If you don't want to manage hive table, It doesn't necessarily means you need to use the vanilla MapReduce. If your workflow is complex using Hive, it won't be that easy to maintain it if everything is implemented directly using MapReduce. I would recommend you to look at libraries such as Cascading (or Crunch or ..).
Of course, there is a leaning curve but these alternatives are 1) java API 2) less verbose than vanilla MapReduce 3) enabling you to reuse common patterns (such as map side aggregation -> pseudo combiner in Cascading) On Wed, Aug 1, 2012 at 7:36 PM, Yue Guan <pipeha...@gmail.com> wrote: > The story here is that we have a work flow based on hive queries. It > takes several stages to get to the final data. For each stage, we have a > hive table. And we try to write the whole work flow in mapreduce. Ideally, > it will remove all the intermediate process and take two rounds of > mapreduce to do the job. > > I just try the buffer in mapper approach, the number of map output record > matches with Hive. Thank you > > On 08/01/2012 11:40 AM, Bertrand Dechoux wrote: > > 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 > > > -- Bertrand Dechoux
