


Structuring MapReduce Jobs
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 20120101 12.34 2 1 20120102 12.56 3 1 20120103 13.78 4 2 20120104 18.94 5 2 20120517 19.32 6 2 20120518 19.63 7 3 20120519 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  20120101 12.34 20120102 12.56 20120103 13.78 will have the mathematical function applied and spit out
Date Value  20120101 28.74 20120102 31.29 20120103 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) 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

Structuring MapReduce Jobs
Resending my query below... it didn't seem to post first time.
Thanks,
Tom On Apr 8, 2012 11:37 AM, "Tom Ferguson" <[EMAIL PROTECTED]> 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 20120101 12.34 > 2 1 20120102 12.56 > 3 1 20120103 13.78 > 4 2 20120104 18.94 > 5 2 20120517 19.32 > 6 2 20120518 19.63 > 7 3 20120519 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 >  > 20120101 12.34 > 20120102 12.56 > 20120103 13.78 > will have the mathematical function applied and spit out > > Date Value >  > 20120101 28.74 > 20120102 31.29 > 20120103 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) > 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 >

Re: Structuring MapReduce Jobs
Hi : Well phrased question .... I think you will need to read up on reducers, and then you will see the light.
1) in your mapper, emit (date,tradeValue) objects.
2) Then hadoop will send the following to the reducers.
date1,tradeValues[] date2,tradeValues[] ... 3) Then, in your reducer, you will apply the function to the whole set of trade values.
4) Note that the mappers will split on files  they are not gaurantees that any particular data will be sent to the mappers. If you want the any data to be "grouped", you will need to write a mapper that performs this grouping on an arbitrarily large data set, and then your group specific statistics will have to be done at the reducer stage.
Think of it this way : The mapper does the grouping of inputs for reducers, and the reducers then do the group specific logic. For example, in word count, the mappers emit individual words  the reducers recieve a large group of numbers for each individual word, and sum them to emit a total count. In your case, the words are like the raw bank records  and the function you are applying to records from a certain "date" is like the sum function in the word count reducer.
On Mon, Apr 9, 2012 at 11:45 AM, Tom Ferguson <[EMAIL PROTECTED]> wrote:
> Resending my query below... it didn't seem to post first time. > > Thanks, > > Tom > On Apr 8, 2012 11:37 AM, "Tom Ferguson" <[EMAIL PROTECTED]> 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 20120101 12.34 > > 2 1 20120102 12.56 > > 3 1 20120103 13.78 > > 4 2 20120104 18.94 > > 5 2 20120517 19.32 > > 6 2 20120518 19.63 > > 7 3 20120519 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 > >  > > 20120101 12.34 > > 20120102 12.56 > > 20120103 13.78 > > will have the mathematical function applied and spit out > > > > Date Value > >  > > 20120101 28.74 > > 20120102 31.29 > > 20120103 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) > > 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!
Jay Vyas MMSB/UCHC

Re: Structuring MapReduce Jobs
What a well structured question!
On Sun, Apr 8, 2012 at 6:37 AM, Tom Ferguson <[EMAIL PROTECTED]> 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 20120101 12.34 > 2 1 20120102 12.56 > 3 1 20120103 13.78 > 4 2 20120104 18.94 > 5 2 20120517 19.32 > 6 2 20120518 19.63 > 7 3 20120519 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 >  > 20120101 12.34 > 20120102 12.56 > 20120103 13.78 > will have the mathematical function applied and spit out > > Date Value >  > 20120101 28.74 > 20120102 31.29 > 20120103 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

