I am not sure about Hive but if you look at Cascading they use a pseudo combiner instead of the standard (I mean Hadoop's) combiner. I guess Hive has a similar strategy.
The point is that when you use a compiler, the compiler does smart thing that you don't need to think about (like loop unwinding). The result is that your code is still readable but optimized and in most cases the compiler will do better than you. Even your naive implementation of the Mapper (without the Reducer and the configuration) is more complicated than the whole Hive query. Like Chuck said Hive is basically a MapReduce compiler. It is fun to look at how it works. But it is often best to let the compiler work for you instead of trying to beat it. For simple cases, like a 'select', Hive (or any other same-level alternative solutions) is helpful. And for complex cases, with multiple joins, you will want to have something like Hive too because with the vanilla MapReduce API it can become quite hard to grasp everything. Basically, two reasons : faster to express and cheaper to maintain. One reason not to use Hive is if your approach is more programmatic like if you want to do machine learning which will require highly specific workflow and user defined functions. It would be interesting to know your issue : are you trying to benchmark Hive (and you)? Or have you any other reasons? Bertrand On Wed, Aug 1, 2012 at 5:13 PM, Edward Capriolo <edlinuxg...@gmail.com>wrote: > As mentioned, if you avoid using new, by re-using objects and possibly > use buffer objects you may be able to match or beat the speed. But in > the general case the hive saves you time by allowing you not to worry > about low level details like this. > > On Wed, Aug 1, 2012 at 10:35 AM, Connell, Chuck > <chuck.conn...@nuance.com> wrote: > > This is actually not surprising. Hive is essentially a MapReduce > compiler. It is common for regular compilers (C, C#, Fortran) to emit > faster assembler code than you write yourself. Compilers know the tricks of > their target language. > > > > Chuck Connell > > Nuance R&D Data Team > > Burlington, MA > > > > > > -----Original Message----- > > From: Yue Guan [mailto:pipeha...@gmail.com] > > Sent: Wednesday, August 01, 2012 10:29 AM > > To: user@hive.apache.org > > Subject: mapper is slower than hive' mapper > > > > Hi, there > > > > I'm writing mapreduce to replace some hive query and I find that my > mapper is slow than hive's mapper. The Hive query is like: > > > > select sum(column1) from table group by column2, column3; > > > > My mapreduce program likes this: > > > > public static class HiveTableMapper extends Mapper<BytesWritable, > Text, MyKey, DoubleWritable> { > > > > public void map(BytesWritable key, Text value, Context context) > throws IOException, InterruptedException { > > String[] sLine = StringUtils.split(value.toString(), > > StringUtils.ESCAPE_CHAR, HIVE_FIELD_DELIMITER_CHAR); > > context.write(new MyKey(Integer.parseInt(sLine[0]), > sLine[1]), new DoubleWritable(Double.parseDouble(sLine[2]))); > > } > > > > } > > > > I assume hive is doing something similar. Is there any trick in hive to > speed this thing up? Thank you! > > > > Best, > > > -- Bertrand Dechoux
