Right now I implement row count over ResultScanner like this

for (Result rs = scanner.next(); rs != null; rs = scanner.next()) {
    number++;
}

If data reaching millions time computing is large.I want to compute in real time that i don't want to use Mapreduce

How to quickly count number of rows.

If you're using a scanner, in your scanner try to have it return the least number of qualifiers as possible. In fact, the qualifier(s) that you do return should be the smallest (in byte-size) as you have available. This will speed up your scan tremendously.

Unfortuneately this will only scale so far (millions-billions?). To take it further, you can do this in real time but you will first need to run a mapreduce job to count all rows.

Store the Mapreduce output in a cell in HBase. Every time you add a row, increment the counter by 1. Every time you delete a row, decrement the counter.

When you need to access the number of rows in real time, you read that field in HBase.

There is no fast way to count the rows otherwise in a way that scales. You can only count so fast.

$ hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename>

Usage: RowCounter [options] 
    <tablename> [          
        --starttime=[start] 
        --endtime=[end] 
        [--range=[startKey],[endKey]] 
        [<column1> <column2>...]
    ]​

 Htable.incrementColumnValue(Bytes.toBytes("count"), Bytes.toBytes("details"), Bytes.toBytes("count"), 1);​

You can use the count method in hbase to count the number of rows. But yes, counting rows of a large table can be slow.count 'tablename' [interval]

Return value is the number of rows.

This operation may take a LONG time (Run ‘$HADOOP_HOME/bin/hadoop jar hbase.jar rowcount’ to run a counting mapreduce job). Current count is shown every 1000 rows by default. Count interval may be optionally specified. Scan caching is enabled on count scans by default. Default cache size is 10 rows. If your rows are small in size, you may want to increase this parameter.

Examples:

hbase> count 't1'

hbase> count 't1', INTERVAL => 100000

hbase> count 't1', CACHE => 1000

hbase> count 't1', INTERVAL => 10, CACHE => 1000

The same commands also can be run on a table reference. Suppose you had a reference to table 't1', the corresponding commands would be:

hbase> t.count

hbase> t.count INTERVAL => 100000

hbase> t.count CACHE => 1000

hbase> t.count INTERVAL => 10, CACHE => 1000

To count the Hbase table record count on a proper YARN cluster you have to set the map reduce job queue name as well:

hbase org.apache.hadoop.hbase.mapreduce.RowCounter -Dmapreduce.job.queuename= < Your Q Name which you have SUBMIT access>
 < TABLE_NAME>