What a well structured question! On Sun, Apr 8, 2012 at 6:37 AM, Tom Ferguson <tomfergu...@gmail.com> wrote:
> Hello, > > I'm very new to Hadoop and I am trying to carry out of proof of concept for > processing some trading data. I am from a .net background, so I am trying > to prove whether it can be done primarily using C#, therefore I am looking > at the Hadoop Streaming job (from the Hadoop examples) to call in to some > C# executables. > > My problem is, I am not certain of the best way to structure my jobs to > process the data in the way I want. > > I have data stored in an RDBMS in the following format: > > ID TradeID Date Value > --------------------------------------------- > 1 1 2012-01-01 12.34 > 2 1 2012-01-02 12.56 > 3 1 2012-01-03 13.78 > 4 2 2012-01-04 18.94 > 5 2 2012-05-17 19.32 > 6 2 2012-05-18 19.63 > 7 3 2012-05-19 17.32 > What I want to do is take all the Dates & Values for a given TradeID into a > mathematical function that will spit out the same set of Dates but will > have recalculated all the Values. I hope that makes sense.. e.g. > > Date Value > --------------------------- > 2012-01-01 12.34 > 2012-01-02 12.56 > 2012-01-03 13.78 > will have the mathematical function applied and spit out > > Date Value > --------------------------- > 2012-01-01 28.74 > 2012-01-02 31.29 > 2012-01-03 29.93 > I am not exactly sure how to achieve this using Hadoop Streaming, but my > thoughts so far are... > > > 1. Us Sqoop to take the data out of the RDBMS and in to HDFS and split > by TradeID - will this guarantee that all the the data points for a given > TradeID will be processed by the same Map task?? > 2. Write a Map task as a C# executable that will stream data in in the > format (ID, TradeID, Date, Value) > 3. Gather all the data points for a given TradeID together into an array > (or other datastructure) > A Naive Way - The Mapper will need to emit key,value pairs where TradeID = key, and the entire record is the value. Hadoop will make sure that all key,value pairs with the same key land up in the same reducer. In Java for example, all records for the same TradeID would become available as an Iterable collection. The Reducer can apply the mathematical function that you're talking about. Another Way - If it is guaranteed that records with the same TradeID occur one after the other (and occur a fixed number of times, say 'k' times), then you can use a custom input format that makes available to the mapper 'k' records at a time, instead of 1. The mapper can then apply mathematical function. No reducer would be required in this case. > 4. Pass the array into the mathematical function > 5. Get the results back as another array > 6. Stream the results back out in the format (TradeID, Date, ResultValue) > > I will have around 500,000 Trade IDs, with up to 3,000 data points each, so > I am hoping that the data/processing will be distributed appropriately by > Hadoop. > > Now, this seams a little bit long winded, but is this the best way of doing > it, based on the constraints of having to use C# for writing my tasks? In > the example above I do not have a Reduce job at all. Is that right in my > scenario? > > Thanks for any help you can give and apologies if I am asking stupid > questions here! > > Kind Regards, > > Tom > Deepak Nettem MS CS SUNY Stony Brook
