Hi Phillip/Hao,
I was wondering if there is a simple working example out there that I can just
run and see it work. Then, I can customize it for our needs. Unfortunately,
this explanation still confuses me a little.
Here is a little about the environment we are working with. We have Cloudera's
CDH 4.4.0 installed, and it comes with HBase 0.94.6. We get data streamed in
using Flume-NG 1.4.0. All of this is managed using Cloudera Manager 4.7.2 to
setup and configure these services.
If you need any more information or are able to help, I would be glad to
accommodate.
Thanks,Ben
Date: Fri, 6 Dec 2013 18:07:08 -0700
From: philip.og...@oracle.com
To: user@spark.incubator.apache.org
Subject: Re: write data into HBase via spark
Hao,
Thank you for the detailed response! (even if delayed!)
I'm curious to know what version of hbase you added to your pom
file.
Thanks,
Philip
On 11/14/2013 10:38 AM, Hao REN wrote:
Hi, Philip.
Basically, we need PairRDDFunctions.saveAsHadoopDataset
to do the job, as HBase is not a fs, saveAsHadoopFile doesn't
work.
def saveAsHadoopDataset(conf: JobConf): Unit
this function takes a JobConf parameter which should be
configured. Essentially, you need to set output format and the
name of the output table.
// step 1: JobConf setup:
// Note: mapred package is used, instead of the mapreduce
package which contains new hadoop APIs.
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.client._
// ... some other settings
val conf = HBaseConfiguration.create()
// general hbase setting
conf.set("hbase.rootdir", "hdfs://" + nameNodeURL +
":" + hdfsPort + "/hbase")
conf.setBoolean("hbase.cluster.distributed", true)
conf.set("hbase.zookeeper.quorum", hostname)
conf.setInt("hbase.client.scanner.caching", 10000)
// ... some other settings
val jobConfig: JobConf = new JobConf(conf,
this.getClass)
// Note: TableOutputFormat is used as deprecated code,
because JobConf is an old hadoop API
jobConfig.setOutputFormat(classOf[TableOutputFormat])
jobConfig.set(TableOutputFormat.OUTPUT_TABLE,
outputTable)
// step 2: give your mapping:
// the last thing todo is mapping your local data schema to
the hbase one
// Say, our hbase schema is as below:
// row cf:col_1 cf:col_2
// And in spark, you have a RDD of triple, like (1, 2, 3),
(4, 5, 6), ...
// So you should map RDD[(int, int, int)] to
RDD[(ImmutableBytesWritable,
Put)], where Put carries the mapping.
// You can define a function used by RDD.map, for example:
def convert(triple: (Int, Int, Int)) = {
val p = new Put(Bytes.toBytes(triple._1))
p.add(Bytes.toBytes("cf"),
Bytes.toBytes("col_1"), Bytes.toBytes(triple._2))
p.add(Bytes.toBytes("cf"),
Bytes.toBytes("col_2"), Bytes.toBytes(triple._3))
(new ImmutableBytesWritable, p)
}
// Suppose you have a RDD[(Int, Int, Int)] called localData,
then writing data to hbase can be done by :
new
PairRDDFunctions(localData.map(convert)).saveAsHadoopDataset(jobConfig)
Voilà. That's all you need. Hopefully, this simple example
could help.
Hao.
2013/11/13 Philip Ogren <philip.og...@oracle.com>
Hao,
If you have worked out the code and turn it into an
example that you can share, then please do! This task is
in my queue of things to do so any helpful details that
you uncovered would be most appreciated.
Thanks,
Philip
On 11/13/2013 5:30 AM, Hao REN wrote:
Ok, I worked it out.
The following thread helps a lot.
http://mail-archives.apache.org/mod_mbox/incubator-spark-user/201310.mbox/%3C7B4868A9-B83E-4507-BB2A-2721FCE8E738%40gmail.com%3E
Hao
2013/11/12 Hao REN <julien19890...@gmail.com>
Could someone show me a simple
example about how to write data into HBase
via spark ?
I have checked HbaseTest example, it's
only for reading from HBase.
Thank you.
--
REN Hao
Data Engineer @ ClaraVista
Paris, France
Tel: +33 06 14 54 57
24
--
REN Hao
Data Engineer @ ClaraVista
Paris, France
Tel: +33 06
14 54 57 24
--
REN Hao
Data Engineer @ ClaraVista
Paris, France
Tel: +33 06 14 54 57 24
