = HBaseConfiguration.create()
hbaseConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
hbaseConf.set("hbase.zookeeper.property.clientPort", "2181")
hbaseConf.set("hbase.defaults.for.version.skip", "true")
val hbaseConn = ConnectionFactory.createConnection(hbaseConf)
val table = hbaseConn.getTable(TableName.valueOf("word"))
x.foreach(value => {
var put = new Put(Bytes.toBytes(value.toString))
put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(value.toString))
table.put(put)
})
})
}
}
第一条的时间戳：1497973306787
最后一条的时间戳：1497973505273
时间戳之差1497973505273-1497973306787=198486
2.第二种方法
批量写入Hbase，使用的API：
/**
* {@inheritDoc}
* @throws IOException
*/
@Override
public void put(final List<Put> puts) throws IOException {
getBufferedMutator().mutate(puts);
if (autoFlush) {
flushCommits();
}
}我的代码：
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, HTable, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
object Word {
def main(args: Array[String]): Unit = {
val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
rdd.map(value => {
var put = new Put(Bytes.toBytes(value.toString))
put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(value.toString))
put
}).foreachPartition(iterator => {
var jobConf = new JobConf(HBaseConfiguration.create())
jobConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
jobConf.set("zookeeper.znode.parent", "/hbase")
jobConf.setOutputFormat(classOf[TableOutputFormat])
val table = new HTable(jobConf, TableName.valueOf("word"))
import scala.collection.JavaConversions._
table.put(seqAsJavaList(iterator.toSeq))
})
}
}
第一条数据的时间戳是
0 column=f1:c1, timestamp=1498013677545, value=0
1 column=f1:c1, timestamp=1498013677545, value=1
10 column=f1:c1, timestamp=1498013677545, value=10
100 column=f1:c1, timestamp=1498013677545, value=100
1000 column=f1:c1, timestamp=1498013677545, value=1000 第9999条数据写进Hbase的时间戳是：
108993 column=f1:c1, timestamp=1498013680244, value=108993
108994 column=f1:c1, timestamp=1498013680244, value=108994
108995 column=f1:c1, timestamp=1498013680244, value=108995
9999 row(s) in 1.2730 seconds时间戳之差t=1498013680244-1498013677545=2699
3.第三种方法
将写进Hbase转换为Mapreduce任务
我的代码：
import org.apache.hadoop.hbase.{HBaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory, Put}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
object Word {
def main(args: Array[String]): Unit = {
val sc = new SparkContext(new SparkConf().setMaster("local[2]").setAppName("hbase"))
val conf = HBaseConfiguration.create()
var jobConf = new JobConf(conf)
jobConf.set("hbase.zookeeper.quorum", "172.17.11.85,172.17.11.86,172.17.11.87")
jobConf.set("zookeeper.znode.parent", "/hbase")
jobConf.set(TableOutputFormat.OUTPUT_TABLE, "word")
jobConf.setOutputFormat(classOf[TableOutputFormat])
val rdd = sc.makeRDD(Array(1)).flatMap(_ => 0 to 1000000)
rdd.map(x => {
var put = new Put(Bytes.toBytes(x.toString))
put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("c1"), Bytes.toBytes(x.toString))
(new ImmutableBytesWritable, put)
}).saveAsHadoopDataset(jobConf)
}
}第一条的时间戳：
0 column=f1:c1, timestamp=1498014877635, value=0 最后一条的时间戳
108993 column=f1:c1, timestamp=1498014879526, value=108993
108994 column=f1:c1, timestamp=1498014879526, value=108994
108995 column=f1:c1, timestamp=1498014879526, value=108995 时间戳之差t=1498014879526-1498014877635=1891
4.总结
通过以上对比可以看出，在其他条件相同的情况下
第三种方法(1498014879526-1498014877635=1891)>第二种方法（1498013680244-1498013677545=2699）>第一种方法（1497973505273-1497973306787=198486）