HBase provides two ways to allow a Hbase client to connect HBase master. One is to use a instance of HBaseAdmin class. HBaseAdmin provides some methods for creating, modifying, and deleting tables and column families. Another way is to use an instance of HTable class. This class almost provides some methods to manipulate data like inserting, modifying, and deleting rows and cells.

Thus, in order to make a hbase table, we need to connect a HBase master by initializing a instance of HBaseAdmin like line 4. HBaseAdmin requires an instance of HBaseConfiguration. If necessary, you may set some configurations like line 2.

In order to describe HBase schema, we make an instances of HColumnDescriptor for each column family. In addition to column family names, HColumnDescriptor enables you to set various parameters, such as maxVersions, compression type, timeToLive, and bloomFilter. Then, we can create a HBase table by invoking createTable like line 10.

HBaseConfiguration conf = new HBaseConfiguration();
conf.set("hbase.master","localhost:60000");

HBaseAdmin hbase = new HBaseAdmin(conf);
HTableDescriptor desc = new HTableDescriptor("TEST");
HColumnDescriptor meta = new HColumnDescriptor("personal".getBytes());
HColumnDescriptor prefix = new HColumnDescriptor("account".getBytes());
desc.addFamily(meta);
desc.addFamily(prefix);
hbase.createTable(desc);

Finally, you can check your hbase table as the following commands.

c0d3h4ck@code:~/Development/hbase$ bin/hbase shell
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Version: 0.20.1, r822817, Wed Oct  7 11:55:42 PDT 2009
hbase(main):001:0> list
TEST

1 row(s) in 0.0940 seconds

• Use the Hadoop Distributed File System (HDFS) for storing large
  datasets, and run distributed computations over those datasets using
  MapReduce
• Become familiar with Hadoop’s data and I/O building blocks for compression, data integrity, serialization, and persistence
• Discover common pitfalls and advanced features for writing real-world MapReduce programs
• Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud
• Use Pig, a high-level query language for large-scale data processing
• Take advantage of HBase, Hadoop’s database for structured and semi-structured data
• Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems

그동안 소스도 분석해 보고 Hadoop 기반 어플리케이션도 짜보고 했지만 좀 더 체계적으로 알고 싶은 마음에 질러볼까합니다.
그런데 바빠서 볼 수 있을지 ~(~_~)~