-Re: Efficient way to read a large number of files in S3 and upload their content to HBase
This is a question I see coming up a lot. Put differently: what characteristics make it useful to use HBase on top of HDFS, as opposed to just flat files in HDFS directly? "Quantity" isn't really an answer, b/c HDFS does fine with quantity (better, even).
The basic answers are that HBase is good if:
a) You want to be able to read random small bits of data in the middle of (large) HDFS files with low latency (i.e. without loading the whole thing from disk)
b) You want to be able to modify (insert) random small bits of data in the middle of (immutable, sorted) HDFS files without writing the whole thing out again each time.
If all you want is a way to quickly store a lot of data, it's hard to beat writing to flat files (only /dev/null/ is faster, but it doesn't support sharding). :) But if you want to then be able to do either (a) or (b) above, that's where you start looking at HBase. I assume in your case, you need sub-second access to single records (or ranges of records) anywhere in the set?
On May 24, 2012, at 1:53 PM, Marcos Ortiz wrote:
> On 05/24/2012 04:47 PM, Amandeep Khurana wrote:
>> Thanks for that description. I'm not entirely sure why you want to use
>> HBase here. You've got logs coming that you want to process in batch
>> to do calculations on. This can be done by running MR jobs on the flat
>> files itself. You could use Java MR, Hive or Pig to accomplish this.
>> Why do you want HBase here?
> Tha main reason to use HBase is for the quantity of rows involved in the
> process. It could provide a efficient and "quick" way to store all this.
> Hive can be an option too.
> I will discuss all this again with the dev team.
> Thanks a lot for your answers.
>> On Thursday, May 24, 2012 at 12:52 PM, Marcos Ortiz wrote:
>>> On 05/24/2012 03:21 PM, Amandeep Khurana wrote:
>>>> Can you elaborate on your use case a little bit? What is the nature of
>>>> data in S3 and why you want to use HBase? Why do you want to combine
>>>> HFiles and upload back to S3? It'll help us answer your questions
>>> Ok, let me explain more.
>>> We are working on a ads optimization platform on top of Hadoop and HBase.
>>> Another team of my organization create a type of log file per click
>>> by user
>>> and store this file in S3. I discussed with them that a better approach
>>> is to storage this
>>> "workflow" log in HBase, instead S3, because in this way, we can quit
>>> the another step
>>> to read from S3 the content of the file, build the HFile and upload it
>>> to HBase.
>>> The content of the file in S3 is the basic information for the operation:
>>> - Source URL
>>> - User Id
>>> - User agent of the user
>>> - Campaign id
>>> and more fields.
>>> So, we want this to then create MapReduce jobs on top of HBase to some
>>> calculations and reports
>>> for this data.
>>> We are valuating HBase because our current solution is on top of
>>> PostgreSQL, but the main issue is when you
>>> launch a campaign on the platform, the INSERTs and UPDATEs to PostgreSQL
>>> in a short time, could rise from 1 to
>>> 100 clicks per second. We did some preliminary tests and in two days,
>>> the table where we store the "workflow"
>>> log grow exponentially to 350, 000 tuples, so, it could be a problem.
>>> For that reason, we want to migrate this to HBase.
>>> But I think that the approach to generate a file in S3 and then upload
>>> to HBase is not the best way to do this; because, you can always
>>> create the workflow log for every user, build a Put for it and upload it
>>> to HBase, and to avoid the locks, I´m valuating to use the asynchronous
>>> API released
>>> by StumbleUpon. 
>>> What do you think about this?
>>>  https://github.com/stumbleupon/asynchbase
>>>> On May 24, 2012, at 12:19 PM, Marcos Ortiz<[EMAIL PROTECTED]
>>>> <mailto:[EMAIL PROTECTED]>> wrote:
>>>>> Thanks a lot for your answer, Amandeep.